288 results about "Research data" patented technology

A scheduling method and system for the operation of reservoirs to recharge freshwater for repelling saltwater intrusion under changing conditions. Research data of a research area is collected, the research area is generalized, and the river network topology of the area is constructed; the research data acquire the seawater flow data of each station in the research area for N time periods, analyze the correlation between the flow of a predetermined station and the saltwater intrusion and build a seawater flow spatial-temporal evolution model; rainfall data and freshwater runoff of each station are acquired to analyze the variation trends of freshwater flow and hydrological regime of each station and a first ending point and build a freshwater flow spatial-temporal evolution model; and a scheduling model for recharging freshwater for repelling saltwater intrusion is built for simulation, a scheduling method is provided, and a scheduling scheme set is formed.

The invention provides a photovoltaic active distribution network voltage out-of-limit problem tracing method comprising the following steps: receiving research data and carrying out multi-scale feature analysis to obtain a standardized multi-dimensional feature matrix and a time-space incidence matrix; constructing an initial causal graph based on basic research data and power grid physical constraints, and constructing a multi-layer causal network by calculating causal intensity; extracting a dynamic feature sequence, analyzing propagation features, and generating a propagation path set and a path weight matrix; performing tensor completion and state estimation on the research data to obtain a state estimation matrix and estimation uncertainty; and outputting an out-of-limit source position, an out-of-limit risk score and early warning information based on pattern matching analysis and risk assessment. According to the invention, through combination of multi-dimensional feature analysis and a multi-layer causal network, accurate positioning and risk early warning of a voltage out-of-limit source are realized, and traceability accuracy and analysis efficiency are improved.

The invention discloses a multi-agent collaborative data authorization operation management method and system, and the method comprises the steps: carrying out the edge grading, edge verification and desensitization of scientific research data, obtaining desensitized shared data, encrypting and transmitting the desensitized shared data to a cloud sharing platform, carrying out the layering encryption, and generating an uploading log, the method comprises the following steps: generating an encrypted request packet, screening data, providing the data for a research and development institution, generating an intelligent contract, carrying out dynamic authorization to generate an authorization instruction and an authorization log, verifying the authorization instruction, opening a data channel permission to determine visited data, carrying out hierarchical decryption on the visited data to obtain collaborative authorization data, and sending the collaborative authorization data to the research and development institution. And carrying out weight sorting on the collaborative authorization data, accessing and downloading the collaborative authorization data, and updating an access log. The method not only can improve the efficiency and accuracy of scientific research data authorization operation management, but also has good interpretability, and can be directly applied to a scientific research data authorization operation management system.

The invention relates to the technical field of medical data analysis and processing, in particular to a medical scientific research auxiliary method and system based on a large model, a terminal and a medium, and the method comprises the steps: extracting theme meanings based on a natural language processing technology, sending retrieval requests to a plurality of medical literature databases based on the theme meanings, and returning retrieved related literatures; performing feature extraction on the related literatures, classifying the related literatures, and generating a literature review; determining an experiment type based on related literatures and medical knowledge maps in combination with the research problem and an expected target, determining an experiment process and an input demand, obtaining a precise search condition in combination with the input demand, and obtaining corresponding research data based on the precise search condition; a corresponding analysis method is selected for analysis processing, and an analysis result is obtained; and generating a preliminary framework of the thesis based on research data, analysis results and literature review in combination with a standard structure template. The efficiency and quality of medical scientific research are effectively improved, the cost is reduced, and the intelligence and systematicness of scientific research are enhanced.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based spleen and liver trauma model establishment method, which comprises the following steps of: S01, acquiring target data; step S02, obtaining research data; s03, inputting research data into the improved U-Net network model, and performing organ segmentation to obtain a target organ contour; s04, performing three-dimensional reconstruction to obtain a trauma model corresponding to the target organ, and performing hemodynamic analogue simulation; through the step S03, construction of the improved U-Net network model is facilitated, compared with a traditional U-Net network model, the improved U-Net network model can significantly improve the accuracy and robustness of organ segmentation, the effectiveness and accuracy of the spleen and liver contour are enhanced, and an effective foundation is laid for subsequent three-dimensional modeling based on the spleen and liver contour.

The invention discloses an ion adsorption type rare earth ore prospecting method based on a multi-element composite mineralization mode, and belongs to the technical field of rare earth ore geological exploration. The method comprises the following steps: firstly, collecting the existing exploration and research data of the known granite weathered crust type rare earth deposit; analyzing the ore-forming basic elements of the known granite weathering crust ion adsorption type rare earth ore deposit; then field geological survey and sample collection are carried out on the known granite weathered crust ion adsorption type rare earth ore deposit; determining each metallogenic mode element by utilizing analysis test data and field survey data; analyzing the formation cause of the ore deposit, establishing a prospecting mark, and constructing a multi-component composite mineralization mode of the granite weathering crust type rare earth ore deposit. And finally, prospecting target area prediction is carried out. For the known high-altitude granite weathering crust ion adsorption type rare earth deposit, multiple exploration technologies are organically fused, the special environment of the high-altitude area is optimized, a multi-element composite mineralization mode is constructed, and brand-new theoretical guidance and technical means are provided for prospecting of ion adsorption type rare earth ore in the high-altitude area.

The invention discloses a senile syndrome intelligent scientific research data management method and system, and relates to the technical field of data processing. The method comprises the following steps: integrating structured data of a hospital information system, analyzing an unstructured text, and extracting a key entity and a time sequence relationship to obtain multi-source patient structured data and time sequence feature data; performing cleaning, standardization and term normalization on the data based on the elderly syndrome proprietary domain ontology; a special disease data matrix is generated by dragging variables, and a scientific research queue is rapidly screened in combination with a Boolean logic grouping rule; dynamically adjusting random forest model parameters by adopting a wild horse optimization algorithm, training a high-risk prediction model based on historical data, and outputting a real-time high-risk case; combining the SHAP value quantization variable contribution degree and statistical test to generate a scientific research report; according to the method, the problems of medical scientific research data islands, non-uniform terms and poor model interpretability are solved, and efficient integration, accurate prediction and interpretable analysis of the senile syndrome data are realized.

The invention relates to a total primary productivity high-precision downscaling method based on a multi-scale attention mechanism, and belongs to the technical field of remote sensing. According to the method, the technical problems of fuzzy details, fuzzy boundaries and insufficient fitting precision of the existing GPP downscaling technology in a complex terrain region are solved. According to the technical scheme, an Enhanced Multi-scale Residual Attention Network (EMSRAN) model is constructed, multi-source remote sensing data including a medium-resolution imaging spectrometer (MODIS) GPP product and a terrestrial satellite auxiliary factor are integrated, data preprocessing, sample generation and model training and optimization are carried out, and a high-resolution GPP image is output. Technical effects include significant improvement of spatial detail reduction ability, enhancement of heterogeneous ecosystem adaptability, enhancement of boundary definition expression, and improvement of carbon cycle research data reliability.

The invention discloses an office building energy-saving reconstruction method based on neural network optimization, and belongs to the field of office building energy conservation, and the method comprises the steps: carrying out the energy audit of a target building, and obtaining the field investigation data; calculating the field research data based on DeST software, and constructing a building energy consumption model; based on the building energy consumption model, performing energy consumption simulation on various external enclosure structure energy-saving reconstruction scheme combinations to obtain energy consumption data under different enclosure structure combinations; constructing an ANN neural network model, and inputting the historical data set into the ANN neural network model to obtain a prediction model; inputting the energy consumption data under different enclosure structure combinations into the prediction model to obtain an optimal energy-saving scheme; and performing technical economic analysis and environmental economic evaluation on the optimal energy-saving scheme, and generating an energy-saving transformation report.

The invention discloses a scientific research archive data management method for a scientific research institute, and belongs to the technical field of archive management. The system solves the problem that existing archive management mainly depends on manual work, and careless mistakes are prone to occurring in the archive management process, the collected scientific research archives of the scientific research institute are classified, numbered and archived and stored in the archive library, a user can quickly search and call the archives conveniently, and then the archives are stored in the archive library according to the identity of the user. Different permissions are distributed to the user, the user can carry out corresponding retrieval and management on the archive according to the distributed permissions, it is ensured that the user carries out corresponding rights on the premise of following the permissions, and therefore the safety of the archive is ensured, abnormal or suspicious conditions of the user are found through real-time monitoring of user behaviors and operation, and the user experience is improved. And measures are taken in time for intervention, so that the safety of the archives can be further ensured, and the confidentiality and integrity of scientific research data are ensured.

The invention belongs to the technical field of computer software, and particularly relates to a data lake and warehouse management deployment method applied to a domestic operating system, which comprises the following steps: establishing a heterogeneous server cluster, deploying various domestic operating systems in the server cluster, and completing configuration of a network architecture and a basic software environment; the method comprises the following steps: carrying out localization adaptation on a software component source code, synchronously modifying package management logic of a self-research data lake and warehouse management platform, and further implementing intelligent compiling to generate a program package for different instruction set architecture and domestic operating system combination; automatically generating and executing a deployment script by utilizing a self-developed data lake and warehouse management platform to complete installation and configuration of each software component on a target node; and after deployment of each software component is completed, systematic deep tuning and component-level accurate tuning are automatically performed on the deployed system and each software component, and automatic verification is executed. The invention provides an efficient, stable, safe and controllable data lake and warehouse integrated solution for users, wherein the data lake and warehouse integrated solution can be used immediately after being opened.

The invention discloses an investment and research information sharing and distributing method for group business management and control, and relates to the technical field of data sharing, and the method comprises the following specific steps: information collection and integration: collecting an external authority information source and capturing related investment and research information by using a network robot, according to the method, multiple authoritative external information sources are regularly monitored and sorted, investment and research data generated by the internal business departments of the group are collected, the universality and accuracy of the information sources are ensured, meanwhile, intelligent screening is carried out by utilizing a rule-based expert system, and the accuracy of the information sources is improved. According to the method and the system, the pertinence and availability of the information are further improved by combining the social relation data with the user portraits and the project requirements, and the personalized and accurate pushing of the information is realized, so that the group enterprises can quickly obtain the information closely related to the business decision.

The invention belongs to the technical field of ecological restoration evaluation, and provides a geological ecological restoration evaluation method and system for geological disaster prevention and control, electronic equipment and a storage medium. The method comprises the steps of research data acquisition, geological disaster type setting, initial evaluation, data preprocessing, model construction and training, risk determination before restoration, information acquisition after restoration, geological disaster risk prediction and geological ecological restoration evaluation. According to the method, the model is trained by using the remote sensing image, the sound wave data and the initial evaluation data, the evaluation result is predicted through the remote sensing image and the sound wave data, the risk evaluation difficulty and time are reduced, in addition, remote sensing and sound waves are combined, meanwhile, surface features and structural features are extracted, and the prediction effect of the model is optimized; through a multi-mode proximity perception model, the perception capability of the surrounding environment is improved, and the prediction precision of a single block is improved; and through the repair effect evaluation rule, a repair effect evaluation mechanism is optimized.

The invention relates to the technical field of market research data analysis, in particular to a market research data analysis method and system.The method comprises the steps that multi-source data is collected and cleaned through a multi-channel self-adaptive data integration technology, and a comprehensive data set is generated; constructing a graph network structure based on a graph neural network, updating node features through a message passing mechanism, generating market prediction data, and optimizing an investigation path based on a reinforcement learning algorithm; comparing the market feedback data with the prediction data through a feedback self-learning mechanism, and adjusting an investigation strategy to obtain an optimized investigation path; the performance of the market under extreme conditions is simulated through Monte Carlo simulation, the abnormal behaviors of the market are identified through dynamic outlier detection, a coping strategy is generated, and an investigation path is adjusted. The method effectively improves the accuracy of investigation data and the allocation efficiency of investigation resources, and adapts to a complex market environment.

The invention provides a teaching document anti-copy protection method and system based on OCR interference resistance, and belongs to the technical field of information security and digital copyright protection. According to the method, anti-OCR font processing is applied to document characters, invisible disturbance symbols are inserted between characters or paragraphs, watermark information is embedded in document typesetting or font strokes, and finally a standard PDF or DOCX file is compiled. Watermark information is embedded through stroke thickness change, paragraph row spacing indentation perturbation and other modes. The system comprises a font disturbance module, a character disturbance module, a watermark embedding module, a compiling output module and a verification module. According to the method, the human eyes can normally read the document, but messy codes or disorder can occur when the OCR tool extracts the document, and the document cannot be directly reused; meanwhile, watermark information cannot be stripped, so that leakage tracking is facilitated; and the output file keeps a standard format and is compatible with the existing office teaching process. The method is suitable for anti-copying protection of teaching documents, scientific research data, test papers, contract files and the like.

The invention relates to an intelligent hospital management system based on clinical scientific research integration, and the system comprises a clinical scientific research integration platform which is provided with a special disease database data service module which is used for integrating patient clinical data with a patient as a center; the special disease database data service module comprises a special disease scientific research data center which is used for storing high-quality multi-dimensional structured patient data processed by an artificial intelligence technology; the regional decision application module is used for collecting multi-hospital-area data and carrying out hospital diagnosis and treatment and multi-dimensional analysis of medication data; the regional collaborative application module is used for realizing medical resource sharing of multiple hospital areas; and the regional multi-center-line image integration module is used for acquiring image system data of a plurality of courtyards and realizing sharing and mutual recognition. According to the invention, multi-center medical data integration and scientific research collaboration are realized, and the collaborative work and collaborative diagnosis and treatment efficiency is improved.

The invention discloses a disaster prevention and control refined division method based on map data. The method comprises the following steps: step S10, subdividing an evaluation unit based on map data, landform data and administrative division; step S20, comprehensively selecting prevention and treatment alternative areas through risk assessment, vulnerability assessment and disaster assessment; step S30, constructing an index system from three dimensions of dangerousness, exposure and vulnerability; s40, performing index quantification based on the map data and the investigation and survey data, and calculating the weight of each index in combination with the information entropy; step S50, calculating a comprehensive index of each evaluation unit by adopting a Jenks natural fracture method; and step S60, finely delimiting disaster prevention and control key areas in combination with actual management department experience and comprehensive indexes. The method is convenient for later-period management, avoids a large amount of repeated work, and has relatively high objectivity and reference value.

The invention relates to the technical field of fracture network prediction, and discloses a connectivity fracture network structure intelligent generation method based on a GA-GAN model, and the method comprises the following steps: 1, building a fracture network database; step 2, establishing a GA-GAN model; step 2.1, importing information and setting parameters; step 2.2, constructing a discriminator; step 2.3, establishing a generator based on a genetic algorithm; 2.4, training the discriminator; step 2.5, carrying out iterative training; and step 3, automatically generating a corresponding connectivity fracture network structure by using the GA-GAN model. The fracture network structure intelligent generation method successfully solves the problems of low efficiency, high cost, insufficient authenticity and the like in the traditional fracture network generation, and the fracture network generated by the model not only retains the complex geometric characteristics of a real network, but also can adapt to different geological scenes, and has good application prospects. And research data with higher reference value is provided for practical applications such as underground water flow simulation and oil and gas exploitation.

The invention belongs to the technical field of biological medicine, and particularly relates to a screening method of tumor metabolism markers. According to the screening method, wide targeted metabonomics data and targeted metabonomics data are utilized and combined with a biomarker recognition and optimization algorithm (BIO-FIRE) based on function and feature importance recursion enhancement to screen tumor metabolic markers. According to the screening method, a stable metabolic marker combination can be identified in large-scale and multi-center research data, so that a set of efficient, accurate and generalizable gastric cancer metabonomics diagnosis scheme is constructed.

An early screening system for chronic obstructive pulmonary disease based on blood routine test data. It belongs to the technical field of digital medical treatment, and specifically relates to the technical field of intelligent early screening of chronic obstructive pulmonary disease. It solves the technical problem that the generalization ability of a COPD prediction model is limited due to differences between different research data sets. The system comprises a data collection module that collects blood routine test data of a plurality of chronic obstructive pulmonary disease patients and blood routine test data of a plurality of normal personnel; a data preprocessing module that performs standardization processing on the blood routine test data to obtain a standardized data set; a screening model training module that trains N classification models using the standardized data set and calculates the prediction accuracy of each classification model; and a screening model application module that obtains the fusion weight of each model by using a normalization processing method based on the prediction accuracy of each classification model, fuses the risk scores predicted by the multiple models, and obtains a final screening result.

The invention discloses a tumor medical record time axis generation method based on multi-modal recognition and dynamic cue words, which thoroughly solves the problem of disordered stage division of a traditional method by introducing a dynamic cue word mechanism into an operation discrimination node. Through a multi-mode OCR + NLP fusion strategy, the accuracy of medical record information extraction is greatly improved; the medical compliance of the time axis is ensured through time sequence verification and field completion driven by the knowledge graph; and through front-end mind map or medical history outline visualization, the complex medical record can be clearly displayed. Practice shows that compared with manual arrangement, the time consumption of sequential output of the complete medical record is reduced by more than 90%, the item missing rate is reduced by more than 80%, and the method can be widely applied to scenes such as clinical decision support, follow-up visit management, scientific research data integration and medical insurance quality control.

The invention discloses a teacher teaching ability evaluation method based on big data analysis, and relates to the technical field of monitoring analysis, and the method comprises the steps: data collection: collecting teaching data and scientific research data of a teacher through a big data platform; data processing: analyzing and processing the teaching data and scientific research data corresponding to the teacher, evaluating the teaching ability of the teacher based on an analysis and processing result, and determining a comprehensive evaluation coefficient corresponding to the teacher based on an evaluation result; early warning management: monitoring the teachers based on the comprehensive evaluation coefficients corresponding to the teachers, and performing early warning on the teachers based on monitoring results; and feedback estimation: estimating the teaching quality of the teacher according to the comprehensive evaluation coefficient corresponding to the teacher. The method has the effect of improving the teaching ability evaluation efficiency of teachers.

The invention relates to the field of multi-currency quotation, in particular to a multi-currency quotation calculation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an original data set related to quotation; acquiring instant and historical exchange rate data of the transaction currency to form a market data set; calculating a cost currency estimated value of each cost composition item and summarizing to obtain a direct cost total amount; analyzing the research data to obtain a risk level, and providing a corresponding insurance cover scheme; calculating a cash flow estimated value under the influence of the exchange rate by combining the cash flow, the historical exchange rate and the insurance cover scheme, generating and sorting a contract quotation amount sample set according to a preset gross profit rate, determining the minimum contract quotation by combining the confidence coefficient, and obtaining the final contract quotation amount according to the premium parameter. According to the method for calculating the multi-currency quotation, the problems that the traditional quotation exchange rate has no standardized calculation logic and the decision is lack of quantitative support are effectively solved, accurate cost accounting and risk management and control are realized, and enterprise profit and market competitiveness are balanced.

The embodiment of the invention discloses a data processing method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: inputting attribute information corresponding to scientific research data into a pre-trained watermark generation model to obtain watermark information corresponding to the scientific research data; wherein the watermark generation model comprises generation of a resistance network model; embedding the watermark information into the scientific research data to obtain encrypted data; and performing data processing on the encrypted data based on a data sharing request sent by a data sharing party. According to the technical scheme of the embodiment of the invention, the imperceptibility and robustness of the watermark can be improved, the reverse engineering attack of the model can be resisted, and the data security of the scientific research data during sharing is ensured.

To easily perform an information extraction from sensor data or an image and an analysis of them, enable a comparison with the past research data, and support a provision of a preliminary survey and preliminary information.SOLUTION: A system contains: means for performing an information extraction from sensor data or an image and an analysis of them; means having a function for a comparison with the past research data; and means for supporting a provision of a preliminary survey and preliminary information.SELECTED DRAWING: Figure 1

The invention relates to the technical field of river ecological toughness evaluation, and discloses a river ecological toughness evaluation method based on a water quality target and flood season pollution control. The method comprises: setting a water quality target of a target water area; collecting ecological research data of the target water area in the flood season, and dividing the ecological research data into historical research data and current research data according to time; performing statistical analysis on the historical research data to obtain first feature data of each flood season; training a neural network model based on all the first feature data, and generating a prediction model for estimating the water quality recovery time; and performing statistical analysis on the current research data to obtain second feature data, inputting the second feature data into the prediction model, obtaining estimated time for recovering the current initial water quality to a water quality target, and generating a river ecological toughness value according to the estimated time. The method can accurately predict the water quality recovery time under flood season pollution control, scientifically quantify the river ecological toughness, and improve the efficiency of river ecological toughness evaluation.

The invention discloses an automatic driving vehicle accident cause analysis method. The method comprises the steps of obtaining a data set of an automatic driving vehicle accident report; pre-processing the data set, wherein the pre-processing comprises abnormal value cleaning, error data correction and missing data repair; constructing an explanation variable system of an automatic driving vehicle accident, and carrying out statistical description on accident characteristics; an accident cause analysis model is provided by integrating existing automatic driving vehicle accident research data and conclusions based on a random parameter Logit method for solving a heterogeneity problem; based on priori knowledge and an analysis result, designing a random parametric variable, and constructing an RPL model: sampling a probability density function for multiple times by adopting a Monte Carlo method, and taking a simulated probability mean value as an integral approximate solution; and the analysis of the variable random parameter effect in the automatic driving vehicle accident is realized through the RPL model. According to the invention, the cause explanation of the heterogeneity of the important characteristics and influence factors of the accident of the automatic driving vehicle is realized.

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 invention discloses a scientific and technological achievement prediction and evaluation system based on big data, and relates to the technical field of scientific and technological achievement prediction and evaluation, and the system specifically comprises a research and development process data collection module, an information scientific and technological achievement prediction module, and an information scientific and technological achievement comprehensive evaluation module. Wherein the research and development process data collection module is used for collecting historical research data, feature data and network transmission performance data of scientific research projects in the research and development process in real time, and the information scientific and technological achievement prediction module is used for establishing a prediction model of the feature data of the scientific and technological achievement research process and setting research process state indexes based on the feature data. The LSTM prediction module is used for obtaining network transmission performance data in a stationary time range and screening out the network transmission performance data in the stationary time range for further training and optimizing the LSTM prediction model, and the information scientific and technological achievement comprehensive evaluation module is used for comprehensively evaluating the research process stability of the information scientific and technological achievements. And setting an information research fluctuation index to predict the stability of the research process.