18results about How to "Improve data utilization" patented technology

The invention discloses a drilling pump pressure prediction method based on an artificial neural network, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, constructing a neural network model; s4, model training and optimization; and S5, model verification and deployment. According to the method, multi-channel data fusion and time synchronization optimization are innovatively adopted, so that the data quality and consistency are improved; in combination with a deep neural network and a self-adaptive optimization strategy, the precision and generalization ability of pump pressure prediction of the drilling pump are improved; and an online updating mechanism is introduced, so that the model can be dynamically optimized according to real-time data, the defects of low prediction precision, poor adaptability and difficulty in real-time updating of a traditional method are overcome, and an efficient and reliable prediction means is provided for intelligent drilling control.

The application relates to the field of fault detection, and discloses a device fault detection method based on small sample learning and acoustic feature migration, a device and a medium, the method comprising the following steps: acquiring an acoustic signal of a target device; extracting a fusion feature vector; generating device-independent features based on a DANN network; generating a synthesized fault acoustic signal by using a Mel-CGAN; mixing the synthesized fault acoustic signal with an actual acoustic signal, and then training the DANN; and obtaining a fault detection result by using the trained DANN. The application can significantly reduce the dependence of fault diagnosis on labeled data, and can reduce the accuracy in a cross-device scene. The application can be widely applied in the fields of wind power and intelligent manufacturing.

The application provides a ship-vehicle-person virtual image establishment method and system, which comprises the following steps: obtaining ship, personnel and vehicle data as source data for preprocessing, calculating and mining labels according to the preprocessed source data, and producing ship, personnel and vehicle labels; defining hierarchical labels; defining group labels; extracting basic attributes of the ship, personnel and vehicle; defining personalized labels, adding personalized labels to the ship, personnel and vehicle according to their basic attributes and respective source data; defining correlation relationship labels among the ship, personnel and vehicle; creating a label table in a database, adding the calculated labels into the label table, and then performing correlation analysis on the respective source data of the ship, personnel and vehicle and the label table according to business requirements to obtain index data; and providing the index data to a ship-vehicle-person virtual image system for drawing charts. The application can improve traffic operation efficiency, safety and risk prediction.

The application provides a traditional Chinese medicinal material hyperspectral anomaly detection classification method and system and a computer device, relates to the traditional Chinese medicinal material quality inspection field, and the method comprises the following steps: reading hyperspectral data, performing foreground detection on the hyperspectral data by adopting a multiscale spatial spectrum fusion method to obtain an abnormal area foreground mask; collecting N three-dimensional Patch arrays of the foreground mask area through three-dimensional Patch automatic sampling; setting a three-dimensional convolutional neural network of a wave band self-attention mechanism, inputting the foreground mask and the three-dimensional Patch arrays into the neural network to generate a high-level semantic feature vector; and finally outputting a classification result through a neighborhood spatial spectrum self-supervision aggregation attention module of the high-level semantic feature vector. The traditional Chinese medicinal material hyperspectral anomaly detection classification method, system and computer device are used, a new efficient, accurate and automatic traditional Chinese medicinal material mildew and insect damage detection method is realized, and the detection quality and the practical value of the system are significantly improved.

The invention provides a civil aircraft configuration management method based on a data statistics and analysis technology, and relates to the technical field of civil aircraft data management and application, and the method comprises the following steps: S1, data extraction: extracting metadata from structured data and unstructured data in different business systems through ETL to obtain meta-incremental data; and S2, data storage: processing the meta-incremental data, and synchronously storing the processed meta-incremental data to a source pasting layer to realize integration and backup storage of the meta-incremental data of different data sources. Through integration of metadata of different sources, the data utilization rate is improved, through a data source pasting layer, full-link tracing influenced by configuration change and configurable billboard design of a visualization layer can be achieved, a configuration management user can generate an analysis view according to needs by himself / herself, increment synchronization only updates change data, redundant storage is reduced by 60% compared with full-amount copying, and the system reliability is improved. And minute-level rapid generation of configuration management related lists and reports can be realized.

The invention provides a remote sensing image analysis method and device, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: receiving multi-source multi-temporal remote sensing image data, and constructing a space-time spectrum multi-dimensional data cube based on a space grid engine; target grid data is dynamically scheduled according to a user request, real-time processing such as dynamic stretching, wave band combination and light and color uniformity is carried out, and the target grid data is published in a map service form. The device comprises a data receiving module, a scheduling module, a dynamic processing module and a service publishing module. Through gridding scheduling, dynamic processing and micro-service architecture, the slicing-free, multi-specification and high-concurrency online service of the remote sensing image is realized, the data processing efficiency, the storage utilization rate and the service stability are remarkably improved, and the method is suitable for real-time analysis and application of mass remote sensing data.

The invention relates to a WEEE recycling supply chain inventory optimization method based on evolutionary reinforcement learning, and belongs to the technical field of supply chain management and artificial intelligence. According to the method, a multi-level inventory cooperative control model is constructed, an inventory optimization problem is modeled as a Markov decision process, and an evolution reinforcement learning algorithm is utilized to train an intelligent agent to learn an optimal strategy. Core innovations include design state space capture inventory dynamics, action space processing continuation to discrete decision mapping, and cost-based minimization of reward functions. According to the algorithm, a population evolution mechanism and experience playback optimization are fused, and the learning efficiency and stability are improved through parallel exploration and sample screening. According to the method, the uncertainty of the recovery amount and demand can be effectively dealt with, the inventory cost is reduced, the supply chain robustness and sustainability are enhanced, and a self-adaptive intelligent solution is provided for WEEE management.

The application provides a medical institution safety production dynamic evaluation method and system based on multi-source heterogeneous data fusion, and is applied to the field of data processing. The application first collects multi-source heterogeneous full-dimensional data of equipment operation, environment monitoring, personnel operation, fire safety, logistics energy consumption and business process, realizes standardized conversion through cleaning, normalization and feature extraction, completes data deep coupling through weighted fusion, space-time correlation analysis and deep learning, and forms a fusion data set. Then, according to risk identification, grade determination and trend prediction, three-dimensional correlation nodes are constructed in layers, and a safety feature correlation system is established; combined with the analytic hierarchy process and fuzzy comprehensive evaluation modeling, core features are extracted, and a safety feature vector is generated. Relying on the edge-cloud collaborative engine, real-time updating, early warning and iterative optimization are completed, and through accuracy verification and dynamic correction, the final dynamic evaluation result supporting fine safety management and active risk prevention and control is formed.

The application discloses a CSR storage format-based SpMV implementation method and device and a medium, wherein the method comprises the following steps: dividing the SpMV into three segment operations, the three segment operations being a value operation, a product operation and a summation operation respectively, and each segment operation corresponding to a task; dividing each task into a plurality of task blocks; dividing all threads into a plurality of vectors, one vector comprising vector_size threads, and the vector being an execution unit of the task block; designing two launch queues and one counter to record the ready state of the task block; each vector acquiring and executing the task block in the ready state, and updating the ready state of the task block after execution; and when all the task blocks are executed, the SpMV operation is completed. The application divides the value and the product into task blocks of a fixed size, and improves the data utilization rate of GPU memory access. The application can be widely applied to the technical field of high-performance numerical calculation.

The present application relates to a kind of multi-service provider-oriented fine-grained verifiable data query method and system, data owner obtains the data unit of original data, according to the sub-data of data unit being stored by service provider, data unit commitment is generated using vector commitment and RSA accumulator, and then chain summary is obtained based on data unit commitment;Data owner chains on-chain summary;Query user initiates query request to target service provider;Target service provider retrieves local database in response to query request, obtains the query result that meets query request and constructs multi-layer verification object, including data layer verification object, unit layer verification object and row column / surface layer verification object;Target service provider returns query result and multi-layer verification object to query user;Query user issues request to chain and obtains chain summary required for query result;And query result is verified in multiple layers in combination with chain summary and the source of error data is realized fine-grained positioning.

This invention discloses a method and system for joint prediction of meteorological, wind, solar, and energy storage power based on ultra-long context, belonging to the field of power system prediction technology. The method constructs a multi-agent collaborative architecture for data cleaning, time alignment, context generation, and joint prediction, achieving automatic processing and efficient organization of historical data on wind power, solar power, and energy storage. By introducing an ultra-long context input mechanism spanning seasons and years, it overcomes the limitations of traditional model context length, achieving unified modeling and collaborative representation of multi-source data from different frequencies. Based on this, it utilizes a large time-series model to conduct zero-sample joint prediction, and combines physical constraints such as wind power curves, solar irradiance, and energy storage state of charge to construct a wind-solar-storage coupling consistency correction mechanism to optimize the prediction results. This invention effectively improves the multi-source covariate fusion capability and prediction accuracy, possessing good generalization performance and engineering adaptability.

The application belongs to the technical field of thermal power plant control, and discloses a kind of integrated centralized monitoring and collaborative control method for auxiliary control system of thermal power plant, and multiple protocol adaptive analysis is realized by wide temperature salt spray resistant embedded edge collection unit, compatible with various manufacturer equipment communication protocol, complete data standardization packaging with IEC 61850 standard, with dynamic calibration and signal isolation, redundant transmission double anti-interference technology, guarantee data stable transmission. Build four-level architecture integrated centralized monitoring platform from field device layer to management decision layer, realize whole auxiliary control subsystem data unified integration, storage and display through double ring fiber redundancy ring network, combined with digital twin virtual-real linkage complete working condition simulation and strategy pre-play, optimize plant-level monitoring information system, management information system interface realizes main and auxiliary system data intercommunication, layered permission and encrypted transmission strengthen data security, change scattered control to integrated centralized monitoring, improve data utilization.

The invention relates to a forest fire prediction method and system based on artificial intelligence, and belongs to the technical field of fire prediction, and the system comprises a data collection and fusion module which is used for obtaining multi-source time sequence data of a target forest, the multi-source time series data comprises meteorological data, remote sensing image data, ground sensor data, topographic data and humanistic geographic data; the forest attribute analysis module judges whether the target forest belongs to a high-human-trace activity forest or a low-human-trace activity forest through a preset classification model; the self-adaptive fire risk prediction engine is used for calculating a comprehensive fire risk index of the target forest and generating a fire occurrence probability space-time distribution diagram; and the targeted remedial measure generation module is used for generating differentiated remedial measure plans matched with the forest attributes. According to the method, the human trace activity attributes of the forest can be accurately identified, the fire risk factor weight is adaptively adjusted for forests with different attributes, and the accuracy of comprehensive fire hazard index calculation is improved.

The application discloses a kind of geotechnical body physical mechanics parameter coupling inversion survey methods, belong to geotechnical engineering survey technical field, comprising: multi-source survey data cooperation collection and establish five-dimensional data account book;To multi-source data carry out abnormal value elimination, normalization and adaptive weighted space-time fusion;Coupling relationship model of geotechnical body physical mechanics parameter is constructed;Establish the coupling inversion objective function of fusion data fitting error, coupling constraint error and regularization term;Set geological condition, engineering experience, physical meaning and dynamic monitoring multidimensional constraint;Coupling inversion iteration solution is carried out using improved adaptive particle swarm optimization algorithm, and dynamically updates coupling model;Residual error is verified to inversion result, cross validation and engineering verification, and generates survey report.The application significantly improves geotechnical body parameter inversion accuracy and reliability, and is suitable for various geotechnical engineering survey and stability evaluation such as slope, foundation pit, tunnel etc..

The invention discloses a gene pool central control method and system based on artificial intelligence, and the method comprises the steps: carrying out the real-time monitoring of multi-dimensional parameters through all nodes, judging a risk state, setting a window, judging a same-period risk, extracting cleaning data, carrying out the feature screening, cause verification and refining, and forming a feature set; and constructing a weighted scoring early warning model to realize graded early warning and iterative optimization. According to the scheme, the cross-node generality risk can be accurately identified, the early warning reliability is improved, and stable operation of the gene cold storage cluster is guaranteed.

A dataset distillation method based on long-range parameter trajectory matching and trainable synthesis includes: 1) generating expert training trajectories and initializing a synthetic dataset; 2) sampling expert trajectory parameters to initialize a student network; 3) training and updating the student network on the synthetic dataset; forming a local training trajectory for the student network based on the initial parameters of the student network; 4) matching expert trajectories and backpropagating the synthetic data; after the student network completes local training, selecting a long-range target parameter corresponding to the starting point of the current expert trajectory, calculating the difference between the updated student network parameters and the long-range target parameter, and using this as the trajectory matching loss; subsequently, performing backpropagation based on this loss to update the gradient of the synthetic dataset, enabling the synthetic data to drive the student network to approximate the real long-range training behavior of the expert trajectory within a finite number of training steps; 5) iterative distillation and evaluating the distillation effect; repeating steps 2) to 4) until the preset maximum number of iterations is reached. This invention can significantly reduce dependence on real data while efficiently reproducing the training effect of deep models, improving model training efficiency and generalization ability.

The invention discloses a space weather real-time monitoring system and method based on multi-satellite cooperation, and relates to the technical field of space weather monitoring, and the system comprises a multi-satellite networking module, a data cooperation processing module, a bidirectional early warning analysis module and a scheduling control module. According to the invention, by constructing the multi-satellite collaborative space weather real-time monitoring system and method, a two-way early warning mechanism of space weather and satellite operation is established, so that accurate pre-judgment of the influence of space weather on satellites is realized, and whether a satellite anomaly is caused by space weather influence or equipment failure is distinguished through multi-satellite data cross validation; the problems of one-way monitoring and easy false early warning in the prior art are solved, the comprehensiveness and the data utilization rate of space weather monitoring are improved through multi-satellite networking cooperative data acquisition and establishment of a correlation analysis mechanism, meanwhile, the accuracy of satellite anomaly judgment is improved, interference of false early warning on a space task is effectively avoided, and the safety of space weather monitoring is improved. And reliable technical support is provided for safe and stable development of aerospace tasks.

The application relates to a road construction process online monitoring system, method and electronic equipment, and belongs to the technical field of road construction process monitoring and management. The system comprises a data acquisition end, a data management and analysis end and a supervision terminal. The data acquisition end is used for collecting construction physical parameters and three-dimensional space information of a road construction site through a field detection device, and constructing a BIM three-dimensional simulation model based on the three-dimensional space information. The data management and analysis end is used for preprocessing the construction physical parameters, and monitoring different construction procedures in the road construction process through multiple monitoring subsystems and the preprocessed parameters to generate index data. The supervision terminal is used for displaying the BIM three-dimensional simulation model. The construction physical parameters and the index data are contained in the BIM three-dimensional simulation model. The road construction process online monitoring system provided by the application improves the field construction quality and the data utilization rate in the construction process, and realizes visual monitoring of a road slope construction site.