19 results about "Research process" patented technology

The embodiment of the invention provides an e-commerce product market research method and system based on artificial intelligence, and the method comprises the steps: S1, analyzing user input information, converting the information into structured data, and decomposing the structured data into a plurality of subtasks; s2, analyzing the requirements of the sub-tasks and the dependency relationship between the sub-tasks by using the large language model, and generating an investigation strategy; s3, calling the MCP tool according to the investigation strategy to generate preliminary investigation data; s4, evaluating the preliminary investigation and survey data, judging whether each subtask reaches an expected investigation and survey requirement or not, and obtaining an evaluation result; s5, judging whether the preliminary investigation data is sufficient or not according to the evaluation result; and S6, calling a special statistical MCP tool according to the research result, and generating a research report. According to the method, data sufficiency is guaranteed through dynamic evaluation and supplementary investigation through a large language model and an MCP tool, an investigation report is finally generated, the whole investigation process is intelligent, and efficiency and conclusion credibility are improved.

The application relates to a multi-round scientific research workflow evaluation system and method for a multi-modal intelligent agent, and belongs to the technical field of large language models and multi-modal large models. The application comprises a multi-round task generation module, a multi-modal information processing module, and the like. The multi-round task generation module constructs a multi-round task scene closer to a real scientific research process. The multi-modal information processing module enables the measured intelligent agent to complete paper active retrieval, multi-modal evidence extraction alignment, cross-paper evidence integration, and round reuse of evidence. The protection points of the application are not limited to a single task sample or a single scoring index, but are focused on a multi-round evaluation benchmark constructed around a real scientific research workflow, a process-level evaluation protocol matched with the benchmark, and an overall implementation scheme supporting landing execution and fine-grained diagnosis of the benchmark.

The invention belongs to the technical field of artificial intelligence, and discloses a knowledge service management method and device fusing generative AI and enhanced retrieval. Comprising the steps of constructing a knowledge base for multi-source heterogeneous data fusion, deploying a dynamic task planning engine, executing a whole-process service, establishing an evaluation and feedback closed loop and the like. Wherein the whole-process service covers AI knowledge questions and answers, AI enhanced retrieval, AI intelligent research and reading, AI auxiliary creation and intelligent agent service, and through intention recognition, mixed retrieval, RAG enhancement, cue word engineering, Deep Research and other technologies, the whole-process run-through from accurate knowledge acquisition and deep content understanding to generative creation is realized. The problems that in the prior art, knowledge obtaining efficiency is low, literature processing intellectualization is insufficient, and scientific research processes are split are solved, a credible, controllable, processized and intellectualized closed-loop knowledge service system is constructed, the efficiency and innovation ability of scientific research work are remarkably improved, and the system is suitable for various academic research and scientific research management scenes.

The invention provides a library subject service intelligent recommendation method and system based on big data mining, and relates to the technical field of library services.The method comprises the steps that firstly, library subject service big data is mined, and subject user research process features covering subject user research stages, resource use tracks and demand evolution features are constructed; and establishing a dynamic adaptation relationship between the research process and the resources in combination with a library subject resource pool. And splitting resource service modules according to research process stages to form staged subject resource service architectures, and generating a staged subject service recommendation list after series connection. And collecting user research process data in real time, dynamically adjusting a recommendation list, supplementing transition resources and services, and forming dynamic subject service recommendation contents. And finally, outputting recommended content and collecting feedback data, and integrating big data to optimize research process characteristics and dynamic adaptation relationships. According to the invention, accurate, dynamic and systematic library subject service recommendation can be provided for the user, and the service quality and the user research efficiency are improved.

The application discloses an error-driven adaptive research process rectification method and system, and relates to the technical field of natural language processing. The method comprises the following steps: decomposing a research task submitted by a user into a plurality of subtasks with a dependency relationship, and generating an initial directed acyclic graph (DAG); executing each subtask according to the DAG, and performing logical verification on an intermediate result to identify an error type; when an error is identified, mapping the error type to a rectification action; adjusting the DAG according to the rectification action, including deleting an error branch, reconstructing a node sequence or inserting a new node; repeatedly executing the process based on the updated DAG until a research completion condition is met, and generating a research report. The application realizes nonlinear re-planning through dynamic adjustment of the DAG, effectively avoids error accumulation, and improves the accuracy and reliability of a complex research task.

A system and method for AI-mediated discovery, notification, and licensed access to copyrighted works during a research session. The system comprises: a content registry in which copyright holders register works and grant trusted read access to authorized AI tools; a trusted content ingestion module that reads the full text of registered works and generates internal representations including semantic embeddings, structural analysis, and portion-level summaries, and generates indexing information for the content registry; an AI research tool that draws on its comprehension of registered works to identify works relevant to a user's research query and present a non-disclosive notification describing relevance and pricing; a trust boundary enforced by technical and contractual controls preventing disclosure of protected content prior to authorized payment; a transaction processing layer that processes payment from the user to the copyright holder; and a content delivery module that delivers the authorized portion within the research session.

The invention relates to an interactive discussion platform construction method based on a knowledge graph, and belongs to the field of discussion. A task initiator constructs a research task framework; the research task framework and the research tasks in different fields are distributed to researchers in the corresponding fields; the researchers carry out association analysis on the received research tasks; obtaining an answer, generating a conclusion and reporting the conclusion to the task initiator; the task initiator performs primary examination and approval on each conclusion and sends a primary examination and approval result of each conclusion to summarizing personnel; after summarizing by the summarizing personnel, feeding back to the task initiator for secondary examination and approval; after secondary examination and approval, outputting a research report; according to the invention, standardization and intellectualization of the discussion process are realized, high-efficiency discussion communication is supported, and an effective conclusion is output.

The invention relates to an isomerization compiling method based on a mimicry system simulation platform, which comprises the following steps: a user uploads an application service source code file A in an operation page of the mimicry system simulation platform, and sends an http request to a mimicry isomerization compiling service; the mimicry isomerization compiling service firstly carries out preprocessing operation and parameter verification work; the mimicry isomerization compiling service analyzes parameters transmitted in the http request, matches a target architecture required by a user according to transmitted arcs parameters, and constructs directory parameters; the mimicry isomerization compiling service traverses a configuration parameter arcsarg array corresponding to the target architecture parameter, and corresponding makefile files are constructed in sequence; the mimicry isomerization compiling service stores a plurality of output heterogeneous simulation application service files to a specific directory Y; and returning the result to an operation page of the mimicry system simulation platform. The problem that a large number of physical resources are occupied in the scientific research process of the mimicry system is solved, research and development and verification work can be more conveniently carried out by researchers, and the research and development cost is saved.

The invention provides a blockchain-based platform for secure management and sharing of research data. It integrates modules for smart contract execution, decentralized data marketplaces, dynamic consent management, cross-chain interoperability, and advanced security features, including post-quantum cryptography and multi-signature authentication. The platform ensures data integrity, transparency, and compliance with evolving privacy regulations by automating research agreements, incentivizing participation through tokenomics, and enabling seamless multi-party collaborations. A decentralized reputation scoring system and comprehensive version control enhance accountability and traceability of research activities. Additionally, the platform supports data anonymization and dynamic consent updates, safeguarding participant privacy while fostering collaborative research. By leveraging AI-driven analysis and decentralized governance through a DAO model, the invention optimizes research processes, facilitates efficient data exchange, and promotes ethical standards in research environments, offering a robust and innovative solution to the challenges of modern research data management.

The application discloses an intelligent auxiliary system for medical scientific research training based on an artificial intelligence large model, and particularly relates to the field of artificial intelligence and scientific research data processing, and is used for solving the problems of multi-source dispersion, non-uniform structure, missing steps difficult to identify and dependence on manual verification of experimental records in the existing medical scientific research process; a digital experimental record file is constructed by image acquisition on an experimental table record carrier and in combination with target detection and text recognition, logical breakpoints are recognized based on numerical jump and semantic co-occurrence, and jump record recognition is realized by using phenotype feature distribution and optimal transport calculation; missing steps are further reasoned and completed by dynamic programming, and finally, a complete experimental process draft is generated in combination with a structured template, automatic analysis, abnormal identification, process completion and multi-modal data fusion processing of scientific research data are realized, and the intelligent level of experimental data processing and the standardization of the scientific research process are improved.

The invention discloses a ship demand automatic generation and difference adaptation method and system based on a historical demand template library, and the method comprises the steps: building a hierarchical and classified historical template library (comprising a universal template, a professional template and a subitem template), receiving the natural language input of a user, and carrying out the automatic generation and difference adaptation of the ship demand. Analyzing and extracting demand elements by using a natural language processing technology (such as a BERT model); calculating a semantic, parameter and context matching degree with a template library through a multi-dimensional matching function, and screening candidate templates; starting a research process (including automatic query of an external knowledge base and manual confirmation) based on difference analysis, and dynamically adjusting the template (such as parameter calibration and entry addition and deletion); and carrying out association verification on the generated demand draft and the ship design model, and finally outputting a customized document and updating a template library to realize self-evolution. The method solves the problems that a traditional method is low in efficiency and poor in adaptability, the automation degree and accuracy of demand generation are remarkably improved, and the method is suitable for demand customization design of various ship projects.

The application provides a method for predicting the aggregation structure of layered minerals based on density functional theory, adopts the density functional theory in the first principle, constructs a layered mineral cell model and an inhibitor molecule model by using Material Studio quantum simulation software, and cuts the cell model; the structure of the inhibitor molecule and the layered mineral cell is optimized by a CASTEP module, and the adsorption energy of the inhibitor molecule on the edge and the surface of the layered mineral is calculated, the aggregation structure of the inhibitor molecule to the layered mineral is predicted according to the adsorption energy data; finally, the prediction result is verified by a cryo-EM, and a basis is provided for further optimization. The adsorption energy of the layered mineral is calculated based on the density functional theory, the aggregation structure between the layered minerals can be quickly predicted; the application is beneficial to understanding the aggregation structure of the layered minerals from the atomic level, so that more efficient flotation reagents and optimized flotation processes can be designed, and the experimental cost in the research process is reduced.

We provide the system. [Solution] Means of collecting research information, A means of analyzing collected research information using natural language processing technology, A means of proposing a research process to the user based on the analyzed results, A means of providing learning materials to support the improvement of expertise for new users, A means of notifying expert users of the latest research results and technological trends, A means of recording users' technical knowledge and thought processes, and accumulating knowledge within the organization, A system that includes this.

The invention discloses an ultrasonic-assisted arc additive numerical simulation and process optimization strategy method aiming at the problems that in the research process of an existing ultrasonic-assisted additive manufacturing process, the influence of ultrasound on a molten pool is not clear, and process optimization depends on a large number of tests. The method comprises the following steps: introducing an ultrasonic volume force model into a macroscopic heat and mass transfer model, establishing an acting force calculation formula of ultrasound on fluid, adding a momentum source item into a built-in momentum conservation equation, finally forming an ultrasonic-assisted heat transfer metallurgical coupling model in combination with a cellular microscopic model, solving a macroscopic heat and mass transfer result and simulating a dendritic crystal growth process. And performing loop optimization on the process based on comparative analysis of a simulation result and an optimization basis. According to the method, internal mechanism research and microstructure prediction of additive molten pool behaviors under the ultrasonic effect can be achieved, a numerical simulation means is used for replacing process tests, cost loss caused by a large number of tests in the process optimization process is solved, and design and optimization of the additive manufacturing process are scientifically and efficiently carried out.

The invention discloses agricultural product fresh-keeping observation research equipment for research, which comprises a support frame provided with a plurality of independent storage grids; the adjusting system is arranged on the supporting frame and provided with a CO2 concentration adjusting assembly, a humidity adjusting assembly and a temperature adjusting assembly, and the CO2 concentration adjusting assembly, the humidity adjusting assembly and the temperature adjusting assembly extend to all the storage grids; the observation box systems are detachably placed in the corresponding storage grids and are connected with a CO2 concentration adjusting assembly, a humidity adjusting assembly and a temperature adjusting assembly in the adjusting system so as to control comparison variables in the observation box systems. By means of the structure, the crop observation environment can be adjusted and observed in the research and study process, operation can be accurately recorded, and therefore the accuracy of observation items in the research and study process is improved.

A system and method for autonomously optimizing clinical research processes through a distributed multi-agent artificial intelligence architecture. One or more processors comprising a neural processing hub and an AI inference accelerator execute a research process optimizing module across a distributed network of computing devices. An automated enrolment and retention module parses unstructured medical records using Natural Language Processing and generates high-dimensional semantic vector embeddings stored in a persistent vector database. A list generating module executes a Transformer-based Large Language Model to perform semantic vector matching against clinical study protocol criteria to generate a ranked list of qualified research participants. An integrated Generative AI chatbot facilitates participant engagement and informed consent. A Reinforcement Learning agent autonomously assigns principal investigators for screening. A primary source data capturing module utilizes Convolutional Neural Networks for automated Optical Character Recognition and deep learning anomaly detection. A financial module performs autonomous reconciliation and invoice generation with verified data recorded on a distributed ledger.

This increases the stability of the research process and reduces costs and effort. [Solution] The processor 11 of the information processing device 1 functions as a means 26 for extracting secondary users from primary users based on account type and tertiary users from secondary users based on the number of posts, a means 27 for requesting the SNS server device 4-1 to send posted data of tertiary users, a means 29 for sending a prompt to the generation AI 5 along with the posts to instruct the generation of tertiary users to generate profiles based on the posts, a means 30 for sending a prompt to the generation AI 5 along with the profiles to instruct the cluster classification of tertiary users based on the profiles, a means 31 for sending a prompt to the generation AI 5 along with the profiles to instruct the generation of tertiary users to generate personas for each cluster based on the profiles, and a means 32 for creating a research report using the personas for each cluster.

The application provides a scientific research data processing method, a processing system and an electronic device, including: acquiring multi-modal process data corresponding to a target scientific research task; performing context alignment processing on the multi-modal process data to obtain context feature data; performing feature extraction and sensitive information processing on the context feature data to generate a data package; wherein the data package includes desensitized general feature information and corresponding result labels; in response to an external search request, determining a target data package matching the external search request based on the general feature information; recording the call flow information of the target data package to trace the origin of the target data package. This way can reconstruct the context association of the scientific research process data while protecting the privacy of the core data, so that the intermediate process data and the unsuccessfully verified data can be safely searched, reused and traced, thereby improving the flow value of the scientific research process data.

The application discloses an error-driven adaptive research process rectification method and system, and relates to the technical field of natural language processing. The method comprises the following steps: decomposing a research task submitted by a user into a plurality of subtasks with a dependency relationship, and generating an initial directed acyclic graph (DAG); executing each subtask according to the DAG, and performing logical verification on an intermediate result to identify an error type; when an error is identified, mapping the error type to a rectification action; adjusting the DAG according to the rectification action, including deleting an error branch, reconstructing a node sequence or inserting a new node; repeatedly executing the process based on the updated DAG until a research completion condition is met, and generating a research report. The application realizes nonlinear re-planning through dynamic adjustment of the DAG, effectively avoids error accumulation, and improves the accuracy and reliability of a complex research task.