229 results about "Data Pooling" patented technology

The invention relates to the technical field of environmental governance and remediation, in particular to a combined contaminated site soil remediation data management method, which comprises the following steps of: firstly, integrating multi-source heterogeneous data through a unified space-time coordinate system to generate a fusion data pool, and then driving a pollution assessment model to generate a multi-dimensional space-time pollution map; and then intelligently matching a candidate repair process chain based on an associated knowledge base, performing iteration deduction through a dynamic multi-objective optimization engine to obtain an optimal repair scheme, converting the optimal repair scheme into a digital regulation and control instruction, and finally monitoring an execution state and environmental response data in real time and feeding back and updating a data pool to realize closed-loop optimization. According to the method, the data quality and the pollution evaluation accuracy are improved, the adaptability of the repair process and the cost performance of the scheme are optimized, the repair effect is ensured to be stable and reach the standard, and the efficiency and reliability of the repair project are improved.

The invention discloses a ground engineering construction quality supervision digital delivery system, which relates to the technical field of engineering supervision, and comprises the steps of generation and multi-source data checking, activity record structuring and anti-verification before hiding, space-time reference unification and point cloud fusion playback, rule checking and differential rectification, and object-level marshalling delivery and asset authority management. According to the method, a unified data pool, activities, events and evidence chains are constructed through object codes to achieve time-space consistent association, image, point cloud and detection consistency check is conducted before hiding, blocking difference and unified reference fusion point cloud are generated, indexes support three-dimensional playback, rule base batch check is conducted, and issuing rectification is conducted. And the object-level delivery list and fingerprints support cross-platform identification and auditing to form a traceable link.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention discloses a traffic flow prediction method and device, a storage medium and electronic equipment. The method relates to the technical field of traffic flow analysis, and comprises the following steps: constructing a multi-source traffic data pool, and preprocessing historical traffic data in the multi-source traffic data pool to obtain a target multi-source traffic data pool; performing data enhancement processing on the historical traffic data in the target multi-source traffic data pool by adopting a pre-trained GANs model to obtain an enhanced sample set for training a target traffic flow prediction model; performing model training on an initial traffic flow prediction model by adopting a Dropout method based on the enhanced sample set to obtain a target traffic flow prediction model meeting a preset condition; and performing traffic flow prediction on multi-source traffic data acquired in real time by using the target traffic flow prediction model to obtain a traffic flow prediction result. According to the method, the accuracy of urban traffic flow prediction can be improved.

The invention discloses a power industry language model training data dynamic selection method, system and device, and the method comprises the steps: obtaining original scene corpus data based on a power industry power grid dispatching and fault handling application scene aimed at language model training, and constructing a candidate data pool and a scene verification data set; performing parallel or pipelined multi-dimensional quality evaluation on the candidate data in the candidate data pool, dynamically adjusting the weight corresponding to each dimension in the multi-dimensional quality evaluation based on an adjustment strategy, and calculating to obtain a comprehensive quality score; a failure case library is constructed, in the language model training process, the failure case library is updated regularly, semantic relevance between candidate data and the failure case library is quantified, and batch construction of training data is carried out based on the semantic relevance; and based on the batch construction result, performing language model training feedback circulation. According to the invention, the overall efficiency and utilization rate of training data are effectively improved.

In various embodiments, a system for detecting and preventing equipment failure using a plurality of agents includes an interface configured to receive information about a state of the equipment from a plurality of sources. The system includes a data pool including the information from the interface. The system further includes a plurality of agents configured to access data in the data pool, where the plurality of agents are trained to assess a condition of the equipment based on the data. The system includes an orchestrator configured to: evaluate an assessment of a first agent; access the second agent for further assessment in response to the evaluation of the assessment of the first agent indicating access of a second agent; and cause a modification to be made to the equipment.

The invention relates to the field of geographic information mutual analysis, and discloses a multi-source dynamic collaborative geographic information mutual analysis method and device, equipment and a medium. The method comprises the following steps: collecting time-synchronized static geographic information, dynamic GPS positioning data and a plurality of pairs of starting and ending point unstructured text data sets, and constructing a dynamic spatio-temporal data pool; high-quality spatio-temporal data is obtained through dual anomaly verification and Kalman filtering cleaning correction; optimizing an address longitude and latitude mapping table in combination with a three-level administrative range and a dynamic high-frequency word; a non-standard address is analyzed into a structured text through a BERT-CRF model, and a precise coordinate and a standardized text are obtained through a hierarchical dynamic matching algorithm. By adopting the method, meter-scale positioning is realized, the non-standard address resolution accuracy is greatly improved, the duplicate name address mismatching rate is reduced, a dynamic scene is adapted, the resolution efficiency and reliability are considered, and an efficient technical support is provided for bidirectional accurate mutual resolution of geographic coordinates and text addresses.

The invention discloses a new energy station operation index multi-dimensional comparative analysis display method and system, and belongs to the field of new energy station operation management and data analysis. The method comprises the following steps: fusing and standardizing multi-source heterogeneous data, collecting and cleaning multi-station data, and classifying and storing the multi-station data as a standardized data pool; rasterized multi-dimensional indexes are calculated, grids are divided, data are mapped, and grid-level and hierarchical multi-dimensional indexes are generated; performing dynamic weight and TOPSIS evaluation, constructing an evaluation model to calculate the score of a single station, extracting a core index intersection, and transversely comparing the ranking of the stations; the evaluation result is subjected to instantiation storage and hierarchical storage to guarantee data query and tracing; and performing multi-dimensional visual display, displaying the data by multiple terminals, and generating a decision report. The system comprises corresponding function modules. According to the method, the problems of data fragmentation, evaluation system deficiency, non-visual display and the like are solved, multi-station data integration, quantitative evaluation and visual decision making are realized, and the operation management efficiency is improved.

The invention relates to the technical field of power grid exception handling, and discloses a power grid exception event handling method and system based on a knowledge graph. The method comprises the following steps: collecting time sequence data flow and space coordinate information of a power grid abnormal event in real time through multi-source sensing equipment, and fusing equipment operation parameters to construct a dynamic event data pool; performing graph structure conversion on the dynamic event data pool based on a knowledge graph topological connection relationship, calculating event node propagation path intensity and generating an event propagation network model; key abnormal indexes are separated out from the model by adopting a multi-modal feature extraction technology, risk probability calculation is completed in combination with an adaptive learning algorithm, and an abnormal risk distribution diagram is output; and classifying event levels according to the graph, and automatically generating a processing instruction sequence. According to the method, omnibearing data perception and accurate analysis of the abnormal events of the power grid are realized, an event propagation rule is clearly presented, risk assessment is enabled to be closer to an actual operation state, and accurate guidance is provided for abnormal disposal.

The invention discloses an intelligent disaster avoidance path planning method for a mine. The method comprises the following steps: acquiring environmental parameters, disaster information, personnel distribution data and risk avoidance facility state data of the mine in parallel; when it is continuously detected that parameters exceed a safety threshold for multiple times, disaster early warning is triggered, a disaster event identifier is generated, and a dynamic data pool is formed by delimiting associated data in an influence area and a convergence area according to disaster information by taking an event as a center; disaster diffusion simulation and personnel evacuation trajectory simulation are executed based on the dynamic data pool, and a disaster diffusion simulation result and a personnel evacuation trajectory simulation result are fused to form a dynamic obstacle map; and obtaining a plurality of paths according to the personnel distribution data and the risk avoiding facility state data, and obtaining a disaster avoiding path based on the dynamic obstacle map by adopting a multi-objective optimization algorithm according to the path length, the safety redundancy and the group evacuation efficiency. According to the method, the dynamic planning of the mine disaster avoidance path is realized, and the path safety and the evacuation efficiency are remarkably improved.

The invention discloses an improvement potential quantification method for carbon emission in a mining life cycle process, and relates to the technical field of mine carbon emission management, and the method comprises the steps: collecting real-time sensor data, equipment operation logs, process parameters and unstructured documents of each stage in the full life cycle of a gold mine, and constructing a dynamically updated multi-source data pool; based on the multi-source data pool, carbon emission entities and a carbon emission entity semantic relationship are extracted, and a carbon footprint knowledge graph is constructed; performing multi-objective optimization on a collaborative decision result, solving a non-dominated solution set, screening an optimal equilibrium solution, and generating an optimization strategy sequence and corresponding expected carbon emission; and inputting the optimization strategy sequence into a mine production scheduling process for execution, monitoring actual carbon emission data in real time, comparing the actual carbon emission data with expected carbon emission, and correcting the dynamic carbon emission reference model through a comparison result. According to the method, the carbon footprint knowledge graph is constructed, so that the semantic understanding and correlation analysis of the whole-process carbon emission of the mine from exploration to pit closing are realized.

The invention relates to the technical field of automatic driving, and particularly discloses an off-line evaluation and iterative optimization method and system for an automatic driving system, and the method comprises the steps: recording a task-level data packet, and binding a reproducible fingerprint for the task-level data packet; in the off-line stage, a consistent input data stream is reconstructed, and the consistent input data stream is input into a current online running old model in parallel to be compared with a to-be-evaluated candidate new model for evaluation; according to a comparison evaluation result, extracting a scene fragment data packet based on a preset multi-dimensional evaluation index and a triggering rule; the method comprises the steps that scene fragment data packets are obtained, failure type labels are automatically generated for the scene fragment data packets, bucket dividing and weighted sampling are carried out according to scene features, and scene fragment data are returned to a model training data pool, full-link data packaging is carried out with task-level data packets as the granularity, reproducible fingerprints are bound for all the data packets, and therefore the scene fragment data can be obtained. Therefore, the offline playback, the A / B evaluation result and the backflow training sample can be subjected to consistent indexing and tracing through the fingerprint.

Methods and systems for a data marketplace in a fluid conveyance device includes a self-organizing data marketplace. The self-organizing data marketplace includes at least one data collector and at least one corresponding fluid conveyance device in an industrial environment, wherein the at least one data collector is structured to collect detection values from the fluid conveyance device; a data storage structured to store a data pool comprising at least a portion of the detection values; a data marketplace structured to self-organize the data pool; and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to a user in response to the user data request.

The invention discloses an image report process AI quality control system based on data fusion and a dynamic rule engine, and the system comprises a data layer module which is used for integrating multi-source data from an image archiving and communication system, a radiation information system and a hospital information system, and carrying out the preprocessing of the data, and forming a high-quality training data pool; furthermore, the image archiving and communication system obtains historical images in a DICOM format and corresponding report data; the radiation information system collects examination application information and doctor diagnosis information. According to the image report process AI quality control system based on data fusion and the dynamic rule engine, full-dimension and high-efficiency accurate quality control is realized: the system can perform comprehensive automatic screening of grammar, text, logic consistency and image-text relevance on an image report by integrating natural language processing, computer vision and a cross-modal matching technology; error correction response time is shortened to a second level from several minutes of each case of traditional manual auditing, and quality control efficiency is remarkably improved.

The invention belongs to the technical field of advertisement putting, and discloses an accurate customer obtaining and advertisement putting system and method based on big data. The method comprises the following steps: S1, multi-source data acquisition: acquiring Internet public data, enterprise private domain data and third-party compliance data to form a comprehensive data pool; s2, data structured storage: the collected data are classified and stored, and an index is established. According to the method, through the collaborative application of multiple technologies such as integrated distributed crawlers, generative adversarial networks, Kalman filtering, edge calculation, multiple linear regression and Lasso regularization, full-link optimization from data acquisition to effect landing is realized, the core problems of inaccurate customer acquisition and poor advertisement effect in the prior art are solved, and the service life of the advertisement is prolonged. Manual intervention cost is reduced through an automatic mechanism, the comprehensive goals of improving customer obtaining precision, optimizing advertisement effect, reducing marketing cost and guaranteeing long-term effectiveness are finally achieved, and a more systematic and more efficient technical support is provided for enterprise digital marketing.

The invention discloses a penetrating supervision method and system based on market subject digital twinning, belongs to the technical field of market supervision and digital twinning, and aims to solve the technical problem of how to realize full-flow penetrating supervision of multi-source data and improve supervision accuracy and efficiency. Comprising the steps of performing data preprocessing on multi-source heterogeneous data, and constructing a unified data pool; performing 1: 1 three-dimensional modeling on the physical space of the market subject to obtain a digital twin model capable of real-time interaction; acquiring market subject state data in real time, synchronizing the state data to the digital twin model, checking the dynamic operation state of the market subject through the digital twin model, and marking an abnormal state; performing parallel computing on a large-scale scene through a parallel visual rendering algorithm, simulating a risk evolution path in combination with a simulation deduction and algorithm integration technology, and generating a risk level and early warning information; and generating a processing task based on the risk level and the early warning information, and feeding back a processing result to the digital twinborn model.

The invention relates to a wire harness quality prediction and evaluation method fusing historical data, and the method comprises the steps: collecting the real-time data of wire harness design, production and test stages, and equipment operation, image collection and defect classification parameters, obtaining the historical data of the same type of wire harnesses according to batch specifications, and building a mapping index to form a basic data pool; extracting and fusing features to construct a multi-dimensional matrix; establishing a failure rule set and a weight model in combination with historical cases; inputting a trained machine learning model to generate a quality prediction result; and supplementing new data to update related rules and models after abnormal traceability to form a closed loop. According to the invention, by fusing real-time and historical data and combining feature extraction, model training and closed-loop updating, accurate prediction and efficient fault tracing of the wire harness quality are realized, the evaluation system is continuously optimized, and the reliability and production applicability of wire harness quality evaluation are remarkably improved.

The invention provides an anesthesia auxiliary system supporting personalized administration and risk early warning. A hardware adaptation layer of the anesthesia auxiliary system adopts a general protocol and a customized driving mode; the data layer realizes acquisition, cleaning, storage and sharing of multi-source data, constructs an anesthesia whole-process data pool, and provides data support for algorithm operation; the core function layer utilizes an algorithm to run through the whole process of decision making, early warning and control to realize intelligent anesthesia management; and the interaction layer adopts a professional mode and concise mode double-interface design, so that the use requirements of different scenes are met, and the operation efficiency is improved. A multi-modal data acquisition-decision support-drug infusion control-risk early warning-data tracing full-closed-loop software system is designed, a layered decoupling architecture is adopted, and anesthesia equipment and a hospital information system are in butt joint through a standardized interface. According to the core improvement point, multi-modal data acquisition is supplemented, a data closed loop is perfected, algorithms such as off-line reinforcement learning, LSTM look-ahead early warning and fuzzy logic control are mainly integrated, and data driving and knowledge driving double-wheel decision making is achieved.

The invention discloses an energy power safety dynamic early warning method and system, and a storage medium. The method comprises the steps of constructing a market and physical coupling data pool including a commercial decision instruction, a power grid physical state and a terminal response feature; generating a business instruction concurrency impact feature vector for quantifying the business instruction space aggregation degree and the time synchronization; constructing a physical vulnerability embedding model based on the graph neural network, and dynamically generating a physical embedding vector representing power grid node vulnerability; and inputting the impact characteristic vector and the physical embedding vector into a cross-modal prediction model, and deducing and early warning a power grid physical risk caused by a commercial instruction in a silent period before instruction execution. By establishing the novel mapping relation between market behaviors and physical safety, advanced early warning of cross-domain risks caused by large-scale concurrent commercial behaviors in the silent period is achieved, power grid safety accidents caused by pursuit of short-term economic benefits can be effectively prevented, and the safe and stable operation capacity of a novel power system is improved.

The invention provides an overlay measurement method of a semiconductor, which comprises the following steps of: providing a wafer, arranging a plurality of areas on the wafer to form an array, arranging a product number pattern in each area, providing a system, arranging a data pool in the system, and storing a plurality of English letter patterns and number patterns in the data pool, english letter patterns and / or number patterns conforming to the product number patterns are found out from the data pool, the English letter patterns and / or the number patterns are spliced into an alignment mark pattern, and the alignment mark pattern is input into a system. And performing a first alignment step on the alignment mark pattern and each product number pattern on the wafer.

The invention relates to the field of coiled material film covering industrial control systems, in particular to a multi-station cooperative intelligent control system for synchronous film covering of multiple coiled materials. The obtaining module obtains a unified state fusion matrix; a multi-station cooperation module; decision generation is carried out on an execution mechanism for synchronous film covering of multiple coiled materials, a prediction control mechanism is embedded in the decision process to serve as constraint processing, and a fuzzy sliding mode control strategy is introduced; an execution and correction module; the system is used for receiving and executing a multi-station coordinated regulation instruction set. According to the method, a multi-agent collaborative decision framework is constructed based on De-POMDP, each station is set as an independent agent, organic combination of local state perception and global collaborative decision is realized, interaction and combinatorial optimization of regulation and control plans of each agent are realized through a shared data pool, an optimal plan combination is screened in combination with a joint reward function, and the optimization of the regulation and control plans is realized. And it is ensured that the regulation and control instruction can accurately meet the actual requirement for synchronous film covering of multiple coiled materials.

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.

The invention discloses an Internet of Things management system, and relates to the technical field of Internet of Things, and the system comprises a region division module which is configured to obtain a planting region division result based on the types of plants planted in an agricultural management section and land block division, and to create an agricultural management digital twinborn model based on the planting region division result; the sensor matrix construction module is configured to arrange a sensor matrix based on the planting area division result and access each sensor in the sensor matrix to the system; the equipment management module is configured to detect the running state and the manual control condition of each sensor in the sensor matrix, and if the running state is abnormal or abnormal manual control is carried out, equipment management alarm information is output; and the planting state detection module is configured to detect the plant state based on the sensor matrix to obtain a multi-modal planting data pool, screen the multi-modal planting data pool, delete invalid planting data information and retain valid planting data information. The method has the effect of improving the management efficiency of the Internet of Things in the agricultural field.

The invention relates to an electric power public opinion data driven work deployment adaptive optimization method and system, and the method comprises the following steps: S1, obtaining multi-source data related to electric power public opinions, filtering noise data, forming a standardized data set, and constructing a unified public opinion data pool; s2, constructing a public opinion knowledge graph, and obtaining a comprehensive public opinion risk index; s3, constructing a multi-target service deployment adaptive optimization model; s4, according to the multi-target service deployment adaptive optimization model, obtaining an optimal work deployment scheme based on a scheme solving and generating method of an adaptive optimization algorithm; and S5, according to the optimal work deployment scheme, pushing the optimal work deployment scheme to a decision maker through a visual billboard, tracking the whole execution process of the scheme, and feeding back an execution result and newly added public opinion data to the step S1. Through real-time public opinion analysis, high-risk events are automatically identified before event expansion, active intervention is realized, and a traditional passive complaint waiting mode is changed.

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 invention provides a semiconductor overlay measurement method, which comprises the following steps: providing a wafer, wherein the wafer comprises a plurality of regions arranged in an array, wherein each region contains a product number pattern, and providing a system, wherein the system comprises a data pool, a plurality of English letter patterns and numeral patterns are stored in the data pool, and finding out English letter patterns and / or the numeral patterns conforming to the product number patterns from the data pool, and splicing some English letter patterns and / or some numeral patterns into an alignment mark pattern, inputting the alignment mark pattern into the system, and performing a first overlay step on the alignment mark pattern and each product number pattern on the wafer.

The application relates to an RPA process design system based on a VS Code plug-in and belongs to the technical field of process automation. The system comprises a text editing module, a graphical parameter configuration module, an engineering management module and an execution adaptation module. The text editing module loads preset instruction libraries and method signature libraries, receives structured instructions, performs format checking and parameter completion, analyzes and generates a memory instruction sequence list; the graphical parameter configuration module listens to the instruction selection state, generates a parameter form, verifies the validity of input parameters, generates instruction-parameter data and stores the data into a to-be-processed data pool; the engineering management module generates a standard directory structure, performs version control and conflict detection; and the execution adaptation module extracts data and converts the data into an executable script and performs verification. The system can efficiently realize the design, management and execution of an RPA process, and improves the convenience and accuracy of robot process automation design.

The present invention relates to a method for selecting training data for training a deep learning model, the method comprising: with reference to a bipartite graph in which each of multiple training images stored in a data pool and individual types corresponding thereto are matched, performing a cycle of selecting some training images matching all of the individual types by repeating a process of (i) identifying, among the individual types, the number of corresponding individual types matching each of the training images, and selecting a specific training image having the largest number of corresponding individual types, and (ii) identifying, among individual types remaining after excluding the corresponding individual types, the number of the remaining corresponding individual types matching each of the training images, and selecting another specific training image having the largest number of the remaining corresponding individual types; and repeating the process of performing the cycle until n training images for training the deep learning model are selected from among training images remaining after excluding some training images selected from among the multiple training images.

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.

This invention discloses a method and system for assessing technical consulting risks based on the internet, relating to the field of data protection technology. It involves defining an enterprise data pool and establishing connections between the enterprise data pool and both the enterprise itself and the technical consulting platform. The enterprise sends consulting data to the technical consulting platform, which then provides suggested data based on the data. The consulting data and corresponding suggested data are stored in the enterprise data pool. A tracking set is set for the enterprise data pool to statistically analyze access information, including the processing results of accessed data (removal / destruction and normal access), resulting in a risk assessment index for the technical consulting. This invention ensures the security of enterprise-related data storage and also helps identify the cybersecurity risks faced by the enterprise during technical consulting.