73 results about "Data Pooling" patented technology

The application discloses a kind of data processing method, data processing system and storage medium, it is related to data processing technical field, method includes: client obtains target object selected target crowd label, obtains the test account preconfigured for label, determine target test account from it and execute automatic login;Forwarding server intercepts interface request sent to simulation server in application running;When request belongs to preset interface request set, it is forwarded to simulation server;Simulation server obtains matched preset response data from shared expected data pool and returns client;Client provides it to application, so that application presents corresponding interaction interface or executes corresponding function logic.The application automatically completes the whole process from crowd label selection to interface presentation by preconfiguring test account and shared expected data pool, avoids different personnel repeatedly constructing account and preparing data, solves the problem of low experience efficiency caused by repetitive labor, and improves experience efficiency.

This invention discloses a distributed digital management and control system and method for all business operations of highway toll stations. The system includes multiple business backend service modules deployed on distributed nodes to form atomic business services; a device protocol parsing module based on a device collaboration network uses an automatic protocol identification algorithm to achieve one-time access for multiple types of devices and system-wide compatibility; a digital twin module constructs a three-dimensional virtual model of the toll station across the entire scenario; and a unified data pool module aggregates and stores relevant data. The system achieves distributed automated management and control of the entire business process through collaborative calls between the business backend service modules. This invention, by constructing a device collaboration network and parsing module, can automatically identify and access multiple types of devices, enabling plug-and-play functionality and reducing integration and maintenance complexity. Simultaneously, by breaking down core business operations into atomic services and forming a distributed architecture through standardized API interactions, it eliminates the need for large-scale modifications, improves system business agility, and effectively overcomes the shortcomings of traditional monolithic architectures.

The application discloses a kind of carbon emission improvement potential method of mine exploitation life cycle process, it is related to mine carbon emission management technical field, including, collecting real-time sensor data of each stage in the whole life cycle of gold mine, equipment operation log, process parameters and unstructured document, construct dynamic updating multi-source data pool;Based on multi-source data pool, extract carbon emission entity and carbon emission entity semantic relationship, construct carbon footprint knowledge graph;Multi-objective optimization is carried out to the collaborative decision result, and the non-dominated solution set is solved and the optimal balanced solution is screened, and the optimization strategy sequence and the corresponding expected carbon emission are generated;The optimization strategy sequence is input into mine production scheduling process execution, real-time monitoring actual carbon emission data and comparing with expected carbon emission, and the dynamic carbon emission benchmark model is revised by comparison result.The application realizes semantic understanding and correlation analysis to mine carbon emission from exploration to whole process of closing pit by constructing carbon footprint knowledge graph.

The application discloses a SNR-MCS dynamic mapping table design method, and belongs to the technical field of wireless communication, comprising: continuously updating the SNR-MCS dynamic mapping table; outputting the transmission success probability based on the probability prediction model according to the signal-to-noise ratio and the modulation and coding mode; screening the MCS candidate set according to the transmission success probability to construct the MCS prediction model; collecting the actual signal-to-noise ratio to input the MCS prediction model to output the modulation and coding mode, and collecting the transmission feedback to update the data pool; dividing the signal-to-noise ratio into several segments, generating the SNR-MCS dynamic mapping table based on the coverage range of the signal-to-noise ratio of each segment and the MCS prediction model; using the SNR-MCS dynamic mapping table for interaction and updating the data pool; and retraining the probability prediction model through the updated data pool. The application solves the problem of low transmission efficiency and reliability caused by the fact that the prior art cannot adapt to the changing channel conditions.

This invention discloses an automatic inspection method and system for server security self-testing. The method includes the following steps: S1, acquiring distributed node information and initial security configuration parameters of a server group, and establishing a global security inspection topology; S2, deploying an adaptive inspection agent on each server node, wherein the agent initializes a local inspection strategy based on the global security configuration parameters; S3, through a distributed computing architecture, each adaptive inspection agent independently performs local security checks and generates node security status data. This invention relates to the field of server security technology. This automatic inspection method and system for server security self-testing, through collaborative communication between agents, enables the system to share threat information, vulnerability remediation suggestions, and system status data in real time, constructing a global security data pool, and deriving a global security posture value based on weighted calculation. This allows the system to promptly understand the security status of the entire server group and provide accurate security posture awareness.

The application discloses a kind of based on context learning's incremental user portrait method and system, belong to electric power system user portrait technical field, the method is: based on the small sample load data of to-be-tested incremental user construction incremental user portrait task pair;Obtain several current context task pairs, and incremental user portrait task pair and several current context task pairs are combined to form test sample;Based on test sample and pre-trained user portrait model, obtain incremental user portrait result;Pre-training process includes: obtaining the stock historical load data sequence set of several stock users;Based on stock historical load data sequence set, preset historical window length and preset future window length, construct the preset portrait model training data pool corresponding to several stock users;The pre-constructed basic portrait model is trained based on preset portrait model training data pool, and user portrait model is obtained, therefore, by implementing the application, small sample, high-precision portrait to incremental user can be realized.

A data anonymization pipeline system for managing holding and pooling data is disclosed. The data anonymization pipeline system transforms personal data at a source and then stores the transformed data in a safe environment. Furthermore, a re-identification risk assessment is performed before providing access to a user to fetch the de-identified data for secondary purposes.

A vision inspection method based on coarse-to-fine patch level classification and a system thereof according to one embodiment of the present disclosure relate to a vision inspection method based on coarse-to-fine patch level classification and a system thereof which simultaneously improve the accuracy and processing speed of vision inspection by coarsely detecting a region suspected of being defective from given vision data and finely classifying the detected region on the basis of a pre-built data pool.

This invention relates to the field of computer data processing and data visualization technology, specifically a personalized report configuration platform system, comprising: a data virtualization module: establishing physical data source mapping and constructing atomic data pools; an indicator graph construction module: generating atomic indicator dependency graphs, storing logical operation relationships rather than calculation results; a context-aware module: capturing environmental parameters such as tenant roles and time windows; a dynamic compilation engine: injecting environmental parameters into the graph and instantiating and parsing logical nodes to generate structured query instructions; and a rendering and compositing module: executing instructions to obtain result data and mapping it to view components. This invention achieves dynamic instantiation of logic defined once in multiple places, avoiding hard-coding development and improving the maintainability and response speed of the system.

The application discloses a network access active defense control method and system. A network layer-terminal layer-power layer multi-source data pool is constructed, and multi-dimensional data is captured through probes and collectors. Time and space normalization is realized by using a sliding window interpolation method and IP network segment division. The LSTM model is used to learn normal traffic patterns, and the improved isolated forest algorithm is used to identify abnormalities, and an attack situation label set is output. The network is divided into three scenes of core area, edge area and hub area, and the abnormal coupling relationship is analyzed. The path node deviation is extracted, the deviation level sequence is constructed, and the defense situation number chain is established. The outstanding abnormal mode is determined through statistical analysis, and is mapped to the defense action strategy table to generate a standardized defense instruction, so that the closed-loop response from abnormal detection to accurate defense is realized, and the controllability and recovery efficiency of the network situation are improved.

The invention discloses a construction method and application of a safety evaluation risk prediction model after child medication marketing, and the method comprises the steps: constructing a three-level index system, and determining the weight of each level of index through an analytic hierarchy process; based on the three-level index system, determining a risk feature set, constructing a three-level model architecture, and performing model training by adopting a machine learning algorithm; collecting multi-source real world data, and establishing a standardized data pool; the performance of the model is improved through hyper-parameter optimization and integrated learning, the performance of the model is verified and evaluated by adopting internal cross validation and an external independent data set, and the transparency of the model is enhanced through an interpretability technology; and establishing a dynamic updating mechanism, deploying the model as a cloud API service, and realizing risk prediction and early warning functions. According to the invention, multi-source real world data can be effectively integrated, the children medication safety risk can be accurately identified, the method is used for safety evaluation after children medication is listed, and the method is of great significance to supplement clinical test insufficiency before children medication is listed, reduce medication risk and guarantee children medication safety.

The application provides a hydrological time series prediction bias self-adaptive correction method and system based on physical constraints and online incremental learning, wherein the method comprises the following steps: monitoring the prediction results of a deep learning hydrological model and actual observation data in real time, calculating a joint bias index based on an improved dynamic time warping distance and flood peak time point difference considering the basin topographic slope and flow field change rate, and triggering a bias correction process according to a dynamic threshold; constructing an incremental data pool with a fixed size; performing lightweight incremental learning according to the magnitude of the bias degree; generating a corrected prediction sequence based on the incremental learning result, and continuously monitoring the correction effect by using a closed-loop feedback mechanism, and dynamically updating the bias threshold. The method can detect and self-adaptively correct the flood peak time phase bias of the deep learning hydrological model in long-time series prediction in real time, significantly improves the prediction accuracy and timeliness, effectively alleviates the catastrophic forgetting problem, reduces the consumption of computing resources, and is suitable for various hydrological models and complex basin environments.

The application provides a visual large model staged training data construction method and device, the method comprises the following steps: obtaining image data of a target application scenario and analyzing the image data to obtain a basic data pool comprising a structured scene attribute description; calculating a single probability of each attribute label, calculating a joint probability of attribute label pairs meeting a first screening condition, and judging as a long-tail sample if the attribute label pairs do not meet the first screening condition; classifying the image data according to the single probability and the joint probability to obtain a sample classification result; in a basic training stage, randomly sampling and screening image data meeting a preset quality condition to construct a basic training data set; in a target scene adaptation stage, weighted sampling according to the single probability and the joint probability to construct a target scene adaptation training data set; in a difficult sample and long-tail sample optimization stage, screening difficult samples and long-tail samples according to a prediction result of a current visual large model and the sample classification result to construct a difficult sample and long-tail sample optimization training data set. The application is self-adaptive to a scene and is efficiently trained in stages.

This invention provides an automatic gas lift control method for shale gas wells with accumulated liquid, relating to the field of shale gas extraction technology. The method involves real-time acquisition of production parameter data from the shale gas well using wellhead sensors; based on the production parameter data, liquid accumulation identification logic is used to determine the real-time liquid accumulation status of the gas well; when the real-time liquid accumulation status is determined to be either a liquid accumulation risk state or an already liquidated state, effective production data under the liquid-free state is retrieved from a liquid-free data pool, and a multi-model fusion algorithm is used to calculate the comprehensive critical liquid-carrying flow rate; based on the comprehensive critical liquid-carrying flow rate, a multi-objective optimization algorithm aiming to maximize gas injection efficiency is used to calculate the optimal gas injection rate; based on the optimal gas injection rate, a valve opening control command is generated, and the control command drives the gas injection valve to actuate, obtaining the gas lift control result. This invention solves the problems of reliance on manual labor, low control accuracy, and slow response in existing technologies.

The application discloses a SAR image data-free black box attack method based on a diffusion model, constructs a diffusion model, processes input random noise vectors based on the diffusion model, and then generates generated data conforming to SAR image structural features and close to target black box model training set data distribution. A substitute model is trained by using generated data in a generated data pool by adopting a knowledge distillation method. Through an iterative optimization diffusion model and substitute model collaborative training mechanism, the diffusion model and the substitute model are alternately trained to enhance the approximation ability of the substitute model, and finally, the substitute model generates adversarial samples. The application introduces a diffusion model to generate high-quality data conforming to SAR image statistical characteristics and structural features, so that generated samples are close to target model training data in the distribution level, thereby providing an effective and stable learning basis for the substitute model. The application significantly improves generated data quality, substitute model training stability and black box attack success rate under the condition of data-free black box.

The application relates to the technical field of server fault prediction, and discloses a server fault prediction system and method. The method comprises the following steps: collecting running index data streams of server components through a monitoring agent to form an initial running data pool; performing multi-level cleaning and context association analysis on the data to construct a high-quality running data set; generating a server health state portrait and establishing a dynamic health state baseline model based on the set; constructing a knowledge graph capable of representing fault causal relationships by analyzing the deviation mode of real-time indexes and the baseline model; and driving a risk calculation engine by using the knowledge graph to output the fault probability of the components and a maintenance scheme. The method realizes more accurate and more forward-looking prediction of server faults by improving data quality and introducing knowledge reasoning, and effectively supports intelligent operation and maintenance decisions.

The application relates to the technical field of industrial communication and intelligent manufacturing system integration, and discloses an enterprise-based intelligent manufacturing digital integration analysis method, which comprises the following steps: collecting real-time operation data of production equipment, forming an integrated data pool after cleaning, standardization and space-time alignment, constructing an enterprise intelligent manufacturing knowledge graph based on the business logic of the intelligent manufacturing field and calculating key performance indicators, combining the integrated data pool and the knowledge graph, applying the integrated data pool and the knowledge graph to constructing a digital twin model, predicting production bottlenecks and evaluating different production scheduling plans, and finally generating a comprehensive analysis report based on the index calculation results and the simulation results. The application realizes full-process automation from data perception, integration and application to decision output, and significantly improves the transparency, analysis accuracy and decision intelligence level of the manufacturing management process.

The application relates to the field of event propagation path analysis methods, discloses an event propagation path analysis method based on RAG retrieval and information extraction technology, and discloses an event propagation path analysis method and system based on RAG retrieval and information extraction technology.The method first constructs a standardized data pool containing unique content fingerprints, realizes data deduplication and tracing by using SimHash; then constructs an exclusive RAG vector knowledge base, converts the seed context into an initial query vector; performs iterative retrieval, extracts propagation triples by using a model, and converts new entities into query vectors to trigger multi-hop retrieval to mine implicit correlations; merges nodes by using co-reference resolution and constructs a time-series directed graph; and finally calculates node centrality and vector semantic offset.The application solves the problems of propagation path breakage and content alienation recognition by iterative retrieval and semantic quantitative analysis, and realizes accurate analysis of event evolution.

This invention discloses a charging and swapping user behavior pattern analysis system and method, relating to the field of electric vehicle charging management technology. It acquires and establishes a multi-source data pool, merges the multi-source data pool, analyzes user charging and swapping behavior patterns and calculates the probability of abnormal behavior, establishes dynamic scheduling rules based on user charging and swapping behavior patterns, performs resource allocation and service classification, optimizes the model and establishes a response mechanism, and generates real-time scheduling instructions. This invention, through multi-source data collection and analysis, accurately predicts user charging and swapping behavior, dynamically optimizes resource allocation, improves charging station operating efficiency and user satisfaction, promotes the integration of virtual power plants, constructs a vehicle-to-grid strategy optimization module, quantifies the potential of users to participate in grid peak shaving, and optimizes regional energy dispatch. Furthermore, it has an anomaly detection and response mechanism to ensure safety and stability, and promotes the development of charging and swapping services towards high efficiency, intelligence, and sustainability.

The application discloses a kind of game level design and creation method, comprising the following steps: providing level editing interactive interface, so that the planner carries out the new establishment of level, modification, save and synchronization to terminal for testing and preview by computer background end;Real-time rendering edited level in terminal, so that the planner experiences level design effect with game player perspective, and optimizes and adjusts level layout;With player perspective, the action of completing level is executed in terminal;Get settlement information after completing level, and the settlement information is synchronously transmitted between terminal and background end, and the settlement information is stored in background end data pool;Based on the settlement information, automatically generate new level, and the new level includes level template matching with specified difficulty.The technical scheme is a kind of game level design and creation method and can predict player purchase behavior.

The application provides a magnetic resonance device stable operation predictive remote monitoring method and system, relates to the technical field of medical device internet of things remote monitoring and intelligent operation and maintenance, and the method comprises the following steps: capturing multi-dimensional running state original physical quantities in the conventional electromagnetic field domain between devices, performing noise reduction processing on the original physical quantities, transmitting the processed data stream to a set cloud data pool through the internet of things, and constructing a high-dimensional initial feature tensor set; taking the high-dimensional initial feature tensor set as input, calling on-site calibrated device intrinsic state benchmark parameters, converting absolute physical quantities into relative potential energy deviation sequences in a cross-modal data affinity mapping manner, and obtaining a standardized relative deviation feature vector group; taking the standardized relative deviation feature vector group as an operation medium, and loading it into a cloud-deployed space-time evolution AI analysis model. The application can realize accurate prediction of magnetic resonance device faults, early warning of superconducting loss and closed-loop management of operation and maintenance.

The present application relates to a kind of based on virtual-real linkage perception large-scale digital twin model adaptive compression and streaming method, comprising: build edge perception networking, form perception data pool;Construction digital twin basic model;Design layered adaptive hybrid compression algorithm, adopt BP neural network dynamic decision each level model compression strategy, according to core layer, middle layer, edge layer executes different hybrid compression in turn and integrates model, obtains integrated layered compression model;Combined with perception data pool adjustment transmission priority, executes dynamic streaming.This application solves the problem that existing fixed compression algorithm is easy to lose key details or transmission delay is too high by layered adaptive hybrid compression algorithm;Reduce the deployment difficulty and operation cost of large-scale digital twin model;Effectively avoid loading lag, interaction lag and other problems, meet real-time monitoring, remote control and other practical application requirements, further promote the popularization of digital twin technology.

This invention relates to the technical field of distribution network line handling systems, and in particular to a distribution network line fault analysis and handling system, comprising a multi-source data acquisition unit, an intelligent fault analysis unit, a collaborative handling unit, a system management unit, and an inspection and troubleshooting unit. The multi-source data acquisition unit is used to acquire fault-related data in real time from external multi-source heterogeneous systems, and to perform standardized processing and fusion to form a unified data pool. These multi-source heterogeneous systems include a distribution automation system, an electricity consumption information acquisition system, a geographic information system, a production management system, and a meteorological information system. By fusing multi-source data, it breaks down information silos, integrates multi-dimensional data from distribution automation, GIS, and meteorology, providing comprehensive support for fault analysis, shortening analysis time, avoiding the risk of misoperation, and improving on-site handling efficiency. The inspection robot enables non-contact temperature measurement and foreign object removal, reducing human error risks.

The application discloses a kind of multimodal data distributed training method and system, it is related to robot field, including: each configuration data pool is equally divided into N shards, and the shard with same serial number in different configuration data pool is distributed to the same computing node;Each computing node loads corresponding shard data subset according to the shard serial number distributed;Each computing node independently executes modal decision;According to modal decision result, corresponding data loader is selected to load homogeneous batch training data of corresponding mode from the data subset of the node;Each computing node sequentially executes forward propagation and back propagation to homogeneous batch training data, and obtains the gradient of each node;Global gradient fusion is carried out to the gradient of all computing nodes, and global gradient is obtained;All computing nodes update model parameters using global gradient.The application is physically isolated from the forward interference and Loss scale conflict between different modal data by shard scheduling and isolated training.

The embodiment of the specification provides a kind of multi-source industrial data intelligent management and dynamic analysis method and system, wherein, method includes: accessing the industrial data of multiple heterogeneous data sources, generates standardized data after pre-processing;Data feature extraction and data drift detection are carried out to standardized data, dynamically update data management rules, adopt the layered cleaning of standardized data using updated data management rules, obtain data after management;Industrial knowledge graph is constructed, and based on industrial knowledge graph, associated dimension identification and cross-source association are carried out to data after management, complete the real-time integration of multi-source data, output to analysis data pool;Based on the priority of preset analysis task, corresponding analysis task is executed by scheduling computing resource, to obtain analysis result;Data quality problems or equipment state information identified in analysis result are fed back to data management link, dynamically optimize data management rules or equipment reference parameters based on feedback, form closed-loop control.

The application discloses a kind of fuel cell vehicle driving range prediction systems based on historical real-time energy consumption, wherein multi-source heterogeneous data perception layer collects data to construct multi-modal data pool, and uses hidden Markov model output driving style determination result;Dynamic response optimization layer is based on model predictive control algorithm dynamically adjusts the output power of electric pile and BOP component;Adaptive prediction model layer aggregates vehicle end and cloud parameters using federated learning framework, combines gated recurrent unit network to dynamically predict energy consumption, and uses convolutional neural network and range rolling prediction model to generate range prediction value and energy consumption distribution heat map;User interaction feedback layer displays prediction results and intelligent recommended path through three-dimensional visualization interface, and returns user feedback signal to front end each layer to realize system optimization.The system of the present application significantly improves the accuracy and system adaptive ability of fuel cell vehicle driving range prediction through multi-layer cooperative closed-loop control mechanism.

This disclosure provides a method, system, apparatus, device, and medium for data transmission between multiple sites. The method includes: responding to a data copy command, controlling all storage nodes in the original data pool of a first site to perform a data copy task, copying the data stripe to be migrated to a free data pool of a sub-site located in the first site; when the data stripe to be migrated is completely copied to the free data pool, controlling the free data pool to generate a migration verification fragment corresponding to the data stripe to be migrated, and storing the migration verification fragment in the free data pool; powering down the storage devices in the free data pool to transport the free data pool to a second site via physical transport, enabling the second site to read the data stripe to be migrated and the migration verification fragment in the free data pool and verify the data stripe to be migrated. This method employs automation, reducing the tediousness and error risk of manual operation, and ensuring the integrity and security of data during transmission.

This invention relates to the field of data sharing technology, specifically to a data sharing management method and system for laboratory equipment. The method includes the following steps: identifying testing items corresponding to multiple laboratory equipment; configuring multiple management points based on the testing items; establishing a shared map among the multiple management points; and using the multiple management points with the shared map as workstations. The shared map includes a project map and a data acquisition network. Multiple clients are configured for each workstation, and a temporary data pool is configured for each client. An electronic lab logbook is identified, and an association is established between the workstation and the electronic lab logbook. Shared data for the corresponding target testing item is determined from the workstation based on the shared map. Equipment data for the corresponding testing item is determined based on the laboratory equipment. The shared data and equipment data are used as experimental data, enabling automatic filtering and replacement of the shared data, thereby improving the efficiency of testing tasks.

This invention discloses a data processing optimization method, device, medium, and program product, relating to the field of computer storage. The data processing optimization method includes: creating a distributed shared storage system for a high-performance computing (HPC) cluster; storing raw cold data in a first cold data pool, and retrieving data to be computed from the first cold data pool based on current data computation instructions; storing preliminary result data obtained by processing the data to be computed through computing nodes in a hot data pool; obtaining target result data by processing the preliminary result data through visualization nodes, and storing the target result data in a second cold data pool; wherein the computing nodes and visualization nodes are cross-system nodes in the HPC cluster. The technical solution of this invention can complete data interaction between computing nodes and visualization nodes based on a distributed shared storage system, possessing multi-system compatibility and low storage cost.