26 results about "Cloud computing big data" patented technology

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 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 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.

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.

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.

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.

The application discloses a kind of campus energy consumption monitoring systems based on cloud computing big data, it is related to energy consumption monitoring technical field, including room energy consumption acquisition module, building sub-item energy consumption acquisition module, room building relationship acquisition module, room building basic information acquisition module, room power type energy consumption analysis module etc.;The application is by collecting the power data of each target equipment or each power type of room in the training stage, trains deep learning model, and corrects error by room total power data and building power type power data, realizes in actual monitoring stage can be through building total power data sequence the power sequence of each power type of each room electric equipment is predicted, reduce the cost of monitoring, by monitoring each room total power sequence and building each power type loop total power sequence, to correct the power sequence of each power type of each room electric equipment predicted, further improve the precision of prediction.

The application discloses a resource control method and device for multiple clusters of namespaces, which can be applied to the fields of cloud computing, big data, intelligent supply chain and the like. A specific embodiment of the method comprises: obtaining a namespace processing request of a target user, the namespace processing request being used for requesting to process resource data corresponding to a target namespace from a target cluster in multiple container orchestration engine clusters; verifying the namespace processing request according to a relationship data model through a master cluster in the multiple container orchestration engine clusters, the relationship data model representing a corresponding relationship between an existing namespace in the multiple container orchestration engine clusters, a creating user and a cluster to which the creating user belongs; and in response to the verification being passed, processing the resource data corresponding to the target namespace from the target cluster according to the namespace processing request. In the application, resource management control of the namespaces in the multiple container orchestration engine clusters is realized, and consistency of resource control permissions in the multiple engine clusters is ensured.

The invention belongs to the technical field of computer information and man-machine interaction, and particularly relates to a cloud computing big data GUI intention prediction driven zero-delay interaction system and method.The cloud computing big data GUI intention prediction driven zero-delay interaction system comprises a client module group and a server module group, and user interaction sequence vectors (UISV) are generated through interaction event capturing and vectorization; triggering multi-hypothesis intention prediction of a server side based on a deep learning model through a predictive request, and further pre-fetching a data result set corresponding to a high-confidence prediction state and actively pushing the data result set to a client side for caching; and after the user operation is stable, the client queries the local cache through the prediction state hash to realize instantaneous rendering. By adopting the technical scheme, the interaction delay can be reduced from hundreds of milliseconds to subten milliseconds, the redundancy calculation and network overhead are remarkably reduced, and the user analysis fluency and the system self-adaptive capability are improved.

The application discloses a search recommendation method and device, intelligent equipment, electronic equipment and a storage medium, which can be applied to artificial intelligence, cloud computing, big data, computer data, intelligent search, information flow and knowledge graph. The specific implementation scheme is as follows: a search task of a user is acquired, a search target object is determined according to the search task, the category to which the search target object belongs is determined, and an associated category having an associated relationship with the category is determined; recommendation information is generated and output according to the search target object and the associated category, wherein the recommendation information includes the search target object and objects belonging to the associated category; by combining the objects belonging to the associated category for recommendation, the problem that the search recommendation in the related art has great limitations is avoided, technical effects of improving the flexibility, diversity, richness and reliability of the search recommendation are achieved, and the search experience of the user is enhanced.

This application discloses an image processing method, apparatus, device, and storage medium, belonging to the field of artificial intelligence technology, and particularly relating to cloud computing, big data, or databases in the cloud technology field. The embodiments of this application can randomly select a processing method to degrade a high-resolution first sample image to obtain a low-resolution second sample image. This eliminates the need for manual processing, significantly reducing labor costs and improving the efficiency of acquiring low-resolution images. Furthermore, during training, a different processing method can be selected for degradation processing each time, ensuring that the trained target image processing model can perform good image processing on images processed by various methods, adapting to a wider range of image processing scenarios. Therefore, this target image processing model has good applicability.