531 results about "Smart data" patented technology

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

Techniques include generating, by an artificial intelligence agent, a request for artificial intelligence data. The techniques further include presenting the request via a user interface. The techniques further include receiving the artificial intelligence data. The techniques further include providing the artificial intelligence data to the artificial intelligence agent for at least one of training or inference.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

The invention relates to an optical data network (10-1) for connecting a plurality of data network nodes (N1-1, N2-1, N3-1, N4-1). The optical data network (10-1) comprises: i) the plurality of data network nodes (N1-1, N2-1, N3-1, N4-1), the data (99) comprising data packets (99p), wherein each data network node (N1-1, N2-1, N3-1, N4-1) is provided with a network interface (Nl); and ii) a data connection network (100-1) comprising at least one data switch (SW) and a plurality of optical connections (50) for allowing establishment of data links (DL1, DL2) between pairs of said plurality of data network nodes (N1-1, N2-1, N3-1, N4-1). At least one of the at least one data switch (SW) is a smart data switch (SSW) comprising at least one packet switch (PS, EPS) and at least one circuit switch (CS, OCS), At least two network interfaces are smart network interfaces (SN, SNC) comprising a data classifier (25) configured for classifying the data packets (99p) into at least two traffic classes including first traffic class (D1) and a second traffic class (D2) in accordance with a classification scheme. The smart data switch (SSW) and the network interfaces (SN, SNC) are configured to route the data packets (99p) of the first traffic class (D1) to the packet switch (PS, EPS) and to route the data packets (99p) of the second traffic class (D2) to the circuit switch (CS, OCS).

Provided are a driving state monitoring and feedback method and system based on multi-modal human-factor intelligent data analysis, and an edge computing terminal device. The method includes: receiving multi-modal human-factor data collected in real time from a tested driver; preprocessing the multi-modal human-factor data; inputting the preprocessed multi-modal human-factor data to a pre-trained first state identification model to obtain a driver state identified in real-time, the driver state including a normal state and a plurality of abnormal states; and generating, in response to the driver state being identified as an abnormal state, a driving state feedback instruction for the category of the abnormal state and sending the driving state feedback instruction to a driving intervention system, to cause the driving intervention system to perform state feedback adjustment on the driver based on the received driving state feedback instruction.

The invention belongs to the technical field of intelligent document processing, and discloses an intelligent data processing method for collected files based on artificial intelligence, which comprises the following steps: acquiring a multi-source multi-version file to carry out layout rectification and OCR (Optical Character Recognition) to obtain a page fragment object set; using a controlled decoding language model to extract clause records according to preset Schema to form a clause record set; performing cross-version alignment on homonymous or synonymous terms, and constructing a time sequence knowledge graph; compiling the cross-document consistency rule into an executable rule set which can be evaluated on the time sequence knowledge graph by adopting an executable collection constraint language; evaluating the scope of action of each rule to generate conflict candidates; and calculating a minimum hit set to obtain a minimum conflict interpretation set, positioning a root cause term instance, and generating a minimum repair suggestion according to a normalization result of the operator and the node violating the rule. According to the method, the unification of clauses and calibers and the automatic judgment of the latest effective state, the accurate positioning of conflicts and the minimum cost repair are realized.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.

The invention relates to the technical field of biological process intelligent control, and discloses a pomegranate leaf fermentation process optimization and intelligent control system based on digital twinning. According to the system, the Internet of Things and a visual sensor are integrated through a multi-source data sensing module, so that synchronous acquisition and fusion of temperature, pH, dissolved oxygen, flora density and image data are realized; the intelligent data preprocessing module extracts high-quality features by adopting a self-adaptive filtering technology; the digital twin simulation engine module drives the virtual model to dynamically update based on real-time data; the deep prediction module accurately predicts the fermentation trend through a deep learning algorithm; the intelligent diagnosis module realizes abnormal root cause analysis by using a knowledge graph; the reinforcement learning optimization module autonomously generates an optimal control strategy based on the diagnosis result; and the self-adaptive execution module accurately regulates and controls fermentation parameters. According to the system, multi-dimensional perception, high-precision prediction and adaptive optimization control of the fermentation process are realized, and the accuracy and stability of process control are effectively improved.

Systems and methods for intelligent data sanitization employing PCM and AI / ML are provided. The idea uses AI / ML to detect specific facts that needs sanitization rather than full properties in incoming records. Data sanitization is optimized using this focused method, saving computational resources. To properly manage changing data volumes, PCM shifts between Logical 0 and Logical 1 states. Logical 0 processes smaller volumes with high resistance and low conductivity, while Logical 1 processes large volumes with low resistance and high conductivity. The AI / ML module organizes and directs data to maximize resource and processing efficiency. The PCM processes data in-memory and directly overwrites, eliminating erasure. AI / ML and PCM integrate to sanitize data quickly, efficiently, and securely, improving system performance and data integrity without a central repository. The system dynamically adjusts to changing data patterns, protecting and optimizing data.

The invention discloses an intelligent data center energy scheduling optimization method based on machine learning. The method comprises the following steps: acquiring data to form a multivariable time sequence; the improved PatchTST model calls a multi-level memory interaction structure to establish short-term memory and long-term memory, and an attention constraint energy consumption memory module is introduced to output prediction features; inputting the prediction features into a decoding layer of the improved PatchTST model; constructing an energy scheduling multi-objective optimization problem, and generating an equipment control instruction set by adopting an NSGA-III algorithm; collecting an actual execution result to form feedback data; performing deviation evaluation and strategy updating on feedback data through an energy self-feedback correction process; and judging whether the energy consumption prediction error and the energy scheduling deviation meet a preset termination condition or not. The method is suitable for intelligent energy scheduling and operation efficiency optimization in multi-energy coupling scenes such as an intelligent data center.

The invention discloses an Internet of Things cluster data analysis method based on big data and artificial intelligence, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the initialization and parameter synchronization processing of an edge side feature coding sub-model and a cloud space-time depth model through a space-time diagram sequence, obtaining a collaborative modeling parameter set, and obtaining a collaborative modeling parameter set; carrying out self-supervised reconstruction and prediction task training on the cloud space-time depth model based on the collaborative modeling parameter set, obtaining a depth representation model, learning abnormal mode parameters by using the depth representation model, constructing an abnormal scoring function, obtaining an abnormal detection model, and after receiving real-time multi-source heterogeneous data at an edge node, carrying out real-time reconstruction and prediction task training on the cloud space-time depth model; performing state prediction and anomaly score calculation by using the anomaly detection model to obtain a real-time anomaly score result; through the intelligent data analysis method, the accuracy, the real-time performance and the interpretability of Internet of Things cluster data analysis are improved.

The invention relates to the technical field of data processing and analysis, in particular to a data interaction method and system based on an intelligent dataset, and the method comprises the steps: capturing an interaction task demand and context through multi-source heterogeneous data fusion and dynamic feature construction, and providing a basis for subsequent processing; a load cloud picture is generated based on cognitive domain division and a graph attention network, and a high-load area and a conflict focus set are recognized in real time; through causal analysis and diffusion coefficient quantification, a conflict propagation path and influence intensity are clarified so as to prevent and cope with conflict diffusion in advance; through combination of load migration trajectory and diffusion coefficient simulation deduction, a conflict generalization trend is predicted, and conflict early warning is realized. Key decision points are positioned through map fusion, and resource allocation is optimized; by triggering adaptive disambiguation and intention reconstruction, contradiction of high-conflict tasks is dynamically reduced, interaction efficiency and accuracy are improved, conflicts are effectively eliminated, and smoothness and stability of data interaction are guaranteed.

The invention relates to the technical field of bridge health monitoring, and discloses a bridge health monitoring system and method and a storage medium. According to the method, a multi-modal monitoring original data flow of a bridge is collected through a distributed sensing network, intelligent data purification and heterogeneous data integration are performed on data, and a bridge health data set in a unified format is generated. And decoding a time sequence mark, a spatial position code and a monitoring parameter dimension identifier from the data set. And dividing the data into overlapped time slices according to the time sequence marks, and generating a time slice data set. And fusing spatial position codes, mapping spatial information in the time slice data set to a bridge global coordinate framework, and establishing a dynamic health state map. And based on the atlas and the time slice data set, performing multi-dimensional data fusion according to the monitoring parameter dimension identifier. And analyzing a state evolution path of the bridge member through a fusion result, and generating a health measurement and prediction sequence.

The invention relates to the technical field of intelligent wearable equipment, and provides a personal health service system based on an intelligent watch digital human, the system comprises an intelligent data analysis platform module comprising an intelligent watch end and a cloud end, the intelligent watch end comprises a digital human interaction module and a multi-source data acquisition module, the digital human interaction module constructs a multi-mode interaction mechanism based on a virtual digital human, obtains user health related information, receives a user instruction and outputs a health analysis result; the multi-source data acquisition module acquires physiological feature data and behavior activity data of a user in real time, and performs dynamic alignment and consistency verification of multi-dimensional data through a space-time association algorithm; and the intelligent data analysis platform module adopts a hierarchical collaborative architecture of edge computing and cloud deep mining to intelligently analyze and process the collected data and generate a personalized health assessment report and a dynamic intervention suggestion, so that the digital human interaction experience can be optimized, and the personalization, intelligence and naturalization of health management can be realized.

This disclosure describes systems, software, and computer implemented methods for taking a user's natural language query, using a generative AI model to produce an embedding of that query, and then comparing that query embedding to a database of embeddings generated from metadata and data set descriptions of the datasets in the datastore. This database of embeddings includes both the embedding vectors, and a metadata object describing each data set in the datastore and is uniquely generated to enable efficient search application. Once comparison results are determined, the closest matching datasets to the user query can be provided to the AI model for a summarization of their contents, before being returned to the user as search results.

The invention discloses a forest resource monitoring system based on unmanned aerial vehicle remote sensing, particularly relates to the technical field of forest resource monitoring, and particularly relates to a forest resource monitoring system based on unmanned aerial vehicle remote sensing. The system comprises an unmanned aerial vehicle platform, a remote sensing sensor carried on the unmanned aerial vehicle platform, a data acquisition module, a data processing module and an output module. The core structure of the system further comprises an intelligent data fusion module and a self-adaptive path planning module. The intelligent data fusion module integrates the multi-source remote sensing data through a feature extraction and weight distribution method to generate a unified data set; and the adaptive path planning module dynamically adjusts the flight path according to the real-time environment data to realize coverage optimization and obstacle avoidance functions, so that the data processing efficiency and path planning adaptability of forest resource monitoring are improved.

The invention discloses an intelligent data calling method based on an MCP protocol, and the method comprises the steps: carrying out the intention recognition and key entity extraction in response to a natural language request of a user, and obtaining a plurality of user intentions and at least one key entity; judging based on the confidence coefficient of the key entity, and screening the user intentions according to the confidence coefficient of the user intentions to obtain an execution sequence containing a plurality of subtasks; and determining at least one target data source tool from a plurality of data source tools packaged based on the MCP protocol according to the description of the subtasks one by one so as to perform data query based on the key entity, obtain a query result and generate a final response. According to the method, the system decision reliability can be improved, the tool calling intelligence is enhanced, the process automation is realized, and the two major problems of low task planning quality and inaccurate tool calling are solved.

According to the method, PCIe extended storage equipment is identified by responding to a system starting signal, and initialization and address mapping control logic is executed to construct a first address space addressable by a GPU (Graphics Processing Unit); then, loading model data according to an AI task instruction, and dynamically dividing hot, warm and cold multi-level data popularity levels by monitoring memory access frequency; a migration data block is generated based on the popularity level, a migration data flow is organized and verified through priority scheduling logic, and intelligent data scheduling between extended storage and a GPU video memory is achieved; finally, the resources are released after the task is completed. Therefore, dynamic expansion of the physical video memory capacity is effectively achieved, large-scale AI model localization training is met, dependence on a high-end GPU chip is remarkably reduced, and data safety and system economy are improved.

The invention relates to the technical field of data processing, and discloses a multi-terminal collaborative office data intelligent synchronization and privacy protection method, and the key points of the technical scheme are as follows: system initialization and node registration, intelligent data synchronization based on edge-federated learning, chaotic encryption and key negotiation, dynamic differential privacy protection, and block chain storage and lightweight consensus. An edge-block chain-cloud three-level architecture is adopted, federated learning, chaotic encryption, dynamic differential privacy and a DQN-Raft + consensus technology are integrated, and efficient intelligent synchronization and full-life-cycle privacy protection of multi-terminal office data are realized through full-process association of core parameters such as identity verification parameters, terminal weights and model errors. And meanwhile, the heterogeneous adaptation complexity of the system is reduced.

The invention relates to the technical field of data security, and discloses an intelligent data security compliance risk assessment method based on a structured knowledge base, which comprises the following steps of: 1, constructing a dynamic knowledge base, namely performing structured processing on an external strategy file to form assessment key points, executing state locking management on the formed evaluation key points; and step 2, screening and combining evaluation key points in the constructed dynamic knowledge base, and generating a customized inspection scheme containing specific inspection items. According to the technical scheme, a multi-source heterogeneous external strategy file is subjected to structured processing to construct an evaluation key point knowledge base containing a state locking management and dynamic updating mechanism, so that the technical effect of unifying, standardizing and dynamic scattered evaluation basis is achieved; compared with the technical scheme of scattered storage, different structures and manual tracking policy updating in the prior art, the method solves the defects of incomplete assessment reference content coverage and updating lagging.

The invention discloses an intelligent data question and answer method and system based on index semantic enhancement, and relates to the technical field of intelligent data question and answer. The method comprises the following steps: acquiring a data analysis demand in a natural language form; the method comprises the following steps: performing information extraction on a data analysis demand by utilizing an index semantic enhanced large language model, generating a query statement according to the extracted information, recalling and rearranging most relevant indexes and elements in an index semantic knowledge base by utilizing a knowledge retrieval enhancement layer according to the data analysis demand and a historical session on the basis of mixed retrieval, and generating a query statement according to the query statement. Generating a prompt template according to a retrieval result of the index semantic injection layer by utilizing the index semantic injection layer, and performing semantic understanding reasoning according to the prompt template to obtain corresponding information; and checking compliance according to the query statement, and returning a query result. The method aims at achieving efficient mapping, dynamic modeling, intelligent SQL generation and optimization, authority safety management and control and multi-round intelligent interaction of natural languages and enterprise-level index assets.

The invention relates to the technical field of slope monitoring and early warning, in particular to a slope radar data VPN private network transmission and remote early warning system which comprises a slope radar monitoring module, an intelligent data processing module, a VPN transmission module based on value priority and a B / S cloud early warning platform. The intelligent data processing module realizes multi-source heterogeneous data alignment and unified analysis through a dynamic protocol feature self-learning and cross-protocol conflict resolution technology; and meanwhile, a data value density model is constructed, and the deformation rate, the geological condition and other multi-dimensional factors are integrated to quantify the data value. The data is graded based on the value density, the VPN transmission module is matched with the link quality grade to realize differential transmission, and the B / S cloud early warning platform completes hierarchical storage and multi-dimensional early warning. The system solves the problems that multi-source data analysis is high in compatibility difficulty and high-value data transmission lags behind, and efficient disposal of slope risk data is guaranteed.

The invention provides an intelligent agent training method of an intelligent calculation data center and an air conditioner control method, relates to the technical field of intelligent calculation data centers, and is used for optimizing an air conditioner control scheme of the intelligent calculation data center. The method comprises the steps that air conditioner historical operation data and computing power cabinet historical operation data of all cooling areas are obtained; based on the historical operation data of the air conditioner and the historical operation data of the computing power cabinet of each cooling area, cooperatively training a first intelligent agent of each cooling area, a second intelligent agent of each machine room and a third intelligent agent of an intelligent computing data center; wherein the first intelligent agent is used for being responsible for controlling the air conditioner of the corresponding cold supply area; the second intelligent agent is used for balancing the temperature of each cold supply area in the corresponding machine room; the third intelligent center is used for balancing chilled water header pipe return water temperature of each machine room; the first agent, the second agent and the third agent are constructed based on a reinforcement learning model.

The invention discloses a data identification method and system based on data high-dimensional feature deconstruction. The method comprises the following steps: decoupling multi-modal time series data into independent implicit factors with clear physical meanings through a domain knowledge constrained depth generation model; based on the implicit factor sequence, using time sequence causal discovery and anti-fact intervention to construct a causal graph and generate a causal effect vector; constructing a differentiable identification strategy network taking the implicit factor and the causal effect as input, and optimizing system meta parameters through a meta learner according to feedback; robustness loss feedback optimization is generated through causal consistency verification and abnormal injection, meanwhile, a structured identification graph is output, and an incremental model library is established. The system correspondingly comprises four function modules. According to the method, the problems that the features cannot be explained, the causal mechanism is missing and the model adaptability is insufficient are solved, and intelligent data identification which can be explained and is robust and has the sustainable evolution capacity is achieved.

The invention relates to the technical field of drainage management, in particular to an intelligent data analysis system for drainage management, which is characterized in that full-chain data from a source, a permeable area, underground water and a tail end are comprehensively acquired through a drainage acquisition module, and a comprehensive water level capable of reflecting the overall load state of the system is calculated through a weighted fusion formula; according to the evaluation method based on the curve form similarity, a comprehensive water level curve is drawn, and the comprehensive water level curve is compared with an expected drainage curve, so that the system can objectively give a conclusion that drainage management is qualified or unqualified in a rainfall event. Compared with single-point over-limit alarm, the dynamic response capability of the system in the time dimension can be reflected more comprehensively, management evaluation is standardized and scientific, and intelligent, accurate and robust self-calibration of core capability parameters of the drainage system is achieved by establishing a dynamic proportional adjustment mechanism based on a preset integral difference value.

The invention discloses an electronic archive intelligent data management method and system based on a large model. The method comprises the following steps: firstly, acquiring an electronic archive file, performing unified access, identifying a text through an OCR (Optical Character Recognition) technology, and reserving original layout coordinate information; analyzing the document page by page to generate a page-level structured object containing text and position mapping; then, on the basis of a preset domain prompt template, calling a large language model to extract file metadata from the page-level object, recognizing entities and relationships in combination with file knowledge ontology, and generating a semantic triple containing an evidence source page number; and finally, fusing the metadata and the triple to construct an electronic archive knowledge graph with a page-level traceability function. According to the invention, through a whole-process semantic governance mechanism from access, analysis to knowledge generation, the problems of difficult analysis of unstructured data, weak semantic understanding and lack of traceability in traditional archive management are effectively solved, and the intelligent level and data credibility of archive governance are significantly improved.

The invention discloses a paralytic nursing intelligent early warning system and method based on the Internet of Things technology. The system comprises a multi-modal physiological parameter acquisition module, an edge calculation preprocessing unit, an intelligent data transmission module, a multi-scale time sequence feature extraction module, a space-time diagram convolutional network module, a cross-modal attention fusion module, a multi-task risk prediction module and a model training and optimization module. According to the system, multi-mode data such as electrocardio, blood pressure, blood oxygen, eye movement tracks and voice are collected, preprocessed at an edge end and then transmitted to a cloud end; a multi-scale convolutional network is adopted to extract time sequence features, parameter association is modeled through space-time diagram convolution, cross-modal data fusion is realized by using an attention mechanism, and finally risk classification, anomaly detection and trend prediction are completed through a multi-task network. The early-stage, accurate and explainable early warning of the stroke risk is realized, and the early warning accuracy and clinical practicability are remarkably improved.

The invention aims to provide an intelligent data analysis multi-round reasoning method. The method comprises the following steps: receiving natural language query request data; performing entity recognition and intention recognition on the natural language query request data; performing complexity quantitative analysis on the natural language query request data by adopting complexity measurement evaluation to obtain a complexity evaluation result; constructing five types of Agents to carry out collaborative reasoning; a standardized cooperation interface and a data transmission protocol among the Agents are established; the accuracy and the reliability of a preliminary reasoning result are ensured through a cooperative conflict resolution mechanism; and sorting any preliminary retrieval result according to the evaluation data, and outputting a final retrieval result. According to the invention, intelligent processing of complex BI data analysis tasks is realized, and an efficient, reliable and intelligent technical solution is provided for enterprise data analysis and business intelligent application.

A method for making gyroscopic azimuth measurements includes estimating a pitch angle and a roll angle of a gyroscopic surveying tool in a wellbore; determining a measurement duration for each of a plurality of gyroscope measurements from the estimated pitch and roll angles; making each of the plurality of gyroscope measurements at the determined measurement duration when the gyroscope is disposed at a corresponding plurality of rotational positions in the tool housing; and computing an azimuth of the wellbore from the plurality of gyroscope measurements.

The invention belongs to the field of fault prediction, and particularly discloses a computer hard disk fault prediction method based on big data analysis, which comprises the steps of data collection, data preprocessing, fault label definition, data set reconstruction and hard disk fault prediction. According to the scheme, the KM survival model is constructed, the life stages of the hard disk are divided based on the KM survival curve, high-quality samples which are sufficient in use time, continuous and complete in data and few in parameter loss are screened out, redundancy removal, noise reduction and purification of SMART data are achieved, and interference of noise to the model is reduced from the source; a double-layer loss function of weighted cross entropy loss and improved focus loss is designed, the weight is dynamically calculated based on the total number of positive and negative samples, the class imbalance problem is preliminarily balanced, then the loss proportion of samples easy to classify is reduced, the loss distribution of the positive and negative samples is adjusted, and the loss weight of samples difficult to classify is greatly improved.