28 results about "Big data cloud computing" patented technology

The invention relates to the technical field of urban geological survey, in particular to an urban geological survey method based on a big data cloud computing technology, which comprises the following steps: based on urban geological drilling detection data, land subsidence monitoring data and underground pipe network distribution data, grouping the data according to geographic grid units and time points, performing statistical analysis to obtain a geological parameter statistical set; and based on the geological parameter statistical set, setting multiple levels of storage partitions according to a data updating frequency, and establishing a geological data partition storage table. According to the method, urban geological data are grouped according to geographic grid units and time points, a multi-level storage partition mechanism is established, hierarchical storage and compression of the data are realized, the occupied storage space is reduced, and the data access speed is increased; key monitoring areas are screened based on a geological parameter change rate threshold, differential compression storage strategies are adopted for different areas, and the overall storage efficiency is optimized on the basis of guaranteeing important data integrity.

The invention discloses a network operation and maintenance method and system based on a digital twinning technology, and the method comprises the steps: collecting and analyzing various data in a real network based on big data, cloud computing and artificial intelligence technologies, and constructing digital twinning network models in one-to-one correspondence with the real network; various fault scenes are simulated in the digital twin network model, and the probability and position of fault occurrence are predicted through a machine learning algorithm; when a fault occurs in the real network, using the digital twin network model to locate the fault type and position; based on the fault type and position, calling a preset repair strategy library or generating a new repair scheme, and implementing repair through an automatic tool; based on network load prediction, user demand analysis and fault prediction results, simulation and optimization of a resource allocation scheme are carried out by using a digital twin network model, and the method is implemented in a real network. According to the invention, rapid response, accurate positioning and intelligent repair of network faults and dynamic optimal configuration of network resources can be realized.

The invention discloses an urban underground space comprehensive digital management system which comprises a digital management system body, and the digital management system body comprises a project application scene platform, a construction content platform, a project technical architecture platform and a technical route platform. The project application scene platform is based on urban underground space comprehensive digital management construction requirements, and the project is fused with an urban underground space business application scene through IOT, AI, big data, cloud computing, digital twinning, XR, GIS and BIM information technologies; according to the invention, IOT, AI, big data, cloud computing, digital twinning, XR, GIS and BIM information technologies are fused with an urban underground space business application scene, a digital twinning basic environment is established, a facility operation and maintenance platform, AI safety management and control, XR practical training simulation and a digital cockpit module are researched, developed and applied, and a virtuality and reality combined urban underground space full-scene digital operation and maintenance management platform is built. And the personnel safety guarantee service of people is met.

The invention relates to the technical field of data fusion, in particular to an efficient rural industry fusion development platform system which comprises a resource management module, a technical support and training module, an agricultural special product brand construction module, an industry planning module, a production management module, a marketing promotion module and a cooperation docking module. The resource management module is used for collecting, sorting, analyzing and deploying various resources involved in the rural industry development process; the technical support and training module analyzes agricultural production data, forms a collaborative development pattern by fusing agriculture, processing industry, service industry, popularization and other industrial fields in rural areas, can depend on big data, cloud computing and other modern information technology means, can accurately meet market demands, and has a good application prospect. The method finds a wider sale for local agricultural products, improves the added value of products, and can help farmers and enterprises to obtain market information in real time, optimize production decisions, reduce cost and increase income.

This invention discloses an intelligent analysis and decision-making system for agricultural finance using big data and cloud computing, belonging to the interdisciplinary field of agricultural finance and big data / cloud computing. The system includes a data acquisition module, a data preprocessing module, a cloud computing processing module, an intelligent analysis module, a decision generation module, a data storage module, a feedback optimization module, and an interactive display module. These modules work together to form a closed-loop process. The data acquisition module collects multi-dimensional data from the entire agricultural industry chain; the data preprocessing module constructs standardized datasets; the cloud computing processing module achieves efficient processing of massive amounts of data; the intelligent analysis module uses proprietary deep learning algorithms for multi-dimensional analysis; the decision generation module generates personalized decision-making solutions; and the feedback optimization module achieves adaptive optimization of the model and rules. This invention offers comprehensive data coverage, high processing efficiency, high analytical accuracy, and intelligent decision-making, overcoming the shortcomings of existing technologies and can be widely applied to various agricultural finance scenarios.

The invention provides a management method, device, system and equipment for a big data cluster and a medium, and relates to the technical field of artificial intelligence, in particular to the technical fields of intelligent cloud, big data, cloud computing and the like. The big data cluster-oriented management method is applied to a unified platform, the unified platform is connected with a plurality of candidate big data clusters, and the method comprises the following steps: intercepting a management request for a target big data cluster in the plurality of candidate big data clusters; the management request comprises identification information of the target big data cluster; obtaining metadata of the target big data cluster based on the identification information; and based on the metadata, managing the target big data cluster.

The present invention belongs to the fields of computer three-dimensional reconstruction, computer vision, big data cloud computing, etc. Specifically, it relates to a hierarchical three-dimensional reconstruction method based on an auto-decoder, which solves the problems that the current mainstream deep learning-based three-dimensional reconstruction technologies cannot reconstruct hierarchical structures, have internal structure losses, or have low internal structure reconstruction accuracy. The hierarchical shape representation and auto-decoder framework adopted by the present invention improve the convolutional neural network to extract data features, learn the relationship between two points in space in the auto-decoder framework, predict the signed distance value of the nearest surface of any spatial point in the neural signed distance field, and show good performance in accurately reconstructing three-dimensional shapes with internal structures. The method of the present invention designs a new mapping relationship, improves the ability of the neural network model to learn hierarchical structures, and enables the neural network model to more effectively learn from data and reconstruct three-dimensional shapes with internal structures.

A closed-loop joint design method for power batteries and their battery management systems is proposed. This method utilizes big data cloud computing and combines digital modeling and parameter joint evaluation and updating to achieve a high-quality closed-loop optimization design that organically integrates power battery design and battery management system design. Through a "know-before-building" approach, a battery with "know-before-testing" capabilities is ultimately obtained. A complete parameter set for the battery to be designed is formed by combining big data cloud computing and battery design parameters. Simulation experiments are then conducted to obtain matching battery characteristic responses, which are used for modeling and developing management algorithms for the battery management system. This results in the manufacture of a digital battery with optimal performance and management indicators, which is then materialized to design a high-performance, well-managed "new battery," achieving the goal of "building for use."

The present application relates to the technical field of heat supply system equipment verification, in particular to a flow meter group online verification and heat correction method based on normalization principle. The present application aims at the problem in the prior art that flow meter group measurement data is not closed, only single equipment can be calibrated on site, and heat supply system cannot stop running for checking the performance of flow sensor, so that the flow sensor working condition cannot be verified without stopping heat supply. The present application provides a flow meter group online verification and heat correction method based on normalization principle, uses internet and big data cloud computing technology to analyze flow meter group, determines normalization coefficient, and unifies group flow sensor measurement data to total heat source flow as a reference flow. Through the calculation method of flow ratio change rate, the judgment basis of online verification is determined, and cloud diagnosis of flow sensor working condition without stopping heat supply is realized and the fault position is determined.

The invention discloses a supply chain finance digital processing system and method, and belongs to the technical field of digital processing, and the system comprises a data collection module, a data processing module, a risk assessment module, a financing matching module, and an execution and monitoring module. According to the method, quantifiable enterprise digital level evaluation indexes are constructed through word frequency statistics and logarithmic processing, and a data basis is provided for subsequent analysis; a fixed effect regression model is adopted, the influence of digital transformation on the financial level of the supply chain is scientifically analyzed, and empirical support is provided for enterprise decision making; dimensional regression analysis is carried out on digital technology dimensions such as artificial intelligence, big data, cloud computing and block chains, specific influences of all technology paths on supply chain finance are identified, and accurate investment of enterprises is assisted. The enterprise credit risk is dynamically evaluated based on real-time data and model output, and conversion from static evaluation to dynamic monitoring is realized.

The invention provides a zoonosis risk prediction method coupling climatic factor time sequence characteristics and land utilization space landscape characteristics under the background of climate and land utilization change, and relates to the technical field of zoonosis space epidemiology. According to the method, land utilization types such as buildings, roads, greenbelts and water bodies are identified through high-resolution remote sensing, landscape features are constructed, weather time sequences such as air temperature, rainfall and humidity are obtained by combining a geographic space big data cloud computing platform, and climate features are extracted by adopting a long-short-term memory network; and meanwhile, spatial population distribution characteristics are formed by utilizing the population data set. And based on the multi-source spatial-temporal characteristics, constructing a multilayer perceptron to simulate and predict the propagation rate. A true propagation rate is calculated based on the case matrix and the susceptible-infected-restorer epidemiological model. And calculating a loss function of the predicted propagation rate and the real propagation rate to optimize deep learning network parameters, and realizing risk dynamic assessment of the coupling space-time propagation mechanism.

The application provides a distributed database processing method, device, equipment, medium and product, and is applied to technical fields such as big data and cloud computing. The method is applied to a batch running device corresponding to any database in a distributed database, the distributed database comprises a plurality of databases, and the method comprises the following steps: receiving a job stop instruction sent by a central control device; in response to the job stop instruction, controlling the database corresponding to the batch running device to store data and stop a batch processing task; and if it is determined that the database stops the job, sending a stop running result of the database to the central control device. The method of the application improves the control efficiency of the distributed database by automatically stopping the control of the database.

1. The name of this design product: Hard disk server (2U25 disk 2.5 inches). 2. Purpose of this design product: A server with a front-mounted 2.5-inch hot-swappable hard drive, primarily used in big data, cloud computing, artificial intelligence, the Internet of Things, and other fields. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

1. The name of the design product: hard disk GPU server. 2. The use of the design product: a front-mounted 2.5-inch hot-swap hard disk server, mainly used in big data, cloud computing, artificial intelligence, Internet of Things and other fields. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention relates to the technical field of communication engineering, in particular to a communication industry-oriented intelligent maintenance method for a line pipe network, which comprises the following steps of: constructing a line pipe network topology digital twinborn model by utilizing a computer visualization three-dimensional technology, three-dimensional GIS (Geographic Information System) combination, a big data cloud computing technology and a topology theory, acquiring basic data of the line pipe network, and establishing a communication network topology digital twinborn model; comprising planar graph data, equipment data, pipeline data and the like; secondly, constructing a topological digital twinborn model, abstracting physical entities of the line pipe network into topological space elements, establishing a connection relation matrix, and calculating topological characteristic values; an interactive system based on the augmented reality technology is constructed, and fusion of the virtual model and the real environment is achieved; topological distance measurement calculation is executed, and a comprehensive evaluation distance is generated in combination with the physical distance and the topological distance; carrying out line pipe network capacity evaluation and optimization, identifying bottleneck points and providing optimization suggestions; implementation of a mobile terminal and a PC terminal is supported, an equipment information change management mechanism is provided, and the precision and efficiency of line pipe network maintenance are improved.

The present invention discloses a method for constructing a smart marine ranch on uninhabited islands and reefs, which relates to the field of marine aquaculture technology. The method generates electricity by comprehensively utilizing wind, solar, and ocean energy to provide power for aquaculture, management, and monitoring systems. Surplus electricity is used to electrolyze seawater to produce hydrogen, and hydrogen batteries are manufactured for energy storage. A robust energy supply is achieved by constructing an energy allocation system. Sustainable fishing of fish resources is achieved by creating fish habitats. A robust supply of clean energy is achieved through an energy storage, transmission, and supply network. Automation and intelligence of fish farming are achieved by combining intelligent lifting cages with open water surfaces. Unmanned operation and management are achieved through big data, cloud computing, and the Internet of Things.

1. The name of the design product: hard disk GPU server. 2. The use of the design product: a front-mounted 3.5-inch hot-swap hard disk server, mainly used in big data, cloud computing, artificial intelligence, Internet of Things and other fields. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

1. The name of the design product: hard disk server. 2. The use of the design product: a front-mounted 2.5-inch hot-swap hard disk server, mainly used in big data, cloud computing, artificial intelligence, Internet of Things and other fields. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The disclosure provides a multi-cluster load balancing method and device, electronic equipment and computer readable storage medium, relates to the technical field of computers, and can be applied to big data, cloud computing and intelligent supply chain. The method comprises the following steps: selecting one from a plurality of sub-clusters as a master cluster, collecting service endpoint information of the master cluster and the sub-clusters through the master cluster, and generating a cluster internal load balancing rule and a multi-cluster load balancing rule according to the service endpoint information, and forwarding data based on an extended Berkeley packet filter technology; and the embodiment of the disclosure can support multi-cluster services.

1. The name of the design product: hard disk server. 2. The use of the design product: a front-mounted 3.5-inch hot-swap hard disk server, mainly used in big data, cloud computing, artificial intelligence, Internet of Things and other fields. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention provides an abnormal data source identification method and device, equipment, a medium and a product, and relates to the technical field of artificial intelligence, in particular to the technical fields of big data, cloud computing and the like. The abnormal data source identification method comprises: obtaining a knowledge graph corresponding to target data, the knowledge graph comprising a plurality of entities, the plurality of entities being in one-to-one correspondence with a plurality of data sources of the target data, and the plurality of entities being divided into a plurality of levels based on an inclusion relationship; on the basis of the abnormal information of the target data, obtaining an abnormal entity in the current entity of the current hierarchy in the plurality of hierarchies; and determining an abnormal data source in the plurality of data sources based on the abnormal entity.

The application discloses a big data-based intelligent collection and detection method and system for urban water supply data, and a big data-based intelligent collection and detection system for urban water supply data, which comprises a city water supply main pipe, intelligent water meters, a city water supply big data cloud computing platform and water meter user terminals, the city water supply main pipe is used for comprehensive water supply of all users in the city, and the intelligent water meters are installed on the pipelines of all user nodes of the city water supply main pipe; the intelligent water meters comprise a water supply data acquisition module, a multi-mode satellite positioning module, a time sequence synchronization module and a data encryption module; in the application, the intelligent water meters are used for comprehensive acquisition of water supply data on all user branch pipes of the city water supply pipeline, and the acquisition position positioning data and time synchronization records are bound during the water supply data acquisition process, wherein the acquisition of the positioning data can be positioned through multiple satellite positioning modes, including GPS positioning, Beidou positioning and QZSS positioning, so that the accuracy and safety of the positioning data are ensured.

The embodiment of the application relates to the elevator operation and maintenance technical field, and discloses an elevator intelligent supervision and risk monitoring and early warning system, the system comprises: an elevator intelligent supervision platform composed of a data cloud processing module and a maintenance quality examination module, and an elevator risk monitoring platform composed of an early warning and prevention module and a linkage disposal module; the data cloud processing module is used for regularly collecting basic information, basic operation data and historical fault data of all elevators accessed to the system, and performing data mining, big data cloud computing and statistical analysis to obtain analysis indexes; the maintenance quality examination module is used for performing maintenance quality examination based on the analysis indexes; the early warning and prevention module is used for performing early warning analysis on each elevator based on the examination results, and judging whether to perform early warning publishing; and the linkage disposal module is used for publishing early warning information to the elevators needing to perform early warning publishing, and contacting corresponding management departments to perform linkage disposal. The system greatly reduces the occurrence probability of elevator accidents.

Traditional tools have serious bottlenecks in six fields such as AI, big data, cloud computing, block chains, green energy-saving computing and high-end software platforms. The reason is that a traditional method and tool depends on a formal system, axiom reasoning and language symbols are subjective mapping space, the indirect logic deduction mode depends on exhaustion, local splicing, iterative superposition and other modes when facing direct complex problems, serious bottlenecks exist in efficiency, accuracy and computing power resource consumption, and the MSA serves as a front-end structure judgment module, so that the accuracy of the system is greatly improved. Corresponding tools are matched according to the structure types, and the situation that various resources are wasted seriously, such as low efficiency and energy consumption, caused by current blind calculation or blind identification is changed. The system can be embedded into various software and system platforms, can quickly judge whether proposition / system logic has a coupling structure and a structure type or not, can match tools according to the type, can reduce blind calculation, trial and error and resource waste of exhaustion on each platform, can provide dynamic convergence feedback response, and can promote the operation efficiency to be greatly improved.

The invention belongs to the field of water and fertilizer monitoring, relates to a data analysis technology, is used for solving the problem that abnormal data in monitoring data cannot be identified and extracted due to the lack of a process of verifying the monitoring data in the prior art, and particularly relates to a crop water and fertilizer intelligent monitoring method, system and device based on big data cloud computing. Comprising a water and fertilizer monitoring module, a depth analysis module and a data cleaning module which are in communication connection in sequence, the water and fertilizer monitoring module and the data cleaning module are both in communication connection with a water and fertilizer irrigation control module, and the water and fertilizer monitoring module, the depth analysis module and the water and fertilizer irrigation control module are all in communication connection with a database; abnormal values in the data collected by the sensor can be automatically recognized and cleaned, wrong data are prevented from influencing irrigation decision, monitoring point data deviating from a normal range are accurately eliminated through variance threshold judgment and an iterative cleaning algorithm, and the accuracy of irrigation is improved. And the reliability of data participating in decision calculation is ensured, and a clear guidance direction is provided for subsequent maintenance and system optimization.

The invention relates to the technical field, in particular to a hydropower station management method and system based on data analysis, equipment and a medium, and the method mainly comprises the steps that a hydropower station fault diagnosis model is established, diagnosis is carried out through the hydropower station fault diagnosis model according to basic data, and a hydropower station fault diagnosis result is obtained. The robot control end obtains all fault devices corresponding to the data set based on the received fault data set, scores based on fault levels and distance information, sorts scores, outputs a checking sequence of the robot for the fault devices according to the sorting, and generates a robot walking path through the checking sequence; and when the robot arrives at the fault equipment, checking through the neural network image recognition model, and if the checking is correct, continuing to obtain the state data of the equipment. According to the method, research and innovation in the aspects of big data, cloud computing, Internet of Things, MR technology and the like are adopted, and reference is provided for intelligent management of the hydropower station industry.

The present invention belongs to the field of smart heating technology, and in particular relates to a smart heating resource allocation system and method based on electricity data analysis. The system includes an electricity collection system, an electricity big data cloud computing platform and a display; the electricity collection system includes an electricity data collector, an electricity meter and an electric carrier transmission line for collecting user electricity consumption information; the electricity big data cloud computing platform includes a system function of collecting user information, user electricity consumption information, etc., to realize the optimal allocation of heating resources, transmit the calculation results to the display, and provide heating resource allocation analysis results; the display is used to realize human-computer interaction, display and query functions. The present invention combines the division of heating areas, cleans the electricity industry expansion application information, conducts logical judgment, and pushes the information of new users predicted to be for heating to the heating enterprise, optimizes the allocation of heating resources, and promotes heating services such as the business handling of heat users by heating enterprises.

The embodiment of the application provides a counting method, a user base counting method, corresponding devices and electronic equipment, relates to technical fields of big data, cloud computing, block chains and the like, and when the method counts based on target data by using a HyperLogLog algorithm, each time new target data is written, a preset total kmax value harmonic parameter is dynamically updated according to an old value and a new value of a storage container changed by writing, that is, a first kmax value and a second kmax value, when counting is read, the preset total kmax value harmonic parameter can be directly used to determine a counting result, an accumulation process is avoided, processing time is effectively reduced, and the performance of the HyperLogLog algorithm in a real-time counting scene is greatly improved.