72 results about "Multilevel data" patented technology

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention relates to the technical field of power equipment safety operation and maintenance, in particular to a power distribution network single-phase grounding positioning method and device based on multi-source information fusion. Heterogeneous sensing nodes deployed in a layered and distributed mode are adopted to synchronously collect steady-state and transient-state electrical quantity data of a power distribution network, and preprocessing is carried out to generate a standardized data set; multi-dimensional feature extraction is carried out; carrying out weighted fusion on the extracted multi-dimensional features based on a multi-feature fusion strategy of a dynamic weight adjustment mechanism and a D-S evidence theory, and calculating a line fault probability; when the calculated line fault probability meets a preset threshold value, the position of a fault point is locked through first-stage line screening, second-stage section division and third-stage accurate positioning in sequence. Therefore, through a multi-stage data fusion and hierarchical positioning algorithm, the fault positioning error is obviously improved from the line level in the prior art to the section level, and the fault positioning problem under the scenes of high-resistance grounding, intermittent faults and a complex power distribution network containing a distributed power supply is effectively solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a demand-driven data processing method, device and equipment and a medium, and the method comprises the steps: obtaining demand information, and generating a structured demand analysis report through a demand analysis agent; generating an executable code by combining a strategy conversion agent with a zero code operation platform; generating a test case through the automatic test agent and executing an automatic test; when the test result is wrong, adjustment is carried out through the result feedback agent; when the test result is passed, executing the executable code to generate a processing result, and verifying the intelligent experience certificate by the accuracy; and storing the verified processing result, and performing multi-level data analysis through the data analysis agent to obtain an analysis result. By establishing a closed loop of demand analysis, rule conversion, test verification and data analysis, rapid conversion and accurate verification from the demand to the result are realized, the processing efficiency is improved, and the error risk is reduced.

The invention relates to the technical field of Internet of Things, intelligent monitoring and sensor fusion, and discloses an intelligent multi-sensor data fusion processing system and method, and the system comprises a data collection preprocessing module, a feature and confidence evaluation module, a core data fusion module, a real-time decision early warning module and a performance feedback optimization module. The system preprocesses data through adaptive Kalman filtering, dynamically calculates the confidence coefficient of each sensor by using error entropy, further performs multi-stage data fusion by using weighted fusion and a D-S evidence theory, and finally outputs accurate state estimation and intelligent decision. Meanwhile, internal parameters are adaptively optimized through a closed-loop feedback mechanism. The system effectively solves the problems of data isolation, low precision, slow response and poor adaptability of a traditional sensor system, remarkably improves the precision, reliability, real-time performance and self-adaptability of Internet of Things monitoring, and is suitable for industrial equipment monitoring, smart cities and other scenes.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention discloses an intelligent prediction method for the stirring power consumption of a reaction kettle, and the method comprises the steps: collecting and calibrating the fluid physical property and process operation parameters in the reaction kettle in real time through a high-precision sensor array and an automatic system, carrying out the multi-stage data cleaning, denoising, normalization and time alignment, generating standardized high-quality data, and carrying out the intelligent prediction of the stirring power consumption of the reaction kettle. Based on physical property mapping, dimension reduction and feature fusion, extracting a low-dimensional fluid physical property descriptor, inputting the low-dimensional fluid physical property descriptor into a deep regression network in combination with process parameters, carrying out stirring power consumption modeling and dynamic prediction, and carrying out dynamic prediction by introducing a self-adaptive physical property embedding mechanism and distribution alignment training. The self-adaption and generalization ability of the model to different and unseen fluid physical property distributions is improved, and the accuracy, the stability and the engineering decision support ability of energy consumption prediction under complex working conditions are remarkably enhanced.

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 thermal power plant data management, in particular to a chemical supervision and management method and system based on a thermal power plant, and the system comprises a regular task management module, a chemical test report inputting and editing module, a data auditing module and an abnormity early warning and processing module which are in communication connection in sequence. The regular task management module creates a chemical supervision task according to a thermal power plant technical standard criterion; the chemical test report inputting and editing module adopts a standardized report template to input chemical test report data according to the executed chemical supervision task; the data auditing module is used for regularly auditing the chemical test data report according to a multi-stage data auditing mechanism; and when abnormal data occurs in the chemical test data report, the abnormal early warning and processing module timely gives out early warning, rechecks the abnormal data, carries out data analysis and generates an analysis report, so that the chemical supervision work of the thermal power plant can be efficiently and accurately carried out.

The invention relates to the technical field of industrial intelligent control and predictive maintenance, in particular to a multivariable time sequence prediction and closed-loop control method based on digital twinning and deep learning fusion, which comprises the following steps: constructing a multi-level data architecture based on a digital twinning five-dimensional model; data preprocessing and SMOTE resampling are carried out; time sequence feature extraction based on the 1D-CNN; virtual twin data generation based on Bi-LSTM (Bidirectional Long Short Term Memory) and an attention mechanism; performing multi-mode fault diagnosis based on the RNN-LSTM hybrid network; carrying out adaptive feedback control based on Lyapunov stability; a multi-task learning mechanism is adopted, and virtual twin data generation and fault diagnosis are jointly optimized. According to the method, excellent prediction precision and control performance are verified on a semiconductor etching process data set, and the fault diagnosis effect and the autonomous optimization capability of the system in the complex industrial process are remarkably improved.

This invention discloses a hierarchical public data visualization retrieval system and method based on crowdsourcing analysis and data standardization. The system includes: a crowdsourcing analysis platform for receiving and reviewing feature indicators extracted from public data by expert users; a data standardization module for performing unified exchange rate conversion and reporting period standardization on financial data to ensure data comparability; an indicator calculation module for calculating individual development indicators according to specific formulas; an index generation module for generating individual development indices, regional development indices, and global development indices based on benchmark values, and generating macro-trend charts; a multi-level index database for storing multi-level data and external links; an interactive response module for responding to chart click events and drilling down to obtain detailed data; a data display module for generating the next level of charts; and a context-aware navigation module for automatically navigating to preset external information sources based on user-selected function tags and entity clicks in the charts. This invention lowers the barrier to data utilization through crowdsourcing, ensures the comparability of global data through standardization, and achieves seamless and efficient retrieval and navigation from macro trends to micro information.

This invention relates to a data processing system for CTD (Conductivity, Temperature, Depth) instrument observations, comprising: building a fully domestically developed technology foundation, employing a combination of Spring Boot, MyBatis-Plus, DM database, and Vue3 to achieve domestic adaptation of the front-end, back-end, and database; incrementally accessing and storing raw CTD data files based on the technology foundation, generating dual traceability identifiers of FileInfoID and DataID; performing multi-step sequential processing on the stored data, including zero-error correction, outlier filtering, data smoothing, salt density and acoustic calculation, and standard file generation, with each step's algorithm optimized for the characteristics of domestically produced CTD data; storing raw data, intermediate data, and output data through a multi-level data table design, utilizing traceability identifiers to achieve full-process correlation and traceability, and providing visual display and standardized output. This invention achieves fully domestic processing from data access to output, effectively improving the accuracy and traceability of data processing and ensuring marine data security.

The invention discloses a data consanguinity and hotspot awareness driven global data table analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: reading a data flow log, ETL job metadata and a query dependency relationship of a global data table, and carrying out the analysis to form a multi-level data consanguinity map; log features are extracted to construct a popularity time sequence and a popularity spectrum; fusing the sensitivity spectrum and the popularity spectrum to construct a double-spectrum hotspot sensing model; and inputting real-time data characteristics into the model to obtain a node comprehensive influence score, and performing node strategy correction management. According to the method, the technical problems that the data dependency relationship of a global data platform is difficult to accurately describe, data hotspots cannot be comprehensively sensed, data node influence evaluation is inaccurate and resource allocation and data value are not matched are solved, and the purposes of clear data dependency, comprehensive hotspot sensing, accurate node influence evaluation and reasonable resource allocation are achieved. Therefore, the technical effects of accurate evaluation and efficient management and control of the middle table are achieved.

The application discloses a device state analysis method based on a multi-level data architecture, and relates to the technical field of big data analysis.The method comprises the following steps: collecting device state data from a production control area of an electrochemical energy storage power station; performing protocol analysis, standardized conversion and measurement point mapping on the device state data in a management information area of the electrochemical energy storage power station to generate time sequence measurement point data; unidirectionally transmitting the time sequence measurement point data to a non-control area of the electrochemical energy storage power station through a forward isolation device; writing the time sequence measurement point data into a Kafka cluster for message buffering, and storing the time sequence measurement point data into a TDengine time sequence database by a consumer program; based on a configuration algorithm tool provided by an algorithm platform, performing algorithm modeling and instantiation on the time sequence measurement point data to generate a device state analysis algorithm; and executing an instance of the device state analysis algorithm through an algorithm center to output a device state analysis result.The application can accurately analyze the device state.

The invention provides an unmanned aerial vehicle obstacle avoidance method and system based on airborne sensor and lightning early warning fusion, and the method comprises the steps: obtaining airborne atmospheric electric field data and multi-source heterogeneous data, and carrying out the time-space alignment and multi-stage data fusion, and obtaining calculation parameters; based on the calculation parameters, combining a pre-constructed unmanned aerial vehicle lightning risk assessment model to obtain a lightning risk assessment value; constructing a three-dimensional risk field based on the thunder and lightning risk assessment value in combination with multi-source heterogeneous data; and obtaining an unmanned aerial vehicle route planning path based on the three-dimensional risk field in combination with a path planning algorithm, and completing obstacle avoidance. According to the constructed three-dimensional risk field, invisible thundercloud electric field intensity is quantified and mapped into a'dynamic obstacle 'in a three-dimensional space, so that the unmanned aerial vehicle has the capabilities of detecting and avoiding the thunder and lightning high-risk area, and the limitation that a traditional obstacle avoidance technology (such as a millimeter wave radar, a visual sensor and the like) only can identify an entity obstacle is broken through.

This invention relates to a method for precise localization of breast tumors based on multi-level double-layer coding, comprising the following steps: acquisition of scattered microwave signals; signal processing and feature construction; multi-level data label coding based on a breast model containing the tumor target: each level contains double-layer coding, the first layer of coding is coarse-grained localization, and the second layer of coding is fine-grained localization; the localization interval of the initial level is determined based on the breast model containing the tumor target; given the constraints of the termination coding, the localization interval of the next level is determined based on the current level's localization interval after data label double-layer coding; model training and coding prediction output.

The invention relates to a method and system for predicting the service life of a filling body based on energy constraint fusion deep learning, and belongs to the technical field of health monitoring and service life prediction of a filling body structure, and the method comprises the steps: collecting a transient elastic wave signal of a filling body sample; performing multi-stage data cleaning processing on the transient elastic wave signal; on the basis of the cleaned data, utilizing a physical damage evolution model based on accumulated energy normalization to calculate damage stage characteristics; the multi-scale feature construction module is used for constructing multi-scale features based on the cleaned data; the original waveform sequence features, the multi-scale features and the damage stage features in the sliding window are input into a Transform deep neural network model based on multi-source information fusion, model training is carried out, and a prediction model is obtained; and utilizing the prediction model to predict the residual life of the filling body. The method can accurately predict the residual service life of the filling body structure in real time, and has important theoretical significance and practical application value.

The invention belongs to the technical field of industrial internet and distributed time sequence data management, and discloses an industrial internet distributed power time sequence database. The database is composed of a time sequence data acquisition unit, a dynamic partition storage unit, a node load balance scheduling unit, a multi-stage data consistency maintenance unit, a time sequence perception index construction unit, a data compression and time sequence aggregation unit, an asynchronous data recovery and fault switching unit and a query optimization and result cache unit. According to the method, a dynamic sliding partition algorithm is designed, and real-time partition adjustment of the data in the time dimension is achieved according to the characteristic of continuous high-frequency writing of the power time sequence data; the system can dynamically slide the partition boundary according to the current data writing rate and the node load state, data is prevented from being continuously and intensively written into a certain single area, and node writing inclination and local hot spot phenomena are remarkably reduced.

The present disclosure relates to a molecular memory device comprising a molecular memory unit that includes a first conducting layer, a second conducting layer, and an organic layer interposed between the two conducting layers. The organic layer comprises a compound selected from Formula I, Formula II, their salts, stereoisomers, solvates, or combinations thereof. These compounds exhibit multiple redox states and voltage-dependent geometric isomerism, enabling enhanced electronic switching behavior and multilevel data storage capabilities. The organic materials are sensitive to external stimuli such as voltage, light irradiation, and pH variations, allowing multifunctional integration of sensing, memory, and computing functionalities within a single device architecture. This present disclosure provides a scalable, energy-efficient, and cost-effective platform for advanced memory and neuromorphic computing applications.

The invention discloses an equipment state analysis method based on a multistage data architecture, and relates to the technical field of big data analysis, and the method comprises the steps: collecting equipment state data from a production control region of an electrochemical energy storage power station; performing protocol analysis, standardized conversion and measuring point mapping on the equipment state data in the management information large area of the electrochemical energy storage power station to generate time sequence measuring point data; transmitting time sequence measuring point data to a non-control area of the electrochemical energy storage power station in a one-way manner through a forward isolation device; the time sequence measuring point data are written into a Kafka cluster for message buffering in the non-control area, and the time sequence measuring point data are stored in a TD engine time sequence database by a consumption end program; based on a configuration algorithm tool provided by an algorithm platform, algorithm modeling and instantiation are carried out on the time sequence measuring point data, and an equipment state analysis algorithm is generated; and scheduling and executing an instance of the equipment state analysis algorithm through the algorithm center, and outputting an equipment state analysis result. The equipment state can be accurately analyzed.

The invention relates to the technical field of data processing, and discloses a crowd nutrition data intelligent distribution processing method and system.The crowd nutrition data intelligent distribution processing method comprises the steps that original crowd nutrition data are processed through a multi-level data representation algorithm, the data are mapped in a layered mode, and an interlayer association mapping matrix is constructed; multi-level nutrition data representation is formed; constructing a directed acyclic graph model with a causal relationship by adopting a structured causal model construction algorithm; an anti-fact scene generation and simulation algorithm is utilized to generate and evaluate the effects of various possible nutrition intervention schemes, an intervention combination is constructed, and anti-fact reasoning is carried out; constructing and solving a differential equation system for describing the long-term influence of nutrition intervention; adopting a heterogeneity analysis and intervention optimization algorithm to identify heterogeneity characteristics in the crowd and optimize a personalized intervention strategy; through the structured causal model and strict confounding factor control, the accuracy of nutrition intervention effect evaluation is improved.

The invention belongs to the technical field of breast cancer molecular typing, and more specifically relates to a mammary gland ultrasound video HER2 expression state identification method based on spatial-temporal feature interaction. The method comprises the following steps: taking an original breast ultrasonic video as input data, performing multi-stage data preprocessing operation, and then performing data balance on training data to obtain a standardized training set; sequentially constructing a UniFormerV2 network-based feature module, a multi-stage feature fusion module and a classification module, and training to obtain a trained model; and inputting the test set into the model to obtain a final prediction result of the HER2 expression state. The problems that an existing HER2 detection method is invasive in operation and cannot perform dynamic monitoring, and an existing deep learning method based on static images neglects space-time dynamic information in an ultrasonic video, so that prediction accuracy of the HER2 expression state, especially HER2-Low subtype is insufficient are solved.

The invention discloses a multi-level data selection method and related equipment, solves the problems of performance bottleneck and code redundancy caused by tedious condition judgment of an application layer and frequent database interaction in a traditional scheme, unifies implementation logic of first-time selection and bit-complementing selection, greatly reduces development and maintenance complexity, and improves the efficiency of data selection. And multi-condition hierarchical selection requirements are integrated through a single-statement SQL, and finally high efficiency and maintainability of the selection process are realized.

The application discloses a drilling process optimization method and system based on digital twinning, relates to the technical field of digital twinning, establishes respective infinite data chains corresponding to respective sensors, deploys multistage data transmission relay nodes on the paths from the respective sensors to data receiving ends, performs preprocessing and buffering on the sensor data that arrives in advance at respective transmission relay nodes, waits for preservation in the case of incomplete data, stores the sensor data of future time nodes received during the preservation waiting period into a secondary cache queue according to time nodes, performs compression preservation on overflow data based on the cache residual amount and the waiting time length, and aligns and injects data according to the time axis of the drilling digital twinning model when it is detected that the downhole sensor data of the same time node all arrive at the transmission relay nodes. The application realizes accurate synchronization and alignment of multichannel drilling data on the time axis of the digital twinning model.

The invention relates to a sugarcane seed bud quality detection method, which comprises the following steps of: acquiring sugarcane segment transmission video data which is the same as a sugarcane distributed mechanical harvesting process environment, converting the video data into static image frames, and marking pictures and dividing a sugarcane image data set; processing the sugarcane image data set by adopting an undersampling strategy and a multi-level data enhancement strategy; a backbone network, a neck network and a detection head of the YOLOv11 model are improved to adapt to sugarcane seed bud detection; and detecting the quality of the sugarcane seed buds through the trained YOLOv11 model. According to the scheme provided by the invention, the problems that the sugarcane bud target is small, the posture is changeable and the sugarcane bud target is easily interfered under a complex background are effectively solved, the detection precision, robustness and generalization ability are improved, and multi-target accurate detection of the sugarcane bud is realized.

The invention relates to the technical field of ground surface settlement monitoring, and discloses a ground surface real-time monitoring system based on a shield construction tunnel. A data acquisition unit of the system obtains a displacement measurement value from an earth surface sensor and generates time sequence data; the sequence segmentation unit analyzes mutation points in the sequence and segments the sequence into a plurality of data segments according to the mutation points; an exception marking unit performs statistical test on each data segment to identify an exception data segment set; the trend evaluation unit performs trend line fitting on the abnormal set and evaluates the fluctuation degree of fitting deviation; the matrix conversion unit organizes the time sequence data into a specific matrix structure and performs matrix decomposition; a feature derivation unit derives feature parameters from the decomposition result; and the monitoring control unit adaptively adjusts a monitoring strategy according to the characteristic parameters and generates a settlement report. According to the system, accurate and self-adaptive monitoring and early warning of ground surface settlement are realized through multi-stage data processing and intelligent analysis.

The application provides an enterprise financial comprehensive management system based on intelligent technology, and relates to the technical field of financial management.The system comprises: an accounting intelligent subsystem for obtaining financial data, performing semantic analysis and conversion on the financial data through a preset rule and a multistage data mapping mechanism, generating standardized accounting business instructions and issuing the instructions to an automatic process engine, so as to automatically generate financial documents in accordance with accounting standards; a financial management subsystem for integrally realizing the execution of a to-do list management, the query of a compliance guidebook and the structured storage of financial data according to a preset standardized process; and a business service subsystem for providing tax-related business services, asset management services, fund management services and operation management service processing and guidance for enterprises through modular design.The application aims to improve operation efficiency, reduce error rate, break information islands, realize effective integration and utilization of resources and provide strong support for the decision-making of enterprises.

The invention relates to an aircraft component test report analysis system based on an anomaly inference model, and relates to the technical field of component testing, and the aircraft component test report analysis system comprises a data analysis module which is configured with a file format analysis strategy to perform file format feature extraction on a test file, carrying out aircraft part test information analysis on the test file by matching a corresponding analysis step in the format analysis database so as to generate a test report; the file caching module is configured with a multi-stage data caching strategy, reads, writes and pre-caches the test file through a pre-constructed memory, file and database caching architecture, and keeps the data read-write efficiency and the system response speed within a preset good threshold range; and the exception analysis module is configured with an exception report identification model and is used for identifying and analyzing the test report through similar fault identification logic and a fault analysis engine so as to determine exception test information in the test report. The method has the effect of improving the analysis efficiency and accuracy of the test report of the aircraft component.

The application provides an encryption access control method and platform for industrial safety data, and relates to the technical field of data encryption access. The method comprises the following steps: performing attribute classification and coding identification on an industrial system data set; performing safety level division on the industrial system data set according to a data safety quantification index system; performing matching analysis on the multi-level industrial safety data set based on a data encryption algorithm list; performing encrypted storage on the industrial system data set based on the multi-level data encryption algorithm and the industrial data identification code set to generate an industrial encrypted data warehouse; establishing a fine-grained access mechanism and a decryption interaction mechanism according to a user role library; and performing access permission allocation and encryption access control on the industrial encrypted data warehouse based on the fine-grained access mechanism and the decryption interaction mechanism. The application can solve the technical problem that the existing technology cannot meet the comprehensive requirements of high safety, high precision and high flexibility in modern industrial systems.

The application provides a low-power wireless self-organizing network dynamic configuration method and system based on adaptive routing, relates to the technical field of network protocols, and comprises the following steps: acquiring node working state parameters, calculating an energy level value to determine a data transmission path, and setting a multi-level data cache occupation threshold; calculating a network load distribution state, and differentially scheduling nodes; selecting a low-power forwarding channel based on node routing applicability; and monitoring a network state in real time and updating parameters. The application improves network adaptability, reduces communication power consumption, prolongs the network life cycle, and improves data transmission efficiency.

This invention belongs to the interdisciplinary field of artificial intelligence and information security, and provides a multi-level secure RAG retrieval method, system, device, and storage medium to address the risk of leakage of security management data during the retrieval process. The method includes acquiring user-input query content, user security level, and user business scope; converting the user security level and user business scope into scalar filtering conditions recognizable by a vector database; executing a hybrid retrieval instruction in the vector database, wherein the hybrid retrieval instruction includes a query vector and the scalar filtering conditions, to prune vector data that does not meet permissions during the indexing scan phase; calculating the similarity between the query vector and the remaining document vectors based on the pruned logical subspace, and returning the retrieval results. This invention is applicable to RAG systems in a multi-level data hierarchical isolation environment and can reduce the risk of leakage of security management data during the retrieval process.