231 results about "External database" patented technology

The invention belongs to but is not limited to the technical field of artificial intelligence, and particularly relates to a controllable content large model generation method and system based on multi-modal thinking chain reasoning, and the method comprises the steps: S1, improving the capability of multi-agent planning task dependence and execution sequence through employing a thinking chain method, enabling a complex task to be subdivided into a plurality of subtasks through the method, and enabling the subtasks to be subdivided into a plurality of subtasks; a resource transmission process between the sub-tasks is precisely planned and is executed in a chain mode; s2, complex tools are combined into sub-agents with different modal characteristics, so that the number of selectable sub-tasks during task planning is reduced, and the planning capability is improved; and S3, storing scene information of a previous dialogue by means of an external database, and when a new request is encountered, improving correlation and accuracy of multi-agent response by quoting historical interaction information and historical multi-modal resources which have context similarity with the current request, and realizing a more intelligent reaction mechanism.

The invention discloses a text retrieval enhancement generation method and device, a medium and equipment, and relates to the technical field of retrieval enhancement generation. Comprising the following steps: establishing a database through vectorized corpora, and screening and labeling key information to generate a golden section vector library; extracting an adversarial sample from an external database by using a training problem, and fusing the adversarial sample with related corpora in a golden section vector library to obtain a training set; and adding a noise classification layer to improve the language large model, and training the improved language large model by using the fusion training set. When a user question is processed, relevant corpora are recalled, noise is recognized through the classification layer, context representation is generated through the attention fusion layer, and the output layer dynamically adjusts a decoding strategy and generates an answer. According to the method, a large language model added with a noise classification task is trained through the fusion training set, so that the model can identify and distinguish different types of noise, the adaptability of the model to different types of noise is enhanced, the model is helped to learn how to distinguish related and unrelated information, and thus the method is more robust when an actual problem is processed.

The invention relates to a multi-data-source data processing method and system, and relates to the technical field of data processing. During data collection, data sources are determined according to business targets, internal and external databases are integrated, quality is evaluated, and connection is established. The data processing includes extraction, cleaning, conversion, fusion and storage. The cleaning adopts an algorithm to optimize a missing value to approach a real value; the conversion meets business requirements; and fusing to form a unified data set. According to data verification, multi-aspect characteristics are verified by setting a difference range and utilizing optimized data. The system has a powerful monitoring optimization function, and optimized data are used for real-time monitoring adjustment, early warning and decision support. According to the method, the traditional problems are effectively solved, the data processing efficiency, quality and reliability are improved, complex business requirements are met, accurate data positioning, stable connection, high-quality processing and reliable verification can be achieved, and powerful guarantee is provided for cross-data-source data analysis and decision making.

A materialized view created from one or more shared producer tables in separate producer clusters may be stored in a consumer cluster. The materialized view May undergo faster a refresh using an incremental refresh by obtaining snapshots of the producer table and incrementally refreshing based on deltas obtained by the snapshot. The materialized view may undergo various checks to determine whether the materialized view in the consumer resource is refreshable or unrefreshable based on schema compatibility. The materialized view may undergo further checks to determine whether the materialized view in the consumer resource is available or unavailable for incremental refresh based on detection of schema / data rearrangement operations on the producer table.

The present invention discloses an AI-enabled automated performance monitoring method and system for efficiently monitoring and evaluating the performance of one or more users in various tasks. The method involves receiving user feedback, processing it with an AI module to extract validation points, validating these points against internal and external databases, and generating refined feedback. The system comprises a user interface, internal and external databases, an AI module, and a second internal database for continuous improvement.

The invention discloses an intelligent task matching method based on multi-modal data and an RAG technology, and relates to the technical field of intelligent management, and the method comprises the steps: collecting multi-modal data, building modal data feature representation, and carrying out feature fusion based on a cross-modal collaborative attention mechanism; reading an intelligent task, performing data evaluation of a fusion result, establishing an RAG retrieval requirement, executing retrieval matching of an external database, and establishing a generation database; and constructing and optimizing a multi-learning framework for executing the intelligent task, and executing task processing of the corresponding intelligent task according to the converged multi-learning framework. The technical problems that in the prior art, a traditional single-modal processing method cannot meet the multi-modal data requirement in a complex scene, and the task processing adaptability and accuracy are low are solved, and the multi-modal data fusion, RAG retrieval enhancement and cross-task cooperation mechanism are achieved. And the adaptability and accuracy of intelligent task matching are improved.

The invention relates to the technical field of big data, and discloses a massive small file reading optimization method, system and device and a medium, and the method comprises the following steps: scanning small files in a distributed file system by using a Spark calculation engine to obtain metadata information of the small files; based on the metadata information, small files are classified, and the small files with the same type and data relevance are classified into one group; the resource states of cluster nodes are monitored in real time, the resource states comprise the processor utilization rate, the memory occupancy rate and the I / O load, the load score of each node is calculated, and small file task quotas are dynamically allocated; executing small file merging operation in parallel by utilizing a Spark memory calculation engine to generate a merged file containing a plurality of original small files; creating and maintaining index information in an external database, and recording the position and attribute of each original small file in the combined file; and providing a read-write interface through the special service layer, and positioning and accessing specific small file contents in the combined file.

The invention is suitable for the technical field of search enhancement generation, and provides a multi-intelligent-agent search enhancement generation system and a question answering method, equipment and product thereof. According to the question and answer method of the multi-agent search enhancement generation system provided by the embodiment of the invention, multiple agents such as the routing decision agent and the tool use agent are introduced, so that information can be obtained from an external database, a reasoning process can be autonomously planned based on domain knowledge, a search strategy is dynamically optimized, and the search efficiency is improved. And a specific analysis tool of a specific industry is flexibly called. Different from a static'retrieval-reading-generation 'mode of a retrieval enhancement generation system in the prior art, the multi-intelligent-body retrieval enhancement generation system adopts a multi-round interaction and intelligent optimization mechanism, and dynamically evaluates a retrieval result, adjusts a query mode and calls a professional tool in an answer generation process so as to ensure high-quality intelligent question and answer service.

The invention provides an image processing method and device, electronic equipment and a vehicle, and the method comprises the steps: collecting an image of an environment where the current vehicle is located, and obtaining a real environment image; identifying the types of environmental elements in the real environment image to obtain at least one type label; respectively acquiring a scene image corresponding to each type label from an external database to obtain a plurality of scene element images; and constructing and obtaining a three-dimensional panoramic image simulating the surrounding environment of the current vehicle based on the plurality of scene element images. According to the image processing method and device, the electronic equipment and the vehicle disclosed by the invention, the three-dimensional panoramic image simulating the surrounding environment of the current vehicle can be constructed, a driver is prevented from parking and getting off to observe the environment, and the driving safety and the driving experience are effectively improved.

The invention relates to a medical question and answer method and system based on a retrieval enhanced language model and a medium, and the method comprises the steps: S1, obtaining a query text X of a user about a medical question, and carrying out the semantic re-editing and expansion of the query text X through medical knowledge and a language model, and obtaining an edited text Xr and an expanded text Xe of the query text X; s2, paragraphs related to the edited text Xr and the extended text Xe are obtained from an external database, and a medical paragraph set P is obtained; s3, filtering partial paragraphs in the medical paragraph set P according to a preset screening standard to obtain a refined medical knowledge paragraph set P '; and S4, constructing a reply text based on the medical knowledge paragraph set P ', and outputting the reply text to the user. Through the steps, more accurate, practical and refined reply can be provided for the user, so that the satisfaction degree and credibility of the user on questions and answers are improved.

The invention relates to the field of data processing, in particular to a risk control rejection user data processing system. The system comprises a rejected user pool module used for adding a first user into a rejected user pool to obtain first rejected user information after a loan application of the first user is rejected; the newly-added external data matching module is used for carrying out matching analysis on the first rejected user information and each piece of external data in the external database to obtain a matching analysis result; the newly-added external data analysis module is used for responding to the matching analysis result that newly-added external data exists; performing data analysis on the newly added external data to obtain a first user tag generation result; and the user tag result storage and solidification module is used for storing and solidifying the first user tag generation result. The system generates a behavior tag for the rejected user by using repayment behavior information contained in the newly added external data after rejection.

Systems and methods for improved data curation and integration are disclosed. An example system includes one or more processors; and a memory storing instructions that cause the system to: receive an initial query, query an external database to obtain a set of keywords, topics, and other queries pertinent to the initial query, and embed articles into a database. The system may also append the keywords, topics, and other queries to the initial query to obtain an external data query and embedded articles to the initial query to obtain an internal data query; and generate (i) an external output by applying a large language model (LLM) to the external data query and (ii) an internal output by applying the LLM to the internal data query. The system may also combine the internal output with the external output to obtain a combined output and cause the combined output to be displayed.

An example computer system includes a transactional database configured to store multiple data sources each corresponding to one of multiple applications, a standby database configured to store multiple rule schemas and multiple data schemas, and processor hardware configured to execute instructions to monitor for a change to stored data in one of the multiple data sources, via a change data capture process, replicate data from the transactional database to the standby database in response to an update indicated by the change data capture process, identify encrypted data in at least one data object of an updated data table of one of the data schemas of the standby database, extract data from the at least one data object to store decrypted data, wherein the standby database is an external database for the data extraction engine, and reload the decrypted data to one of the data schemas of the standby database.

A system for evaluating aspects of a residential plumbing system may (1) receive, via a user device, a user input indicative of an address for the residential property; (2) communicate the address to an external database, the external database containing a plurality of residential records associated with the address in a geographic location; (3) receive, via the plurality of residential records, a plurality of plumbing system aspects and a plurality of residential property aspects; (4) predict an estimated remaining lifespan of the plumbing system based upon the plurality of plumbing system aspects and the plurality of residential property aspects; and / or (5) generate a notification, the notification comprising the estimated remaining lifespan of the plumbing system and one or more recommended actions.

A system for executing a multi-tenant application includes at least one processor and at least one memory storing program instructions. The multi-tenant application generates one or more page size recommendations and one or more sequential request count recommendations for one or more calls to an external database. The multi-tenant application performs a first call to the external database using a page size which is based on a first page size recommendation, where the page size specifies a number of records to retrieve from the external database. The multi-tenant application also performs, in a sequential manner by the multi-tenant application, the first call and a number of subsequent calls to the external database, where the number of subsequent calls is based on a first sequential request count recommendation. Related methods and computer program products are also provided.

The invention provides a vehicle-mounted chip industry chain monitoring system and method based on an NLP technology, and the method comprises the steps: carrying out the deep mining of a vehicle-mounted chip industry knowledge base through the NLP technology, and extracting the high-frequency features related to a vehicle-mounted chip to form a local professional feature library; acquiring industrial chain information of the vehicle-mounted chip according to the local professional feature library to form an external database; semantic extraction based on the NLP technology is carried out on data in the external database; constructing a multi-dimensional feature model based on the extracted semantic information by adopting a deep learning algorithm; performing information classification and hierarchical labeling on the specialized feature library of the specimen place by using the multi-dimensional feature model; creating a recommendation system by adopting a KNN algorithm on the basis of the multi-dimensional feature model subjected to information classification and hierarchical labeling; and performing visual display on the classified and graded data output by the recommendation system. According to the invention, the monitoring efficiency and accuracy of the industrial chain of the vehicle-mounted chip are improved, and accurate information classification, dynamic updating, visual early warning and auxiliary decision making are realized.

The invention discloses a knowledge indexing and retrieval method for an AUTOSAR hierarchical architecture, and the method comprises the steps: taking a hierarchical structure of an AUTOSAR CP knowledge system as a trunk, carrying out the vectorization of an input document, constructing an entity-relation hierarchical tree, obtaining a knowledge graph indexing structure, carrying out the query matching through the similarity during retrieval, and carrying out the retrieval. Sorting retrieval output entities and relationships, constructing a context, and outputting a structured answer; comprising the following steps: step 1, indexing: intelligently partitioning external database content input by a user and performing vectorization storage, operating general entity extraction and / or AUTOSAR special extraction and combining results, constructing a tree structure, performing hierarchical clustering to form a knowledge graph, and generating a community report; and 2, retrieval: receiving user query, finding related entities through vector similarity search, aggregating contexts in combination with tree and graph dual structures, and starting universal cue words and / or AUTOSAR special cue words to generate structured answers. The invention further discloses a system for implementing the method, and the system has wide application value.

The invention provides an industrial chain analysis method, system and device, a storage medium and a program product, and relates to the technical field of data analysis. The method comprises the steps that when it is detected that to-be-queried information is input into an industrial chain analysis model, intention recognition is conducted on the to-be-queried information, an intention recognition result is obtained, the intention recognition result at least comprises the name of a preset industrial chain, and the industrial chain analysis model is a large language model; the external database is a graph database constructed based on industrial chain data in a vector form; the intention recognition result is decomposed, a sub-query task set is obtained, and each sub-query task corresponds to the name of one industry chain in the intention recognition result; based on the target sub-query task, an analysis result of the target sub-query task is determined, and the target sub-query task is any one sub-query task; and determining an industrial chain analysis result corresponding to the to-be-queried information based on the analysis results of all the target sub-query tasks. According to the invention, the analysis accuracy can be improved.

The invention discloses a scientific choice question automatic answering method based on a large language model, a medium and equipment, and the method comprises the steps: carrying out the retrieval enhancement based on an external database, and obtaining a document set D related to a question; secondly, abstracting and preprocessing each document in the document set D to generate a preprocessed document set D '; and splicing the questions, the answer options and the preprocessed document information to obtain an input sequence S. And extracting answer information based on a Flash Attention mechanism, and voting based on a multi-thinking chain to obtain b answer options as answers of scientific choice questions. The method has higher answering accuracy and reliability, the experimental result on a Kaggle scientific subject selection question data set shows that the score of the method reaches 0.909, the method is remarkably superior to other baseline models, and the answering accuracy is remarkably improved.

The invention provides a whole-process intelligent supervision method and system for network drug sales, and the method comprises the steps: carrying out the encryption of network drug sales information through a hash function, and storing the encrypted network drug sales information in a block chain; the method comprises the following steps: extracting and integrating drug sales and context data of a block chain and an external database of the block chain, performing time sequence prediction by using a neural network combining LSTM and MLP to obtain a trained neural network model, and obtaining a predicted price, a predicted sales volume and a normal threshold of a predicted inventory of network sales drugs through the neural network model; obtaining an abnormal detection value of a data point in the block chain, and calculating a risk probability of the data point in the block chain based on the abnormal detection value; and deploying an intelligent contract, and automatically triggering an abnormal mark when the risk probability exceeds a second preset threshold value. Through the method and the corresponding system, real-time monitoring and warning of abnormal transactions or risk events can be ensured, and potential threats can be responded quickly.

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving an input text data retrieved from a transcription associated with a previously recorded audio data file between a user of a plurality of users and an agent associated with a call center; identifying personal information associated with the user of the plurality of users from the input text data by inputting the input text data into a trained machine learning model; determining at least one key term within the personal information associated with the user of the plurality of users; automatically determining a confidence positivity score associated with the at least one key term; automatically extracting a plurality of tuples from the input text data; storing the plurality of tuples in an external database; and automatically generating a call script for conducting a subsequent call with the user.

The invention provides a parameterization programming method based on a hydraulic support structural part welding program, which comprises the following steps of: 1) compiling a standard series workpiece welding program template according to process parameter information of a standard series workpiece; 2) determining the number of required numerical registers according to the design specifications of the standard series of workpieces so as to match the number of variable parameters of the standard series of workpieces; (3) according to the placing position standard of the workpiece on the positioner and the layout of the robot welding workstation, a function relation between a robot world coordinate system and the characteristic points of the positioner is constructed, and assignment operation logic of a position register and a numerical value register is defined; 4) taking the changed process parameter information as position register data for updating; 5, the welding robot calls the information in the position register, and updating of the welding gun operation program parameters is achieved.The method has the advantages that hardware equipment input and external database support are not depended on, and parameterized programming is completed through internal hardware of the welding robot.

The invention relates to the technical field of information query, in particular to a recruitment and application personnel information query system and method based on big data, and the system comprises a data acquisition module which is used for extracting resume data according to an application resume document and outputting a structured resume field; the data verification module is associated with an external database, verifies authenticity according to the resume field, and outputs a verification result; when the system is used, fuzzy detection, exaggerated word recognition, semantic similarity and abnormal probability distribution jointly participate in judgment, risks are stably recognized in different contexts, background cross validation and multi-dimensional fusion are carried out based on a big data model, system robustness can be improved, and man-made subjective judgment errors can be reduced; potential false information such as exaggerated packages and fuzzy descriptions is effectively recognized, the labor cost of'resumes are attractive but not consistent in ability 'is reduced, the system automatically completes preliminary screening credibility scoring, repeated consulting by workers is not needed, and the recruitment efficiency is improved.

The invention discloses a private domain-oriented large language model personalized question and answer method and related products, and relates to the technical field of large language model question and answer, the method comprises the following steps: matching a rewritten question with data in an external database to obtain a related context set; clustering the related context set to obtain a plurality of category clusters; respectively inputting each category cluster into a large language model to obtain summary data of each category cluster; and inputting the summarized data and the original questions into a large language model to obtain answers to the questions. The rewriting problem in the application is a problem obtained after the large language model performs complementation and professional adjustment on the original problem by using own parameter knowledge, so that the quality of the user problem is effectively improved; clustering the related context sets to obtain a plurality of category clusters; and respectively inputting each category cluster into the large language model to obtain summary data of each category cluster, so that a retrieval result with low quality can be prevented from being input.

To reduce the time and effort involved in creating BF materials during sales activities.SOLUTION: A system being the patent application target, comprises: means for coordination with external databases; information automatic complementation means; and sales activity reporting means.SELECTED DRAWING: Figure 1

It is provided a procedure for managing and updating regulatory digital textual documents, wherein the documents each define a textual structure including at least one or more textual portions, one or more reference parameters defined by at least one article and / or one paragraph, and one or more metadata, and wherein the process comprises acquiring a plurality of different documents from at least one external database, extracting the textual portions, the reference parameters and the metadata from each of the textual structures, recognising each textual structure by means of a first logic implementing an artificial intelligence based on a machine learning approach of supervised or zero-shot type, performing a multimodal analysis of the textual structure, by means of the first logic, labelling each of the textual portions, the reference parameters and the metadata to validate the extraction phase, identify the reference parameters and / or the metadata by means of a second logic implementing an artificial intelligence to produce a digital representation compliant with the Akoma Ntoso specifications or other standard for encoding normative texts, to index the textual portions by associating to each textual portion a respective reference parameter and / or a respective metadata by producing a plurality of indexed texts, to record separately each indexed text in an internal database accessible by a user, to logically link each indexed text whose reference parameters and / or metadata are mutually correlated within the database.

A clinical workflow optimization system includes a data processing and analysis platform electrically connected or communicatively coupled to a user device and an HIS server, and a generative AI model set communicatively coupled to the data processing and analysis platform. The data processing and analysis platform provides one or more assistant tools. When the user device selects an assistant tool to execute a task instruction, the data processing and analysis platform uses one model of the generative AI model set to interpret and execute the task instruction. The analysis platform presents the execution result of the task instruction to the user device, wherein data required to execute the task instructions is recorded in the content of the task instructions or provided by the HIS server, the user device, or an external database. On the platform, users can independently develop and build clinical data processing tools using natural language.

The invention provides a chemical reaction prediction method based on multi-agent cooperation, and the method comprises the steps: analyzing the first input content of a first user, generating a corresponding first task flow, and issuing a corresponding task instruction according to the first task flow; accessing an external database or network resources according to a preset query path, recalling a first result corresponding to the first chemical reaction target, and structuring the first result; analyzing and summarizing the structured first result, and extracting a first reaction prediction content corresponding to the first chemical reaction target; performing reasoning prediction on the first input content in combination with the first reaction prediction content to generate a corresponding first chemical reaction prediction result; and integrating and formatting the first chemical reaction prediction result to obtain a second chemical reaction prediction result, and returning the second chemical reaction prediction result to the first user. According to the method, the accuracy of chemical reactant prediction is improved, and the potential illusion problem during large model reply is relieved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating augmented output sequences by using a language model neural network and a plurality of databases. Each database is associated with a respective level of confidence. Each database stores a respective plurality of pre-generated embeddings in an embedding space.

Cybersecurity mission planning and analysis uses artificial intelligence systems to make red and blue team exercises more comprehensive and effective by supplementing individual expertise, reducing reliance on intuition, and eliminating gaps in knowledge. In an embodiment, a platform for cyberattack missions planning and analysis by red and blue teams is coordinated by a control center. An incident generator generates cyberattack scenarios and events using data from external databases and an internal attack knowledge manager having a knowledge graph of data about the network under attack in conjunction with one or more machine learning algorithms configured to identify potential network vulnerabilities. Red are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths. Blue teams are guided by a machine learning algorithm configured to provide suggestions as to potential successful attack paths.