1524 results about "Data selection" patented technology

The invention discloses a cloud edge cooperative control method, which comprises the following steps: establishing a cloud edge cooperative architecture, collecting data by edge nodes, and selecting a corresponding data compression strategy to compress the data; generating an edge-cloud collaborative uploading data stream; the cooperative control layer executes adaptive load balancing based on a cloud-side node state, task characteristics and a network condition which are monitored in real time so as to realize task allocation and scheduling between the cloud and the side; the cloud receives the data stream transmitted by the edge node, decompresses and processes the compressed data according to the metadata information, completes the task and feeds back an execution result; a node performance prediction model is constructed by using system state monitoring information, a task allocation strategy and a resource allocation and data compression strategy are adjusted in real time according to a prediction result, and cloud-side collaborative optimization is realized. Efficient data transmission, flexible task scheduling and self-adaptive resource management are realized, and the performance of the system in a complex and changeable environment is comprehensively improved.

The invention provides an encryption transmission method and system of a wireless information transmission communication base station, relates to the technical field, discloses the encryption transmission method of the wireless information transmission communication base station, and belongs to the technical field of wireless communication. The method comprises the following steps: receiving a communication signal and extracting identity authentication information and a signal characteristic parameter; performing identity authentication scoring based on a deep neural network, performing signal feature scoring through spectral analysis, and constructing a communication security assessment model; a key pair is generated by adopting an elliptic curve cryptographic algorithm, and data is encrypted after being subjected to confusion transformation and diffusion transformation; transmitting a key by using a quantum key distribution technology; and selecting an optimal transmission channel based on the operation state data of the communication base station, and performing data transmission by adopting interleaving coding and adaptive power control technologies. According to the invention, the security and transmission reliability of wireless communication are improved, and the anti-interference capability is enhanced.

The invention provides an intelligent human resource training recommendation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining post data, skill data, performance data, training records and development target data of a target, building an employee database, and constructing a knowledge graph; post data, skill levels, performance expressions, training records and individual development targets of the employees are integrated to form a comprehensive portrait; dividing all courses according to difficulty levels, selecting courses with corresponding difficulty according to post requirements and performance data of the employees, and generating a group of preliminary course lists matched with current skill levels and post requirements of the employees; semantic analysis is carried out on skill requirements of employee portraits and recommended course contents, a semantic analysis result is obtained, and according to employee portrait data, a semantic matching algorithm is used to calculate the matching degree between the employees and each training content; and according to the matching degree scores of the employees and the training contents, generating a course list subjected to semantic matching optimization.

The invention relates to the technical field of information synchronization, in particular to a cross-platform electronic communication synchronization system, which comprises a network construction and path optimization module for collecting basic network parameters of communication equipment of a plurality of platform networks, obtaining cross-platform network data according to the basic network parameters, selecting an optimal transmission path according to the cross-platform network data, and transmitting the optimal transmission path to a server; and generating real-time optimized path information. In the invention, the basic network parameters of the communication equipment of the plurality of platform networks are collected, the cross-platform network data are acquired, the edge weight is dynamically updated according to the delay and the bandwidth, and the optimal transmission path is selected based on the network state, so that the effect of efficiently utilizing network resources is achieved, and the real-time performance and the accuracy of synchronous data transmission are remarkably improved. The to-be-synchronized data is classified and subjected to priority setting, and the data processing sequence is adjusted in combination with the real-time network data, so that accurate scheduling of the synchronization task is realized, the delay and conflict problems in data transmission are reduced, and the efficiency and reliability of data synchronization are improved.

An adapter layer may be used to customize a machine learning component by transforming data flowing into, out of, and / or within the machine learning component. The adapter layer may include a number of neural network components, or “adapters,” configured to perform a transformation on input data. Neural network components may be configured into adapter groups. A router component can, based on the input data, select one or more neural network components for transforming the input data. The input layer may combine the results of any such transformations to yield adapted data. Different adapter groups can include adapters of different complexity (e.g., involving different amounts of computation and / or latency). Thus, the amount of computation or latency added by an adapter layer can be reduced for simpler transformations of the input data.

The invention provides a mobile robot operation efficiency evaluation method and system based on the industrial internet, and relates to the technical field of operation efficiency evaluation, and the method comprises the steps: collecting multi-mode operation data, selecting a transmission channel, uploading the data to an edge calculation node, carrying out the preprocessing, completing the information, and carrying out the filtering operation, generating a first semantic feature and an operation feature descriptor; selecting an optimal transmission path and a service function chain, uploading the operation characteristic descriptor to a cloud server, carrying out real-time analysis on the operation characteristic descriptor, generating an initial operation efficiency evaluation result, carrying out analysis and comparison, determining an efficiency bottleneck, and generating an initial optimization improvement suggestion; sending an initial operation efficiency evaluation result to an edge computing node, performing semantic enhancement and logical reasoning, generating a refined efficiency evaluation report, analyzing an incidence relation and an influence mechanism among different evaluation indexes, determining key efficiency bottleneck factors, generating a comprehensive optimization suggestion, collecting operation data in real time, and generating a comprehensive efficiency evaluation report.

Systems, methods, and computer-readable media for autonomous agents are disclosed. The autonomous agent can include one or more processing circuits configured to receive or identify a dynamic data structure comprising one or more functions or one or more frameworks for performing a plurality of cyber resilience operations. The processing circuits can register the autonomous agent with a decentralized network, centralized network, or data source (DNCNDS). The processing circuits can receive or identify, from at least one computing system external or internal to the DNCNDS, cyber resilience data. The processing circuits can perform, in real-time, the at least one cyber resilience operation of the plurality of cyber resilience operations based at least on selecting at least one function of the one or more functions of the dynamic data structure based at least on the cyber resilience data.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

Bending data is used to facilitate tracking operations of an extended reality (XR) device, such as hand tracking or other object tracking operations. The XR device obtains bending data indicative of bending of the XR device to accommodate a body part of a user wearing the XR device. The XR device determines, based on the bending data, whether to use previously identified biometric data in a tracking operation. A mode of the XR device is selected responsive to determining whether to use the previously identified biometric data. The selected mode is used to initialize the tracking operation. The selected mode may be a first mode in which the previously identified biometric data is used in the tracking operation or a second mode in which the previously identified biometric data is not used in the tracking operation.

A database system is operable to perform a power supply strategy selection function based on first energy utilization-based operation optimizer input data to generate first energy utilization-based power supply strategy data denoting, for each power supply type of a plurality of power supply types, a corresponding first proportion of power supply resources that be implemented as the each power supply type. A first set of power supply resources is selected to execute a first database operation based on the first energy utilization-based power supply strategy data. The first set of power supply resources are utilized to power a first set of computing devices to enable the first set of computing devices to participate in execution of the first database operation in accordance with the first energy utilization-based power supply strategy data.

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 method for training and evaluating a large model in the field of table data analysis. Screening and scoring the obtained table and text data; compiling a plurality of sets of different prompt words, and making'question 'data and'question-answer' data pairs under a table data scene as basic training samples; performing data enhancement on the generated'question 'and'question-answer' sample pairs; collecting and screening common text questions and answers, logic mathematics and code generation task data, selecting a proper data ratio, and performing all-parameter instruction alignment training on the base model; collecting and making an evaluation data set, compiling an evaluation script, and running to obtain a corresponding evaluation index; and iteratively optimizing the training data set according to the evaluation index and the feedback of the evaluation set until the model reaches an expected index. According to the invention, the large model in the field of table data analysis obtained by training not only has strong table question and answer ability, but also has strong general problem solving ability.

The invention relates to the technical field of wind power plants, and provides a wind energy resource high-precision simulation method and system, and the method comprises the steps: obtaining geographic data, wind measurement data and mesoscale meteorological data of a target region; the method comprises the following steps: constructing a terrain model based on geographic data, dividing the terrain model into a plurality of sectors, and generating a wind profile as a boundary condition of each sector by using mesoscale meteorological data; the method comprises the following steps: selecting multiple groups of turbulence models based on mesoscale data and wind measurement data of a selected wind measurement tower, performing directional calculation in combination with boundary conditions, constructing a relationship between the selected wind measurement tower and a target wind measurement tower based on a directional calculation result, obtaining a simulation value of the target wind measurement tower through interpolation, and evaluating an error between the simulation value of the target wind measurement tower and the wind measurement data. Selecting an optimal turbulence model based on the error; and performing comprehensive calculation based on the wind measurement data and a directional calculation result corresponding to the optimal turbulence model to obtain a whole-field wind energy resource. And the wind resource simulation accuracy is improved.

The invention relates to the technical field of industrial automatic detection and computer vision, in particular to a complex workpiece high-precision defect detection method based on a dynamic light source and multi-dimensional detection, which comprises the following steps: performing multi-dimensional data acquisition on a complex workpiece by using high-precision laser scanning equipment; working parameters of the workpiece are adjusted according to characteristics of the workpiece and environmental changes, and the image acquisition process is optimized; performing data preprocessing on three-dimensional coordinate data obtained by laser scanning and texture information obtained by structured light projection; respectively extracting geometric features, texture features and depth defect features of the workpiece from the preprocessed data; acquiring sample data from the database, selecting and optimizing a proper deep learning model, training the model by using the sample data, and constructing a defect detection model; and inputting the data subjected to preprocessing and feature extraction into a defect detection model, and detecting the defect degree of the workpiece.

The invention discloses an engine residual service life prediction method and system based on space-time state selection, and belongs to the field of equipment residual service life prediction. According to the method, multiple technologies such as a time sequence-space double-branch coding and hierarchical fusion mechanism, a selective state space model, a cross-layer residual jump connection and parameter sharing mechanism and an efficient self-attention mechanism are integrated, and an STSSFormer network model is constructed to predict the residual service life of the aero-engine; the system mainly comprises a data selection and preprocessing module, an input sample construction module, a model training module and a residual service life prediction module. According to the method, the feature expression and fusion capability is comprehensively enhanced, and the method has remarkable advantages in the aspects of prediction accuracy of the remaining service life, model robustness and engineering practicability.

The invention discloses a mine digital asset storage management method, which comprises the following steps of: extracting characteristics of mine data, dividing the mine data into hot data, warm data and cold data according to the characteristics, selecting different storage devices for the data, and dynamically adjusting migration of the data among the different storage devices based on access frequency and business requirements; future data storage requirements are predicted through a machine learning algorithm, and storage resources are scheduled; performing full life cycle management on the data, performing business value evaluation on the data, and adjusting storage priority and resource allocation based on the data value; carrying out full-life-cycle encryption on the data, and recording data operation through a block chain technology; energy consumption of the storage equipment is monitored in real time, and storage energy efficiency and carbon footprint management are adjusted. The problems of low storage efficiency, high storage cost and insufficient energy consumption optimization caused by data type diversity, access frequency difference and insufficient storage resource utilization in mine data management can be solved.

Disclosed is a method for analyzing changes in urban economic development characteristics of an urban agglomeration based on nighttime light remote sensing according, including: building a Gross Domestic Product (GDP) spatialization model: spatializing GDP of an urban agglomeration region by using an industry-based modeling approach, modeling spatialization of a primary industry output GDP1 with land use data, and modeling spatialization of a secondary and tertiary industry output GDP23 by selecting an optimal light index on the basis of nighttime light data; measuring an increase or a decrease of a specific variable over time at a pixel level using trend analysis; and modifying a gravity model that reflects an economic linkage strength between cities. The present disclosure can provide data support and methodological basis for the high-quality economic development of the urban agglomeration.

The invention provides a teacher teaching ability analysis method and system based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data in a teaching process of a teacher, wherein the multi-modal data comprises video, audio and text data; and after preprocessing the data, selecting a matched AI model for analysis according to the data, and generating an evaluation result of the teaching ability of the teacher. Evaluation is based on a plurality of first-level indexes, and comprises lesson mark and teaching material interpretation, teaching design implementation, teaching reflection evaluation, technology fusion teaching and teaching effect evaluation. The system integrates and analyzes multi-modal data by using a cross-modal attention mechanism, and maps teaching behavior characteristics to a multi-dimensional evaluation index. And providing personalized improvement suggestions according to the evaluation result, and regularly collecting and updating data to realize dynamic optimization of the teaching scheme. Through big data analysis and multi-modal fusion, teacher teaching ability evaluation is automatically generated, a teaching improvement scheme with high adaptability is provided, and the teacher teaching effect is effectively improved.

The systems and methods disclosed herein generate responses using data retrieved by validating vector embeddings using hash signatures. An output generation request is received via a computing device and includes an input that includes a content set and a command set. The content set includes data chunks, which refers to text, audio, image, and / or video data. A subset of the data chunks that fail to associate with existing hash signatures are selected. Each selected data chunk is validated against predefined constraints that define the operative boundaries of a guideline set and subsequently assigned a unique hash signature to indicate a degree of satisfaction of the data chunk with the guidelines. Using an artificial intelligence (AI) model, a response to the output generation request is generated in accordance with the data chunks, where each data chunk is associated with a corresponding unique hash signature.

The invention discloses an automatic leveling method for intelligently controlling an underwater leveling machine. The method comprises the following steps: acquiring real-time attitude data and working surface flatness requirements of a plurality of execution mechanisms of the underwater leveling machine, and constructing state and operation action information characteristics; an execution mechanism is mapped into an agent with a body, information is shared through an underwater acoustic communication network, and each agent selects and executes a leveling action based on self and neighborhood interaction data; after execution, obtaining a reward value, storing the reward value in an experience pool, sampling and updating the state-action function, and performing iterative training to obtain a multi-body agent reinforcement learning optimization model; continuously monitoring the state of a mechanism when the model runs, dynamically updating the features of an affected area when a fault or hydrodynamic abnormality is identified, and recalculating a leveling strategy to obtain a dynamic re-planning scheme; the scheme is combined with model output to generate a real-time leveling instruction sequence, and the real-time leveling instruction sequence is decomposed and then issued to each execution mechanism, so that self-adaptive accurate leveling control of the underwater leveling machine is realized, and the working efficiency and reliability of a complex underwater environment are effectively improved.

The present application discloses a regional traffic heavy load digital weighing method and synergy system, the weighting method includes: based on data of a physical weighing system available in a region and basic data of a bridge in the region, selecting a reference physical weighing system at a position and a number M and determining a reasonable number N of bridge group transfer layers; based on the reference physical weighing system, constructing a corresponding single digital bridge scale model; obtaining a branch scale network model based on the reference physical weighing system through migration learning and correction; composing a regional digital scale network model based on branch scale network models corresponding to different reference physical weighing systems.

Implementations relate to generating a plurality of training instances to train a generative model in performing tasks using external tool(s) / service(s) accessible via corresponding API(s). The plurality of training instances each includes a synthetic natural language user instruction and execution step(s) to perform a task (e.g., a single-API task or a multi-API task) specified in the synthetic natural language user instruction. The synthetic natural language user instructions selected for the synthetic training data can be generated based on processing textual prompt(s) using a first LLM, where the textual prompt(s) can each include a list of APIs and associated API documents, one or more seed examples, and a request to synthesize a natural language user instruction. The one or more execution steps for a corresponding synthetic natural language user instruction can be generated based on parsing the corresponding synthetic natural language user instruction using the first LLM or a second LLM.

To provide a drawing processing device capable of appropriately selecting a requesting party for processing or estimating a processed product described in a drawing, without depending on a user's knowledge or experience. [Solution] The drawing processing device 2A includes a drawing receiving unit 202 that receives new drawing data D10 related to a processed product, and a requestee selection unit 203A that selects candidate requestees to process or estimate the processed product in the new drawing data D10 based on the new drawing data D10 received by the drawing receiving unit 202. The requestee selection unit 203A inputs the new drawing data D10 to a first learning model 220A to obtain processed product characteristic data D11 for the new drawing data D10, and inputs the processed product characteristic data D11 to a second learning model 220B to obtain processed product request data D12 for the new drawing data D10, and selects the candidate requestees based on the processed product request data D12.

Systems and methods are disclosed for dynamically ranking entities based on comprehensive quality assessment. The method includes collecting a plurality of data from source(s); selecting, based on the plurality of data, procedure(s) and / or condition(s) upon which performance evaluation of entities is based; selecting, based on the plurality of data, entities for which the performance evaluation is conducted; for each of procedure(s) and / or condition(s): determining, using model(s), a performance score for each of the selected entities based on performance indicator(s), wherein the performance indicator(s) include one or more of: risk-adjusted outcome(s), process measure(s), and structural measure(s); and generating a rank for each of the selected entities based on the performance score determined for each of the selected entities; and causing a display of the rank for the selected entities in association with the procedure(s) and / or condition(s) in a user interface of a device.

Systems and methods are disclosed for ranking entities based on performance evaluation with respect to specialties. The method includes collecting a plurality of data associated with entities from source(s); selecting, based on the plurality of data, entities for which performance evaluation is conducted with respect to specialties; for each of the specialties: determining, using one or more models, an overall score for each of the selected entities based on performance score(s) determined for performance component(s), wherein the performance component(s) include: a structural component, a process / expert opinion component, an outcome component, a patient experience component, or a public transparency component, wherein each component includes performance indicator(s); generating a rank for each of the selected entities based on the overall score determined for each of the selected entities; and causing a display of the rank for the selected entities in association with the specialties in a user interface of a device.

The invention relates to the technical field of aerodynamic simulation, in particular to an aircraft unsteady aerodynamic modeling method and system based on a neural network. The method comprises the following steps: establishing sample data about unsteady aerodynamic characteristics of an aircraft, and selecting characteristic parameters from the sample data to form a data set; carrying out normalization on the characteristic parameters; constructing a long-short-term memory neural network model to describe the relationship between the flight conditions and aerodynamic characteristics of aircrafts with different airfoils; adopting a quantum particle swarm optimization algorithm to optimize and adjust hyper-parameters of the long-short term memory neural network model; and based on the optimized hyper-parameters, training the long and short term memory neural network model to obtain an optimized aircraft unsteady aerodynamic model. According to the method, tedious aerodynamic effect decomposition and a traditional mathematical modeling process are avoided, the complete characteristics of the unsteady aerodynamic effect are reserved, the fidelity of the model is improved, and the simulation capability of the aerodynamic model on aerodynamic characteristics is enhanced.

The invention relates to a seabed sand wave and pipeline interaction numerical simulation method and system and a readable medium. The method comprises the following steps: selecting a sub-model under an environment dynamic simulation model according to environment data; selecting a sub-model under the sediment transportation simulation model according to the seabed sediment data; whether a fatigue stability simulation model needs to be carried out is selected according to the pipeline type, if the pipeline is a rigid pipeline, the fatigue stability simulation model is not started, and otherwise, the fatigue stability simulation model is started; starting a sub-model of the protection and treatment simulation model; determining a computational domain of a numerical simulation model, adjusting grid boundary conditions through an environment dynamic simulation model, and combining all selected models to form a final numerical simulation model; and inputting to-be-tested material parameters and environmental parameters into the final numerical simulation model to obtain a seabed sand wave and pipeline interaction numerical simulation result. According to the method, a numerical simulation result which better conforms to the dynamic change process of the sand waves in a real environment can be obtained, and the dynamic change of the seabed sand waves can be better understood and predicted.

The embodiment of the invention provides an enterprise credit data privacy protection method and device based on a zero-knowledge proof technology, and the method comprises the steps: obtaining enterprise credit data, and encrypting the data; adaptively selecting a zero-knowledge proof protocol according to service requirements; generating a pair of public key and private key, and selecting a zero-knowledge proof protocol to generate a zero-knowledge proof based on the private key and the enterprise credit data; transmitting the zero-knowledge proof and the public key thereof to a third party, so that the third party performs verification by using the received zero-knowledge proof and the public key; receiving a verification result fed back by the third party; through the zero-knowledge proof, the enterprise can prove the credit condition without disclosing specific credit data, so that sensitive information and commercial secrets are protected, the risk of data leakage is reduced, in addition, the enterprise does not need to share any sensitive data, does not need to decrypt and share the data, and can complete credit verification only by providing the zero-knowledge proof. And the potential risk caused by data sharing is greatly reduced.

An example operation may include one or more of connecting, by a sparse peer, to a blockchain network of a plurality of peers controlled by orderers, selecting, by the sparse peer, a subset of data to be replicated, specifying, by the sparse peer, data selection filters based on the selected subset of the data, providing, by the sparse peer, the selection filters to the plurality of the peers, receiving, by the sparse peer, blocks matching the selection filters over a gossip protocol from the plurality of the peers, constructing, by the sparse peer, a local ledger of the sparse peer from the received blocks, and bootstrapping the sparse peer based on the local ledger.

The invention relates to a redundancy integrated navigation system, which is characterized in that a modular redundancy integrated navigation system hardware platform is constructed, the redundancy integrated navigation system comprises two navigation modules with identical configuration, and each navigation module mainly comprises an array micro-electro-mechanical inertial measurement unit, a satellite navigation receiver, a magnetometer and a core processor; the array MEMS-IMU adopts a redundancy design, a plurality of MEMS-IMUs form an array, and fused angular velocity and acceleration information is obtained through fault detection and isolation and information fusion; performing fault detection and data selection on the satellite navigation receiver, and performing fault detection and data selection on the magnetometer; each navigation module carries out combined navigation calculation according to the data of the array micro-electro-mechanical inertial measurement unit, the data of the selected satellite navigation receiver and the data of the selected magnetometer; and finally, navigation data output selection is carried out. According to the scheme, system-level reliability and robustness are improved, long-time stable work of the system is guaranteed, and the method can be widely applied to pose measurement in the field of aerospace navigation.