36 results about "Taxonomic database" patented technology

The invention provides an acquisition and analysis method and system for 3D printing tool clamp data based on big data, and relates to the technical field of 3D printing and intelligent manufacturing, and the method comprises the steps: obtaining a clamp historical design scheme, machining parameters and sensor data, carrying out the feature extraction and data cleaning, and forming structured data; then, the design intention is analyzed based on a BERT-CNN model, and a clamp applicability classification database is constructed in combination with the classification model; and then, generating a lightweight clamp structure by adopting a generative adversarial network and topological optimization, and optimizing the topological structure of the lightweight clamp structure through an optimization model. Thirdly, simulation analysis is carried out, stress, deformation and thermal stress are evaluated, errors are optimized based on simulation parameters, and optimized clamp design data are obtained; and finally, the clamp is manufactured, stress data are collected in real time, and machining process parameters are optimized through dynamic analysis. According to the method, clamp design and manufacturing are optimized through data driving, and design efficiency and clamp performance are improved.

The invention relates to the field of BIM modeling, in particular to a historical building BIM parametric modeling method. The method comprises the following steps that a historical building component classification database is constructed, the historical building component classification database divides historical building components into seven categories, each category is associated with a parameterization template, the parameterization template comprises geometric parameters and culture attribute fields, and the culture attribute fields are associated with a culture database. Historical building basic data are obtained, contour lines and feature points in the historical building basic data are extracted, and then the parameterized template is matched, so that a parameterized instance is obtained; and assembling the parameterized instances into a historical building BIM model, loading the BIM model and the multi-modal culture data in the culture database in a visual platform, and generating an interactive three-dimensional scene. The modeling of the historical building in a parameterized manner is realized, the engineering difficulty is reduced, and the finally generated three-dimensional scene is more real and immersive.

The invention discloses an intelligent test paper analysis and question bank automatic generation system and method, and the system comprises a question recognition module which is used for analyzing question information from obtained test paper information through a large language model; the question classification module is used for classifying the question information analyzed by the question identification module; and the database management module is used for carrying out structured storage on the classified question information and outputting the corresponding question information according to the received query information. Based on the scheme disclosed by the invention, by combining advanced technologies such as image processing, natural language processing and machine learning, the question bank meeting specific requirements is automatically generated while the test paper is efficiently analyzed.

The invention relates to a training method of an RNA sequence modification prediction model, a prediction method and a related device. The method comprises the following steps: acquiring modification site sequencing data of an RNA sequence; on the basis of sites with target chemical modification and sites without target chemical modification in the modification site sequencing data, a plurality of positive sample sequences with different lengths and a plurality of negative sample sequences with different basic groups added at the central position are obtained respectively, so that a multi-level classification database is constructed; inputting each sample sequence in the multi-level classification database into a to-be-trained modification prediction model to obtain a prediction result of a modification site of each sample sequence; and on the basis of the prediction result and the actual condition of each sample sequence, updating model parameters of the to-be-trained modification prediction model until the trained modification prediction model is obtained. Compared with the prior art, the method can improve the training efficiency and precision of the model.

The invention relates to a new energy power system power flow prediction method considering source-network-load uncertainty, and the method comprises the specific steps: carrying out the normalization processing of the historical parameters of a power grid, extracting multi-dimensional track features through variational mode decomposition, and constructing an operation trend set; a cellular automaton model is combined to simulate a spatio-temporal evolution law, and a fault feature recognition mechanism is optimized; constructing a trend state classification database based on the residual transformation rate; dynamically distributing state contribution weights by using a multi-constraint regression model, and establishing a prediction model; and iteratively optimizing the model through error feedback, and constructing a vulnerability evaluation system based on sensitivity and interference analysis. According to the method, multi-source data and dynamic weight distribution are fused, the problems that traditional prediction precision is low and fault response is lagged are solved through a bidirectional feedback optimization framework, the accuracy of power grid trend prediction and vulnerability assessment can be improved, and support is provided for security defense.

The invention relates to the field of public health, in particular to an intelligent identification and real-time early warning system for unknown pathogens in respiratory tract samples of fever outpatient service, which comprises a data quality control processing module for performing quality control and standardized preprocessing on metagenome original sequencing data to obtain pure microorganism reading length; the known pathogen identification and annotation module is used for identifying known pathogens, classifying non-host reads, constructing a classification database according to a classification result, and annotating reads with low classification confidence or unclassified reads in the classification result to obtain an annotation table; the recognition model training module is used for constructing a positive and negative sample data set and training a recognition model; and the unknown pathogen identification module is used for identifying potential unknown pathogens in the metagenome of the pure microorganism read length by adopting the trained identification model according to the classification database and the annotation list. According to the invention, identification, classification and dynamic monitoring of known and unknown respiratory pathogens can be realized.

The invention provides an equipment evaluation and maintenance suggestion method and system based on multi-modal data fusion, and relates to the technical field of equipment evaluation.According to the method, fault feature retrieval and maintenance suggestion intelligent output are achieved by constructing a classification database containing multi-modal historical fault information, combining weight factors and utilizing a large language model; a vibration signal and a temperature field are effectively fused, a bearing, a strain gauge, a motor, a guide rail and a lead screw are analyzed one by one, feature vectors are extracted, correlation analysis is performed on the feature vectors, multi-source features are fused, an equipment fault evaluation index is generated, and a numerical control machine tool state and maintenance suggestion is output based on the equipment fault evaluation index. The system improves the accuracy and sensitivity of fault diagnosis, can dynamically reflect the health state of equipment, assists a worker in taking maintenance measures in time, and remarkably improves the operation safety and production efficiency of the equipment.

The invention relates to the technical field of computers, and discloses a domestic AI server intelligent control method and system, and the method comprises the steps: setting a special interface at each slot position of a PCIe expansion board, and integrating an in-place detection pin and an I2C bus; directly connecting the in-place detection pin and the I2C bus to a mainboard BMC (Baseboard Management Controller) chip through a special cable; the BMC generates a fan speed regulation instruction based on the slot real-time data; and the BMC provides a GPU parameter input interface and stores the GPU parameter input interface to the classification database. According to the invention, through the synergistic effect of an original hardware direct connection architecture, dynamic mapping control and an elastic adaptation mechanism, the energy efficiency performance of the equipment is obviously improved; according to the method, the traditional lengthy deployment period is compressed to an instant level, and the operation and maintenance complexity of the system is unprecedentedly reduced on the premise of ensuring the extreme computing power output, so that the domestic server realizes breakthrough leap in the aspects of safety, reliability and management agility.

A user requests validation of a human biometric profile to access a bio-origin validation engine (OVE) with a classification database for all types of biological organisms using biometric signals from all type of sensors and devices such as wearables, environmental and peripherals. The bio-origin components are comprised of an OVE validator system, a biological classification system, an intelligent network interface, validated objects, and a service bridge. A validation engine provides access to a secured network of validated users and systems components. The service bridge provides a platform for validated users use of dynamic biometric signals to augment intelligent services outcome and output. Users of the service bridge biometric use metrics are recorded and logged to validate and issue a certificate of ownership and authenticity. The outcome or output is delivered to a bio-origin validated machine readable encoded content with terms and conditions of distribution and use.

The invention discloses a target plate shape dynamic feedback adjustment method, which comprises the following steps of: firstly, establishing a target plate shape classification database, and forming a rolling mill target plate shape curve of classified strip steel; the coefficient a1 of the target strip shape curve is subjected to iterative calculation according to the control period, a1 in the period is equal to a1 + delta a1 in the last period, delta a1 is a deviation correction coefficient, and a new target strip shape curve is formed and issued to a rolling mill strip shape control system for production; and then the deviation condition of the period is obtained according to the through plate stability data of the batch of classified strip steel produced by the subsequent unit, the deviation correction coefficient delta a1 of the classified strip steel is calculated, the step starting from the target strip shape curve coefficient a1 is repeated and iterated, and dynamic feedback adjustment of the target strip shape is completed. The target plate shape of the rolling mill is changed from static setting to dynamic setting and can be dynamically adjusted in a targeted manner according to the actual plate passing condition of a subsequent unit, so that the plate passing stability is improved; and target plate shape adjustment work is carried out in a classified manner, so that the efficiency is improved, and the safety and the feasibility are improved.

A method for automatically assigning changed permissions for diagnostic purposes for work container instances is provided including: in a reconfiguration unit of a runtime environment of the device or an orchestration device, receiving configuration information for starting a container instance, identifying diagnostic configuration information for providing at least one diagnostic container instance of a diagnostic application that carries out diagnostic activities, retrieving existing work container instances from a classification database, determining a reconfiguration requirement of the retrieved work container instances, changing initial work configuration information to provide diagnostic data according to the diagnostic configuration information for each work container instance for which a reconfiguration requirement has been determined, restarting the changed work container instances according to the changed work configuration information, and starting the at least one diagnostic container instance of a diagnostic application according to the diagnostic configuration information.

The present invention relates to a method for predicting the flow of a new energy power system that takes into account source-grid-load uncertainty. The specific steps are as follows: normalizing historical parameters of the power grid, extracting multidimensional trajectory features through variational mode decomposition, and constructing an operating trend set; combining a cellular automaton model to simulate spatiotemporal evolution and optimize the fault feature recognition mechanism; constructing a trend state classification database based on the residual transformation rate; dynamically assigning state contribution weights using a multi-constraint regression model to establish a prediction model; iteratively optimizing the model through error feedback, and constructing a vulnerability assessment system based on sensitivity and interference analysis. The present invention integrates multi-source data with dynamic weight allocation, and through a two-way feedback optimization framework, solves the problems of low traditional prediction accuracy and delayed fault response. It can improve the accuracy of power grid trend prediction and vulnerability assessment, providing support for security defense.

The invention relates to the technical field of artificial customer service, and discloses a method for deep fusion of artificial intelligence and customer service scenes. The artificial intelligence and customer service scene deep fusion method comprises the following steps: establishing an artificial intelligence customer service learning system; establishing a classification database; establishing an entry analysis system; according to the invention, the established classification database contains most customer service scene fields in the market, and the artificial intelligence customer service can automatically answer the questions in different fields proposed by the customer, so that the adaptability and flexibility of the artificial intelligence customer service are greatly improved; when an artificial intelligence customer service cannot respond to a customer according to the content in the classification database, the artificial intelligence customer service can be automatically switched to ensure the flexibility of the artificial intelligence customer service, and entries are established and stored in the industry sub-database according to the content required by the customer and responded by the artificial customer service in combination with product words and customer demand words, so that the user experience is improved. And automatic expansion of classified database contents is realized.

The application relates to a training method and a prediction method of an RNA sequence modification prediction model and related devices. The method comprises the following steps: obtaining modification site sequencing data of an RNA sequence; based on sites with target chemical modification and sites without target chemical modification in the modification site sequencing data, a plurality of positive sample sequences with different lengths and a plurality of negative sample sequences with different bases added at a central position are obtained respectively, so as to construct a multi-level classification database; each sample sequence in the multi-level classification database is input into a to-be-trained modification prediction model respectively, and a prediction result of a modification site of each sample sequence is obtained; based on the prediction result and an actual situation of each sample sequence, model parameters of the to-be-trained modification prediction model are updated until a trained modification prediction model is obtained. Compared with the prior art, the application can improve the training efficiency and accuracy of the model.

The invention discloses an industrial product dynamic display system with an intelligent recommendation algorithm, and the working process of the system comprises the steps: a data collection and storage stage: building an industrial product three-dimensional model database, and achieving the digital storage and management of product data; performing multi-dimensional classification labeling on the warehousing products, and constructing a structured product database; a user behavior analysis stage: collecting and recording product browsing behavior data of the user in real time; analyzing user browsing records based on the consulting quantity and the consulting time, and quantifying the preference degrees of the user for different types of products; in the intelligent recommendation stage, matched products are screened from the classification database for personalized display in combination with a product classification system and user preferences. According to the invention, the user experience can be improved. The method can be widely applied to the field of intelligent interaction.

The present invention is grading method for establishing the response of EEG files to physiological emotions through human factor lighting (HCL) system and EEG includes the following steps: Step 1, enhance spectrum which obtained the specific color temperature with synergistic effect on the specific physiological emotional response identified by fMRI; Step 2, making the user wear an EEG and give the element with specific emotional stimulation to stimulate the user's specific physiological emotion and induced the user's specific physiological emotion; Step 3, carry out the lighting program, after step 2, start the HCL system to lighting the user with different color temperatures, record and store the EEG files after lighting with different color temperatures; Step 4, EEG files that classify specific emotions by similarity through the learning method of AI; Step 5, establish the EEG classification database according to the similarity ranking of EEG files with specific color temperature.

The invention provides a spacecraft general assembly data fusion processing system and method, and the system comprises a dual-core architecture which comprises a general assembly cooperation model module and a knowledge-driven engine module; the hierarchical classification database is connected with the dual-core architecture; the version penetration management module is used for establishing a product version penetration management mechanism based on a preset code; and a rule verification engine for automatically verifying the data based on a preset rule. The method comprises the following steps: carrying out collaborative sharing and intelligent decision making through a dual-core architecture, storing spacecraft general assembly data into a hierarchical classification database for standardized storage, establishing a product version penetration management mechanism, and automatically checking the data based on a preset rule obtained from a knowledge-driven engine module. According to the method, the data normalization and accuracy can be remarkably improved, the full-life-cycle accurate tracing of the data is realized, and the cross-professional collaboration efficiency is improved.

The application discloses a method and system for homologous heterogeneous power grid topology mapping, and belongs to the technical field of power system simulation calculation. The method comprises the following steps: establishing a classification database according to a classification result; determining the mapping mode of homologous heterogeneous power grid equipment according to the type of power grid equipment and the modeling mode of power grid equipment; determining the unique power grid topology of the first-level network of the power grid network; determining the electrical data of the second-level network according to the power grid topology, and mapping the electrical data to the classification database; determining the primary equipment data of the third-level network according to the power grid topology, and mapping the equipment data to the classification database; for the external network, generating a supplementary file according to the power grid topology of the primary equipment connected by the external network, and mapping the supplementary file to the database. The application realizes the fusion of multi-source data.

The application discloses a method for detecting viruses and virus replication in media based on Small-RNA high-throughput sequencing. The application belongs to the technical field of virus detection, and the method for identifying virus types in a to-be-detected sample by Small-RNA sequencing developed by the application comprises the following steps: 1) extracting total RNA from a virus-infected to-be-detected sample, constructing a cDNA library, and separating small RNA sequence fragments from the cDNA library; 2) downloading virus sequences and classification databases from GenBank, and comparing the small RNA sequence fragments with virus sequence files; 3) comparing a contigs3 file with a virus nucleic acid database, selecting sequences with the minimum e value in the comparison results in the contigs3 file, calculating the coverage of the contigs3 sequences in corresponding virus genomes, distinguishing virus information with a coverage threshold, and deducing the virus source of the small RNA sequence.

The invention discloses a dynamic comprehensive monitoring platform for an artificial fish reef, which obtains ecological data such as fish varieties and water quality parameters in real time through a data acquisition module, ensures timeliness and continuity of the data, enables managers to master ecological dynamic states immediately, and overcomes the defect that the traditional method is difficult to know ecological changes in time. Meanwhile, a classification database construction module stores data in a multi-dimensional classification mode, regionalized reference data are formed, a dynamic monitoring and evaluation engine calculates an ecological benefit comprehensive index according to the data, multi-dimensional comprehensive evaluation can comprehensively and systematically reflect the ecological benefits of the artificial fish reef, and the problem that the evaluation index is single is solved; besides, the intelligent early warning and decision support module sets a safety threshold value, performs automatic early warning when indexes are abnormal, and generates a targeted and operable repair suggestion report, so that the platform can respond quickly, managers can take repair measures in time, ecological problem deterioration is avoided, and the problem that targeted repair cannot be performed in time is effectively solved.

The invention provides a full life cycle competency data acquisition and storage system based on observable behavior classification, and belongs to the technical field of personnel training evaluation. The system comprises a competency-OB classification analysis module used for performing multi-dimensional analysis on competency and observable behaviors to generate a structured classification framework; the multi-source data acquisition module is used for acquiring data from multiple systems in real time and automatically marking the data according to a structured classification framework; the special classification database cluster adopts a three-layer architecture to support efficient data processing; the data quality guarantee module is used for implementing verification in multiple links to guarantee data quality; the offline-online integrated module supports full-function offline evaluation and automatic account checking; the block chain evidence storage interface ensures that the evaluation record cannot be tampered; and the standardized API gateway provides an external system interface. The technical problems that traditional competency assessment data acquisition is low in structuralization degree, poor in cross-system interoperability, insufficient in data credibility and the like are solved, and the method can be widely applied to full-life-cycle training and assessment of high-risk occupational posts such as civil aviation and nuclear power.

The invention relates to the technical field of data processing, in particular to a processing method for improving the collection efficiency of localized translation demand data, which comprises the following steps: acquiring translation demand data at each moment; obtaining a database recommendation set of the translation demand data at each moment by combining the professional degree of the translation demand data at each moment according to the text information similarity condition between the translation demand data at each moment and the translation demand data in each classification database; according to the intersection condition of the database recommendation set corresponding to the current moment and the database recommendation set corresponding to the historical moment, the database recommendation set at the current moment is updated in combination with the recommendation priority degree of the classification database in the database recommendation set at the historical moment; and carrying out collaborative filtering recommendation on the translation demand data at the current moment based on the updated database recommendation set at the current moment. According to the invention, the collection efficiency of the localized translation demand data is improved.

The invention relates to the technical field of substance and component extraction, and discloses a plantain herb antioxidant substance extraction system which comprises a data extraction module, a first data acquisition module and a prediction antioxidant module. The antioxidant substances in the plantain herb are extracted under the assistance of ultrasonic waves, and the extract is subjected to antioxidant activity evaluation and main component analysis. Through antioxidant activity evaluation and main component analysis, antioxidant experiment records are obtained from a plantain molecular experiment database, classification is performed according to an antioxidant determination method, and an antioxidant data dichotomy database is constructed. And constructing an osteoarthritis simulation model based on the osteoarthritis data, training the osteoarthritis simulation model by using the data in the anti-oxidation dichotomy database, and predicting the influence of the plantain herb anti-oxidation data on the osteoarthritis. The method is used for extracting the antioxidant substances in the plantain herb, greatly saves the separation and purification period, and effectively evaluates the potential influence of the antioxidant substances in the plantain herb on the osteoarthritis treatment effect.

Systems and methods for identifying travel way features in real time are provided. A method can include receiving two-dimensional and three-dimensional data associated with the surrounding environment of a vehicle. The method can include providing the two-dimensional data as one or more input into a machine-learned segmentation model to output a two-dimensional segmentation. The method can include fusing the two-dimensional segmentation with the three-dimensional data to generate a three-dimensional segmentation. The method can include storing the three-dimensional segmentation in a classification database with data indicative of one or more previously generated three-dimensional segmentations. The method can include providing one or more datapoint sets from the classification database as one or more inputs into a machine-learned enhancing model to obtain an enhanced three-dimensional segmentation. And, the method can include identifying one or more travel way features based at least in part on the enhanced three-dimensional segmentation.

The present application relates to the field of engineering surveying and mapping technology, in particular to an engineering surveying and mapping data intelligent acquisition, analysis and processing system and method, mainly applied to data acquisition, analysis and processing in the process of engineering construction; the system covers image acquisition, data classification, database storage, data processing, data analysis and decision feedback modules; the image acquisition module obtains an engineering surveying and mapping image feature set, the data classification module receives and classifies and stores the data; the database stores the classified data, the data processing module divides and calculates the importance of the image feature nodes; the data analysis module identifies abnormal data, and the decision and data feedback module receives the identification result and adjusts the data threshold; the system significantly improves the surveying and mapping precision through feature node processing and importance calculation; at the same time, the multi-level comparison mechanism improves the accuracy of abnormal detection, and provides efficient and accurate data support for engineering construction.

The present invention relates to the field of data processing technology, and in particular to a processing method for improving the efficiency of collecting localized translation demand data, comprising: obtaining translation demand data at each moment; obtaining a database recommendation set for the translation demand data at each moment based on the similarity of text information between the translation demand data at each moment and the translation demand data in each classified database, combined with the professionalism of the translation demand data at each moment; updating the database recommendation set at the current moment based on the intersection of the database recommendation set corresponding to the current moment and the database recommendation set corresponding to the historical moment, combined with the recommendation priority of the classified database in the database recommendation set at the historical moment; and performing collaborative filtering recommendation on the translation demand data at the current moment based on the updated database recommendation set at the current moment. The present invention improves the efficiency of collecting localized translation demand data.

The present application relates to the technical field of data governance, and particularly relates to an intelligent data grading and classifying method and system based on data portrait, which comprises the following steps: extracting field information from a database to be classified, and inputting a large language model to extract field semantic features; determining a classification system according to the field semantic features, and constructing a classification definition of the database to be classified; obtaining a table creation statement, extracting grading data from the database to be classified according to the table creation statement, grading the grading data according to the classification definition, and generating a grading data table corresponding to the table creation statement. In view of the problem that data grading and classifying rules are prone to failure, a large language model is introduced to identify the field information in the database, so as to obtain the field semantic features of the database. In the subsequent grading process, the large language model can be used to describe the role of each field data in the database, so that the model can accurately determine the category and importance of the related data, thereby obtaining the corresponding grading data table.

The present invention provides a method and system for collecting and analyzing 3D printing tooling fixture data based on big data, which relates to the fields of 3D printing and intelligent manufacturing technology. It includes obtaining fixture historical design schemes, processing parameters, and sensor data, and performing feature extraction and data cleaning to form structured data. Then, based on the BERT-CNN model, the design intent is parsed, and a fixture applicability classification database is constructed in combination with a classification model. Subsequently, a lightweight fixture structure is generated using a generative adversarial network and topology optimization, and its topology structure is optimized through an optimization model. Next, simulation analysis is carried out to evaluate the force, deformation, and thermal stress, and the error is optimized based on the simulation parameters to obtain the optimized fixture design data. Finally, the fixture is manufactured and the force data is collected in real time, and the processing process parameters are optimized through dynamic analysis. The present invention optimizes the fixture design and manufacturing through data driving, improving the design efficiency and fixture performance.

The application discloses a substation secondary equipment modeling method and device based on a knowledge graph and a medium. The method comprises the following steps: first classifying a basic database of substation secondary equipment according to the types of the secondary equipment, obtaining a first classification database, processing a data set of each type of secondary equipment in the first classification database according to characteristic information of the secondary equipment, obtaining a characteristic matrix and an equipment type matrix of all secondary equipment of the substation, calculating the correlation degree of all secondary equipment of the substation through a correlation degree calculation network trained according to the characteristic matrix and the equipment type matrix of all secondary equipment of the substation, constructing a knowledge graph according to obtained entities, attributes and relations, and completing substation secondary equipment modeling. The application classifies the secondary equipment first, extracts features from the classified data set, and calculates the correlation degree for each feature, thereby improving the working efficiency and the modeling accuracy.

A computer system, computer program product, and method are provided. The method includes analyzing a performed query by identifying a plurality of indicative markers based on a pre-stored taxonomy database associated with the performed query; generating a plurality of facets based on the analysis of the performed query; selecting at least two facets from the generated plurality of facets by determining a quantified similarity value between each respective facet and the plurality of identified indicative markers associated with the performed query; prioritizing the selected facets by using a supervised machine learning algorithm based on a computed total score associated with an assigned weighting value of each selected facet from the generated plurality of facets, thereby dynamically ranking the selected facets; and displaying the dynamically ranked facets within a user interface of a computing device associated with a user.