36 results about "Cloud computing big data" patented technology

The invention provides an intelligent conference management system, method and device based on a large model and a medium, and belongs to the technical field of computers. The system comprises a pre-meeting module used for automatically generating and sending a meeting invitation through an integrated enterprise schedule system, using telephone notification and IVR interaction to confirm a meeting participation form, and counting a meeting participation condition and a meeting arrival state; the in-conference module is used for collecting multi-source audios based on a microphone array in the conference process, transferring conference content in real time and generating subtitles in combination with a voice recognition technology, synchronously recording videos, recording process information of the conference, and performing structured analysis; and the post-conference module is used for extracting key indication information and backlogs in the process information of the conference, generating a standardized conference summary, automatically distributing tasks, tracking the progress and supporting semantic retrieval of historical conference records. According to the invention, a large model technology is utilized, and technologies of cloud computing, big data, artificial intelligence and the like are combined, so that automatic management of the whole conference process is realized.

The utility model belongs to the technical field of servers, and discloses a cloud computing big data storage server which comprises a cabinet body, a memory placing space located in the cabinet body and a hard disk quick pop-up structure assembled in the memory placing space, and the hard disk quick pop-up structure comprises a partition plate and a hard disk support located on the left side of the partition plate. By means of the design of the hard disk rapid pop-up structure, the installation process of the hard disk is greatly simplified, a user only needs to place the hard disk on the inner wall of the hard disk support and then push the hard disk support, the action can promote the movable plate to move backwards, meanwhile, the sliding blocks smoothly move in the sliding grooves, and the hard disk can be rapidly popped up. In the moving process of the sliding block, the stopping block and the limiting plate act together to limit the moving path of the sliding block, and the sliding block gradually slides into the clamping groove along with the continuation of the pushing action, so that the positions of the movable plate and the hard disk bracket are locked, and rapid installation of the hard disk is realized.

The invention relates to the field of cloud computing big data secure storage, in particular to a cloud computing-based big data secure storage method and system. The method comprises the following steps: performing encryption and fragmentation processing on an original data stream to generate a fragmentation sequence with an index; generating a dynamic key sequence and a verification key group based on the fragment information; constructing a secure transmission channel through a quantum key distribution mechanism in combination with a geographic position weight and a load balancing strategy, and realizing encapsulation and transmission of a secure fragment packet; node state monitoring and redundancy management are implemented, and an update mapping table is generated; and finally, a complete data stream is recovered through fragment positioning retrieval, data recombination and dynamic decryption. According to the invention, the security and reliability of data storage and the overall load balancing capability of the system are improved.

The invention provides a personnel passing verification method and system based on space-time rule dynamic matching. The method comprises the following steps: identifying the identity of a passing person to obtain an identity label; acquiring current spatio-temporal information including current time and current position; according to the current spatio-temporal information, dynamic matching is carried out from a preset spatio-temporal rule set, a dynamic permission group is generated, and the rule set comprises a permission group template formed by combining authorization rules of a time dimension and authorization rules of a space dimension; and judging the passing authority according to the identity label and the dynamic authority group and outputting a decision result. The system comprises an identity recognition module, a spatio-temporal information acquisition module, an authority verification and decision module and an execution module which are used for executing the method. The invention relates to the technical field of personnel access verification, and in addition, the invention also relates to the field of Internet, cloud computing and big data service.

The invention relates to internet data services such as databases and cloud database services, more particularly belongs to the field of internet and cloud computing and big data services, and particularly relates to a method and a system for realizing intelligent sorting of data assets based on a self-research algorithm, which comprises the following steps of: constructing an intelligent sorting link of a target database; generating an initial field description set of the target database through a field description generation module of the intelligent carding link; according to the semantic consistency consensus level and the field description accuracy of the initial field description set, establishing a quality evaluation matrix corresponding to the intelligent carding link; according to the quality evaluation matrix, reconstructing a dynamic increment Schema processing mechanism corresponding to the intelligent carding link; and performing vectorization storage on the data assets finally confirmed by the dynamic increment Schema processing mechanism to complete data asset sorting of the business system to be sorted. According to the method, the storage result of the data assets can be ensured, and database Schema dynamic change in a complex business scene can be accurately adapted.

The present disclosure provides a commonality extraction method and device of an object, electronic equipment and storage medium, which can be applied to the fields of cloud computing, big data, intelligent supply chain, etc. The method comprises: obtaining a plurality of to-be-processed objects, and querying and obtaining dimension values of each to-be-processed object under a plurality of to-be-analyzed dimensions; determining a plurality of dimension groups according to the plurality of to-be-analyzed dimensions; determining a dimension value group under each dimension group and a co-occurrence probability of the dimension value group according to the dimension values of each to-be-processed object under each to-be-analyzed dimension; constructing a dimension tree based on the dimension value group and the co-occurrence probability thereof; and determining a commonality dimension result of the plurality of to-be-processed objects according to the dimension tree. The method can statistically process the dimension values under a plurality of dimensions, quickly determine the dimension value group that can be retained as the commonality dimension result of the to-be-processed objects by constructing the dimension tree, and achieve the effect of efficiently and accurately extracting commonality.

The present disclosure provides a cycle length determination method, device and equipment of traffic signal light and storage medium, relates to the fields of intelligent transportation, cloud computing, big data and the like. The specific implementation scheme is: according to the coordinate position and time stamp of each trajectory point in the target moving trajectory of a plurality of target objects in a set period, determine the switching point in the plurality of target moving trajectories;Wherein, the switching point is the trajectory point of the target object switching from the stop state to the moving state;According to the difference between the time stamp of each switching point, the cycle length of the traffic signal light is determined. Therefore, according to a large number of moving trajectories, the cycle length of the traffic signal light can be determined, which can improve the accuracy and reliability of the determination result.

The invention provides a data volume migration method which can be applied to the technical field of cloud computing and big data. The method comprises the following steps: in response to a data volume migration instruction, carrying out standardized identification on a demand document; based on the standardized recognition result, mapping the content of the demand document to a test scheme target chapter; generating chapter content according to the mapping result and the target chapter type; and generating a target test scheme based on the chapter content, wherein the standardized identification of the demand document comprises the following steps: determining a target keyword based on a preset rule or a historical reference statistical result; and if it is determined that the demand document contains the target keyword, determining that the demand document is a standardized document. The invention further provides a data volume migration device and equipment, a storage medium and a program product.

The invention relates to the technical field of operation and maintenance management in a distributed computing environment, and discloses a federated multi-cluster unified log and monitoring method and system, and the method comprises a log data collection module, a feature analysis module, a monitoring strategy generation module and a monitoring execution module. The log data acquisition module acquires historical log records, performance indexes and abnormal event data through a cross-cluster interface; the feature analysis module performs cleaning and feature extraction on the data by adopting a time window method and quantile calculation to generate log, performance and abnormal feature parameters; the monitoring strategy generation module performs strategy retrieval based on the knowledge graph space, and dynamically matches an optimal monitoring strategy; and the monitoring execution module carries out monitoring according to the strategy and analyzes the result. Uniform collection, intelligent feature analysis and self-adaptive monitoring of log data in a multi-cluster environment are achieved, the operation and maintenance efficiency and monitoring precision of a cross-cluster system are improved, and the method is suitable for distributed scenes such as cloud computing and big data.

The invention relates to the technical field of servers, and discloses a cold and hot data management method and system and a server. According to the method, data access behavior information is obtained, multi-dimensional prediction and business rule collaborative decision are combined, a data storage scheduling strategy is generated, and corresponding storage resource allocation operation is executed. According to the invention, the utilization efficiency of storage resources can be improved, the response capability of the system to high-value data is enhanced, and the compliance and safety requirements under the scenes of finance, e-commerce and the like are met. The method can be widely applied to cloud computing, big data platforms and distributed storage systems.

The invention relates to the technical field of operating systems, in particular to a method and a system for desensitizing file data of an operating system, which comprises the following steps of: in an operating system kernel, searching a kernel function table based on kprobe, replacing an openat system call function by registering an ftracefunction function, and executing a desensitization process on the file data of the operating system; constructing and storing an ftrace hook data structure containing a function name, a new function address, an original function address and ftrace operation information; the method has the beneficial effects that a file data desensitization security module is realized, file name capital writing, file data reverse-order writing and user keyword star number display are supported, a data desensitization function on an operating system level is realized, the security of the operating system is improved, and higher security capability is provided for applications such as cloud computing, big data and databases.

The present disclosure provides a method, apparatus, device, and medium for obtaining live videos, which relates to the field of artificial intelligence, and particularly relates to technical fields such as natural language processing, cloud computing, big data, computer vision, speech technology, and deep learning. The specific implementation solution is as follows: in response to a viewing request initiated by a first user, obtain a first video indicated by the viewing request; wherein, the sign language identifier in the viewing request represents whether the sign language recognition function is enabled; the status identifier of the first video represents whether the first video is a composite video in a co-hosting state; if it is determined that the sign language identifier represents that the sign language recognition function is enabled and the status identifier represents that the first video is a composite video in a co-hosting state, then convert the voice information in the first video into sign language information, and obtain and push the first video with sign language information to the first user. Through the above method, the first user can accurately obtain the effective information in the first video in the co-hosting state, improving the viewing experience of the first user.

The invention provides a security group configuration method and device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of cloud computing big data, and the method comprises the following steps: detecting security group update information corresponding to a current service session; wherein the security group update information is the same as the communication metadata of the current service session; determining a current security group flow table corresponding to the current service session in a preset information set based on the security group update information; wherein the preset information set comprises a corresponding relationship between the communication metadata of each service session and the corresponding security group flow table; and deleting the current security group flow table, and configuring the security group update information based on the current service session.

The invention discloses a non-exchange data center internal network architecture based on free space optical communication, and belongs to the technical field of optical communication and data center networks. According to the framework, a server with a tunable light emitter, a light distribution beam combiner and the like are arranged in a cabinet, and non-line-of-sight communication is realized through a spatial light element; all-optical routing is completed through wavelength, spatial mode and time slot joint multiplexing, and a traditional electric / optical switch is not needed. The optical path routing in the architecture adopts passive devices, optical fiber wiring is avoided, online expansion of a cabinet and a server is supported, deployment and energy consumption cost is remarkably reduced, high-bandwidth, low-delay and low-power-consumption green data center communication is achieved, and the system can be widely applied to scenes such as cloud computing and big data.

The invention relates to the technical field of Internet, cloud computing and big data service, and discloses a staff education platform system integration method and system, and the method comprises the steps: obtaining a real-time behavior event set of a platform user, extracting a cross-behavior association rule, and generating a dynamic feature vector group; collecting interface response time delay parameters and load state data of each subsystem, and constructing a resource state topological graph; superposing the dynamic feature vector group on a resource state topological graph in a time dimension, marking a space-time mismatching region of resource distribution and demand density, and generating a demand overflow coordinate set; if the resource gap density of the demand overflow coordinate set exceeds a preset system pressure-bearing threshold value, dynamically adjusting default service priorities among subsystems, and constructing a dynamic compensation channel of the staff education platform; and configuring the dynamic compensation channel to the platform integrated gateway, outputting a resource redistribution instruction set, and executing cross-system deployment verification. According to the invention, the integration efficiency of the staff education platform system can be improved.

The invention relates to the technical field of cloud computing, big data analysis and intelligent recommendation, and provides a smart tourism information analysis and push system and method based on a cloud platform, and the method comprises the steps: obtaining information data of scenic spots, carrying out the type division of the scenic spots, providing user login entrances, obtaining classification data according to the intention of a user, and carrying out the classification of the classification data. Dividing scenic spot information based on the scenic spot recommendation value; obtaining browsing data of a user; processing and analyzing to obtain a browsing interest value; analyzing and processing to obtain a priority sorting value based on the scenic spot recommendation value and the browsing interest value; and adjusting a scenic spot recommendation threshold value and the like to provide more personalized recommendation services conforming to user preferences, so that quick search is facilitated, and the experience feeling is improved.

The utility model relates to the technical field of data storage, in particular to a cloud computing big data storage device which comprises a main hard disk and an expansion hard disk box, an expansion slot is arranged on the main hard disk, an expansion hard disk box plug is arranged on the expansion hard disk box, and the main hard disk and the expansion hard disk box are mutually connected through the expansion slot and the expansion hard disk box plug. An expansion hard disk groove is formed in the expansion hard disk box and used for installing a newly-added hard disk for expansion, the main hard disk comprises a main hard disk plug, an expansion slot and an expansion hard disk guardrail, the main hard disk is of a box body structure with a built-in hard disk, and the main hard disk plug is a protruding plug arranged on one side face. According to the cloud computing big data storage device, the requirement for capacity expansion of the hard disks in the main hard disk is met by arranging the external expansion hard disk box which can be quickly disassembled and assembled on the main hard disk, meanwhile, a composite connecting socket is adopted in the external expansion box, expansion hard disks of different models can be adapted, and the cloud computing big data storage device has very high use flexibility.

The invention relates to the field of wireless, core network, transmission and bearing and cloud computing big data edge computing, and provides a data transmission bottleneck positioning method and device, a terminal, equipment and a product. The method is applied to a first terminal and comprises the following steps: acquiring a maximum read rate and a real-time available read rate of first storage equipment in the first terminal; based on a transmission protocol, sending a network detection message to a second terminal through the intermediate transmission node, and receiving a network bandwidth feedback message sent by the second terminal and a write rate feedback message of a second storage device sent by the second terminal; and when the data sending rate of the first terminal is greater than the network transmission bottleneck rate, determining a data transmission bottleneck position based on the maximum read rate, the real-time available read rate, the maximum write rate, the real-time available write rate and the network transmission bottleneck rate. According to the data transmission bottleneck positioning method provided by the invention, the data transmission bottleneck point can be quickly determined.

1. The name of this design product: All-in-one training machine (all-in-one training machine for pre-shift meetings). 2. Purpose of this design product: Equipment used for cloud computing and big data processing. 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: three-dimensional picture.

This invention belongs to the field of Internet and cloud computing, big data service technology; specifically, it relates to a method and system for dynamically evaluating the innovation capabilities of technology enterprises. The method includes the following steps: acquiring multi-source data; converting first unstructured data into text information data, and extracting second structured data from the text information data; fusing the first structured data and the second structured data to obtain standardized data; the standardized data includes first quantitative data and first unquantified data; quantifying the first unquantified data to obtain second quantitative data; normalizing the first quantitative data and the second quantitative data according to historical dataset data to obtain evaluation data; and calculating the evaluation result based on the evaluation data. This invention can realize the dynamic evaluation of the innovation capabilities of technology enterprises and can achieve a balance of data from different dimensions.

The application discloses an EC decoding method and system of a distributed cluster, relates to the technical field of cloud computing, and can be applied to the technical field of cloud computing, big data, intelligent supply chain and the like. The distributed cluster uses an error correction code (EC) coding mode to determine data object blocks for storage, and the data object blocks comprise data blocks and check blocks. One specific implementation of the method comprises the following steps: in response to an exception of a data block of any data object stored by a target node, obtaining the identities of a plurality of decoding nodes storing the data object blocks; sending a decoding request to the decoding nodes according to the identities, so that the decoding nodes perform calculation according to the locally stored data object blocks; and obtaining an abnormal data block recovered based on the final calculation result of each decoding node. The implementation can reduce the network overhead and CPU resource occupation of a single node in the data block recovery process.

The invention discloses a cloud computing big data real-time processing method and system fused with an AI technology, and relates to the technical field of big data real-time processing methods, and the method comprises the steps: collecting original data from a plurality of heterogeneous data sources through employing a data access interface, and obtaining an original data set; preprocessing the original data set by adopting a distributed computing framework, and obtaining a preprocessed data set; constructing a load state prediction model based on a machine learning algorithm and the preprocessed data set, inputting the preprocessed data set, and outputting a load state prediction result; adjusting the cloud computing resource allocation quantity in the original data set based on the load state prediction result to obtain a resource allocation strategy; establishing a data processing channel by adopting a stream processing technology based on a resource allocation strategy, and executing an identification task in combination with an AI algorithm to obtain an analysis result; generating a decision report of the specific application scene based on the analysis result; the Kafka Connect connector is configured to be connected with a plurality of heterogeneous data sources, so that efficient integration of multi-source data is realized.

This disclosure provides a data indexing method, apparatus, electronic device, and storage medium, relating to the field of data processing, specifically cloud computing, big data, the Internet of Things, and intelligent search. The specific implementation involves: acquiring data to be stored; extracting index features from the data to be stored; sorting and storing target data associated with the data to be stored and the same storage space based on the index features; and establishing an index for the data to be stored based on the index features and the sorted storage location. Embodiments of this disclosure can improve query efficiency.

According to the property information sharing platform and the use method, the platform deeply integrates advanced technologies such as cloud computing, big data and the Internet of Things, and aims at solving property management contradictions, improving community life quality and promoting transparent sharing of information. The platform constructs a comprehensive service system which integrates community management, property payment, property management, parking management, advertisement management, community group construction and mental civilization construction, home care for the aged and life service. The stability and availability of the platform are ensured through a distributed system, containerized deployment and a multi-cloud strategy, and user privacy and data security are protected by using security measures such as data encryption and access control. The platform realizes interaction and information sharing among owners, property management and business committee, optimizes the processes of maintenance fund application, voting, approval, bid invitation, settlement and the like, and improves the management efficiency and service quality. And service quality. Meanwhile, the platform automatically collects and analyzes data, provides decision support for community management and service optimization, promotes community governance modernization, and constructs a harmonious livable community environment. The protection range covers the aspects of platform architecture and technology implementation, functional modules and business processes, information sharing and data mining, user interaction and interface design and the like.

The present application discloses a metadata management method and device, relating to technical fields such as cloud computing, big data, and intelligent supply chains. A specific implementation of the method includes: determining an index identifier for metadata in a file system; determining a target storage list corresponding to the metadata and a target storage location corresponding to the metadata in the target storage list from a storage list set based on the index identifier and a preset value, wherein the storage list in the storage list set is used to store a preset value of metadata; and storing the metadata in the target storage location in the target storage list. The present application eliminates the coupling between metadata in terms of storage location and the coupling between metadata in terms of storage relationships, thereby improving the accuracy and reliability of metadata.

The application relates to the field of cloud computing big data security storage, in particular to a big data security storage method and system based on cloud computing. The method comprises the following steps: encrypting and fragmenting an original data stream to generate an indexed fragment sequence; generating a dynamic key sequence and a verification key group based on the fragment information; combining a geographical position weight and a load balancing strategy to construct a safe transmission channel through a quantum key distribution mechanism to realize encapsulation and transmission of a safe fragment package; implementing node state monitoring and redundancy management to generate an update mapping table; and finally recovering a complete data stream through fragment positioning retrieval, data recombination and dynamic decryption. The application improves the security, reliability and overall load balancing capability of data storage.

The invention provides a group service migration optimization method based on multi-level cloud collaboration and related equipment, and relates to the field of cloud computing big data edge computing. The method comprises the following steps: acquiring state information of a first environment state according to user task request information and edge node information of each edge cloud administered by a regional cloud; obtaining an action value estimation value of each migration action based on the state information of the first environment state by using a migration algorithm network model of the regional cloud, and obtaining a migration action decision according to each action value estimation value, the migration algorithm network model is a neural network model for outputting an action value estimated value of each migration action in a corresponding state based on state information, and model parameters of the migration algorithm network model are trained and updated by the central cloud according to data provided by each regional cloud; and sending the migration action decision to the corresponding edge cloud. According to the technical scheme, the task execution efficiency can be improved.

The invention provides a resource allocation method and device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of cloud computing big data edge computing, and the method comprises the steps: obtaining a plurality of tasks, and carrying out the iterative updating of particle swarms of the plurality of tasks based on a particle swarm optimization algorithm, determining an individual optimal position feature of each particle after each iteration and a global optimal position feature of the particle swarm; wherein the resource use cost of the resource allocation scheme corresponding to the individual optimal position feature is optimal in the particle individual dimension, and the resource use cost of the resource allocation scheme corresponding to the global optimal position feature is optimal in the particle swarm dimension; and performing next iteration of the particle swarm after the to-be-mutated particles are mutated on the basis of the variation intensity determined by the number of stagnation times of each particle in the iteration process, stopping iteration until a preset number of iterations is reached, and determining a final global optimal position feature and a final distribution scheme represented by the global optimal position feature.

The invention provides an intelligent community planning management method and system. The method comprises the steps of analyzing community geographic information, and reasonably planning community layout and resource configuration; the operation state of community facilities is intelligently monitored, and remote management and fault early warning are achieved; big data analysis is carried out to provide a scientific decision basis for community planning and management; the system comprises a GIS (Geographic Information System) data processing module, an Internet of Things equipment monitoring module, a cloud computing big data analysis module, an artificial intelligence prediction module, an integration module, an automation module and a user interface module, improves the efficiency and accuracy of community management, and improves the safety of the community. Meanwhile, detailed data support is provided for community planning, the time from problem finding to problem processing is shortened, risks faced by a community are reduced, more personalized services are provided for community users, the operation state of equipment is mastered in real time, and fault early warning and maintenance are carried out in time.