41 results about "Big data cloud computing" patented technology

The invention provides a smart park optimization method and device based on digital twinning and BIM, and the method comprises the steps: collecting the actual information of a park, and carrying out the preprocessing; based on the configured basic BIM model, integrating the basic BIM model by using actual information; constructing a three-dimensional digital twinborn model, and fusing the integrated BIM model with the three-dimensional digital twinborn model; and analyzing and processing the park operation data by using the current fused three-digit digital twinborn model so as to realize park optimization. The digital mirror image of the smart park is constructed by integrating digital twinning, BIM, Internet of Things, big data, cloud computing, GIS and other frontier technologies, and comprehensive perception, accurate analysis, intelligent decision and efficient management of the park are realized. According to the scheme, the intelligent level, the operation efficiency and the sustainable development capability of the park are improved, and convenient, efficient and intelligent service experience is provided for park managers, settled enterprises and individual users.

The invention discloses a green power computing power fusion scheduling model construction method and a scheduling platform, and relates to the technical field of green computing power. The green power computing power fusion scheduling model construction method comprises the steps of determining an adjustable reference load, determining a peak clipping and valley filling optimization load, determining a clean energy output load, determining a computing power center computing power task execution optimal load curve, monitoring a computing power center fusion scheduling task execution process and the like. A green electric power computing power fusion scheduling platform is constructed, and fusion scheduling task execution responsivity monitoring, computing power center electricity charge economy analysis, computing power center carbon emission reduction analysis and the like of a computing power center are carried out. The beneficial effects of the invention are that the system employs the technologies of big data, cloud computing and the like, builds a green power calculation power scheduling platform, monitors and analyzes the clean energy supply and demand conditions of the calculation power centers of the whole province, provides calculation power task scheduling services for the calculation power centers of the whole province, integrates clean energy consumption and green power calculation power, and improves the efficiency of the whole province. And the calculation power load active matching clean energy supply is realized.

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.

A task processing method and apparatus, and a device, a storage medium and a program product, which are applied to task processing scenarios such as artificial intelligence, big data and cloud computing. The task processing method comprises: on the basis of task prompt information corresponding to a target task in a service scenario, generating a system interaction parameter corresponding to the target task, wherein the task prompt information is used for describing the target task in the form of natural language, and the system interaction parameter is used for describing the target task in the form of a machine instruction; calling a task system of the target task, and processing the system interaction parameter; and on the basis of output information of the task system of the target task regarding the system interaction parameter, determining a processing result corresponding to the target task.

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 a generative adversarial network-based point cloud reconstruction method, and belongs to the technical fields of computer three-dimensional reconstruction, computer vision, big data cloud computing and the like. Aiming at the problems of how to improve the point cloud reconstruction learning capability of a network architecture and how to reduce the training complexity of the network architecture in the existing point cloud reconstruction method, the method adopts a generative adversarial network as a main body framework, and designs a generator to map a point cloud from a feature space to a signed distance field; and constructing a discriminator to optimize a global gradient and a spatial point signed distance prediction value so as to improve the detail precision of reconstruction. The generator directly generates an initial shape from the point cloud data, and the discriminator optimizes the implicit field and gradient predicted by the generator to improve the quality of the final shape. According to the method, the generative adversarial network framework is improved, the point cloud reconstruction capability of the neural network model is enhanced, and the neural network model can more effectively reconstruct a correct and fine topological curved surface from the point cloud data.

The invention relates to the technical field of talent management, and discloses a talent multi-dimensional analysis application system which comprises a data layer, a business logic layer and a presentation layer. Wherein the data layer is used for collecting and storing talent data; the business logic layer is connected with the presentation layer and the data layer, and is used for responding to a user service request transmitted by the presentation layer, processing the user service request under the cooperation of the data layer, and returning a processed result to the presentation layer; and the presentation layer is used for visually presenting the service information so as to interact with the user, and presenting the talent multi-dimensional analysis information after interaction. According to the method, technologies of big data, cloud computing, artificial intelligence and the like are utilized, comprehensive, accurate and timely analysis of talent data can be digitally realized, the data management efficiency is improved, the processing and analysis requirements of the talent data are met, and powerful support is provided for leader decision making of enterprises and talent data application of professional departments and grassroots units.

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.

The invention relates to the technical field of electric power systems, Internet of Things and computing, in particular to an electric power Internet of Things safety protection technology based on an electric Hongyu Internet of Things operating system. According to the invention, an efficient, intelligent and safe power Internet of Things data processing flow is constructed by integrating advanced technical means of the Internet of Things, big data, cloud computing, edge computing, artificial intelligence and the like, so that the security of the power Internet of Things is comprehensively improved. As the core of the technology, the electric Hongyu Internet of Things operating system not only provides strong data processing and communication capabilities, but also has abundant safety functions and mechanisms, and can realize comprehensive safety protection of the electric power Internet of Things. The invention aims to solve the security challenge faced by the power internet of things, and provides powerful guarantee for stable operation and power supply security of a power system by introducing an advanced technical means and an innovative security protection strategy.

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 invention relates to the technical field of big data cloud computing, in particular to a computing method of a computing network arrangement scheduling strategy based on a multi-factor algorithm. By fully knowing user requirements and providing personalized resource schemes, actual requirements of users are met, and user experience and satisfaction are improved. Through an accurate resource allocation algorithm, calculation, network, storage and other resources are reasonably allocated according to actual demands, the resource utilization rate is improved, and resource waste is reduced. The automatic resource allocation process reduces the necessity of manual intervention, improves the resource allocation efficiency, and accelerates the resource supply speed. Intelligent scheduling is carried out according to user demands and a resource allocation algorithm, so that waste caused by excess or insufficient resources is avoided, and resource purchase and maintenance costs are reduced. According to the preference and preference of the user, a plurality of recommendation schemes suitable for the user are generated, more choices are provided for the user, the final scheme is determined according to secondary screening of the user, and personalized requirements are met.

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.

The invention discloses a cloud service operation and maintenance management platform for an intelligent mining area, and the platform comprises the steps: system carding and analysis: carrying out the comprehensive carding and analysis of an existing production management system, a safety monitoring system, a geological exploration system and an equipment management system of the mining area, determining the functions and characteristics of all systems, finding out the generality and difference, and laying a foundation for the subsequent integration; data integration and platform establishment: big data and cloud computing technologies are adopted, a unified data platform is established, data intercommunication and sharing among systems are realized, and a complete data resource pool is formed; the system has the beneficial effects that comprehensive integration and sharing of various information systems and data resources in the mining area are realized, and the data utilization efficiency is improved; real-time and comprehensive information support is provided for mining area managers, and scientificity and accuracy of decision making are improved; and through automatic and intelligent management, the production efficiency is improved, and the operation cost is reduced.

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.

A method for vehicle lane changing control, a device, a storage medium and a program product, relating to the fields of autonomous driving, intelligent transportation, big data, cloud computing, etc. in computer technology. A specific implementation solution is: during a process of vehicle driving, when a distance between the vehicle and a front bifurcation is less than or equal to a longest lane changing operation distance, according to map data and traffic data of a road section ahead, it is determined whether a current position is a preferred position to start performing the lane changing operation, so as to determine whether performing the lane changing operation at the current position; and if determined to perform the lane changing operation at the current position, lane changing operation execution information will be issued to enable the vehicle to start performing the lane changing operation at the current position.

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.

The present invention discloses a road friction coefficient evaluation and prediction method, comprising the following steps: S1, collecting vehicle and environment information; S2, analyzing and evaluating basic friction coefficient data; S3, measuring road friction coefficient; S4, evaluating friction coefficient big data; S5, predicting friction coefficient warning. The present invention can obtain lane-level real-time road friction coefficient evaluation values ​​accurate to complex environments, which is closer to the actual situation, and constructs a road friction coefficient Internet of Vehicles system including an on-board terminal, a big data cloud computing processing platform, and a road friction coefficient warning platform, which can be used for real-time road friction coefficient warnings for road vehicles and provide medium- and long-term solutions.

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.

Provided is a method for processing an oracle region cache, an electronic device and a storage medium, relating to the field of data processing technology, and in particular to the fields of big data, cloud computing, distributed database, intelligent search and other technologies. The method includes: obtaining a benefit parameter of a region to be processed, wherein the benefit parameter is used to represent a difference between benefit and cost of setting the region to be processed in the oracle region cache; and selecting the region to be processed to update the oracle region cache when the benefit parameter of the region to be processed meets a target condition.