164 results about "Cloud systems" patented technology

Techniques for a centralized vulnerability security scanning and distributed detection system are disclosed. Some techniques set forth a set of operations including receiving, in a first cloud environment of a cloud system, image scan results from a second cloud environment of the cloud system, receiving container identity data from a particular deployed container of a set of deployed containers in the first cloud environment, based on a comparison between the image scan results and the container identity data, determining that the particular deployed container of the set of deployed containers is running a vulnerable software product, and generating, for presentation on a graphic user interface (GUI), information associated with the vulnerable software product. The image scan results correspond to a vulnerability scan of a plurality of software products running in the set of deployed containers.

To provide a radio communication system and a communication method capable of safely relaying communication between a radio station connected to the Internet network and a radio station connected to a closed network.SOLUTION: The closed area system 11 and the cloud system 21 included in the wireless communication system 1 include a closed area side gateway server 13 and a cloud side gateway server 22. When receiving an authentication request from a radio station 41,42, a closed area side gateway server 13 and a cloud side gateway server 22 store activation information including unique information of the radio station 41,42 and generation information of an encryption key in a storage device 27. Regardless of whether the wireless station 41,42 is connected to the closed network 10 or the Internet network 20, the communication between the wireless station 41,42 and the closed system 11 or the cloud system 21 is protected by the encryption key generated based on the generation information included in the activation information.SELECTED DRAWING: Figure 1

Systems and methods for intelligent network incident management, Root Cause Analysis (RCA), and automated remediation include receiving metrics, graphs, and historic logs associated with network performance of a tenant of the cloud system; identifying a network issue based on the received metrics, graphs, and historic logs; performing an automated RCA to determine a cause of the network issue; and remediating the identified network issue based on the determined cause. Various embodiments include training specialized Large Language Models (LLMs) for performing the automated incident identification, RCA, and remediation.

The application provides a resource scheduling method and device, electronic equipment, storage medium and edge cloud system. The resource scheduling method comprises: in the case of identifying the resource requirement of a target workload, determining whether there is an edge node in the edge cloud cluster that meets the resource requirement; in the case that there is an edge node in the edge cloud cluster that meets the resource requirement, deploying the target workload on the edge node that meets the resource requirement; in the case that there is no edge node in the edge cloud cluster that meets the resource requirement at a specified time, or according to at least one of the resource requirement, the resource state of the edge cloud cluster, the running monitoring information of the edge cloud cluster and the current traffic corresponding to the target workload, performing global scheduling on the load deployment state of the edge cloud cluster. The technical scheme of the application can improve the resource utilization rate of the edge cloud cluster.

The application provides a data transmission method and device, electronic equipment and storage medium, which are applied to a cloud system. The method comprises the following steps: in response to a transmission request of first data, the first data is received, wherein the cloud system is deployed in a local area network; in response to the received first data, the first data is encrypted to obtain second data; in response to a data sending request, the second data is decrypted, and third data is generated based on the decrypted second data; and the third data is sent to a transmission target. The application can improve the security of data transmission.

The invention discloses a cloud manufacturing optimization system for a linear cutting workshop. The cloud manufacturing optimization system comprises a user layer, a service center layer, a scheduling entity layer and a user feedback layer, the user layer is provided with a cloud terminal and is used for exchanging data with the service center layer; the business center layer comprises a model center, a data center, a search center, a computing center and a planning center; the scheduling entity layer comprises an enterprise level, a workshop level and a production line level, three levels of automatic scheduling systems exchange data with the service center layer respectively, and distributed automatic scheduling systems are arranged in the three levels of automatic scheduling systems respectively; the user feedback layer feeds back the work summarization condition to the user layer; according to the method, a cloud system of a whole wire cutting product is integrally redesigned by utilizing a new computing mode, a business mode and an application mode of cloud computing according to a design concept of'cloud centralized management of discrete manufacturing resources and on-demand service of overall planning of cloud resources.

The application discloses an unmanned aerial vehicle intelligent sensing and intention reasoning system and device, which comprises image acquisition, target detection, knowledge graph, multi-modal large model, airborne computing power and edge-cloud cooperation module. The system is guided by target detection, enhances scene knowledge through the knowledge graph, improves the analysis ability of the multi-modal large model, and realizes the intelligent monitoring of the unmanned aerial vehicle based on the RK3588 platform. Real-time monitoring is realized by using the maneuverability of the unmanned aerial vehicle, which can not only analyze the scene, but also infer and predict the behavior intention and situation, thereby realizing early warning. The local and cloud systems are equipped with a historical database, a visual interface and a large parameter model, and the returned information is deeply analyzed. The system can be applied to the scenes of border patrol, city security, animal protection and the like.

A system and method for analyzing screen-recorded personalized digital content using on-device computer vision and generative AI. A user captures content from a recommender system interface, such as screen activity or browser-rendered content, optionally with concurrent voice commentary. On-device processing generates intermediate representations using CLIP-style embeddings, OCR text, and transcribed audio tokens, flagging advertisements, changes in content, diversity in content, and similarities in content. A compact generative AI model produces metadata summaries, which users may annotate with tags or comments. A composite metadata package is transmitted to a cloud system, while raw media is deleted. The invention enables privacy-preserving, bandwidth-efficient insight into recommender-based media and user feedback.

A system includes a key service, that is configured to authenticate requests from an external manufacturing system (“EMS”), receive a request for generating a keyset, generate the keyset, send the keyset to the EMS, and send the keyset to a persistent cloud storage system, wherein an external cloud system accesses the keyset for use in secure communication with a hardware device.

The embodiment of the invention provides a hypergraph-based resource allocation method and system in a hybrid cloud management platform. The method is applied to the technical field of cloud computing, and comprises the following steps: performing virtual extension on a matched individual by adopting an F matching algorithm according to a service request, and performing one-to-one matching conversion; on the basis of the individuals subjected to one-to-one matching conversion, constructing an optimal distribution model by adopting a hypergraph theory; and solving the optimal allocation model by adopting a heuristic algorithm of local search to obtain a resource allocation scheme. In this way, virtual machine resources can be effectively allocated, and the cloud system can achieve the maximum performance.

This application discloses a logical node management method and cloud system based on cloud computing technology, belonging to the field of cloud service technology. The method includes: a cloud management platform creating a target logical node in the infrastructure, wherein the target logical node includes a virtual CPU, a first virtual extended processor, and a virtual bus network, the virtual CPU and the first virtual extended processor being logically connected to the virtual bus network respectively; if the cloud management platform confirms that at least one first extended processor in the extended processor device pool has failed, it unloads the first virtual extended processor from the target logical node; the cloud management platform then attaches a second virtual extended processor to the target logical node, the second virtual extended processor implementing all or part of the functions of at least one second extended processor in the extended processor device pool, the second virtual extended processor being logically connected to the virtual bus network. This application improves the utilization rate of cloud resources.

Embodiments of the present application provide an edge resource expansion method, an edge cloud system, a device and a storage medium. In the embodiments of the present application, a center service node in a center cloud can request a server producer to produce a target server based on an edge resource expansion request of a target user; and install an operating system on the target server and deploy a cloud service; then, a logistics transportation party can be instructed to transport the target server to a target edge cluster corresponding to the target user, so as to deploy the target server in the target edge cluster and expand an edge node in the target edge cluster. In the embodiments, a whole-link expansion process integrating the user's edge resource expansion request, server production, transportation and edge cluster deployment is implemented, and the edge node expansion is integrated and end-to-end edge node expansion is implemented.

The utility model discloses terminal equipment of a DHR system based on radio frequency identification, which comprises an electronic tag provided with an antenna and a first inductor, and the antenna and the first inductor are connected to form a first induction loop; wherein a first capacitor is also arranged in the first induction loop; the reader-writer is in signal connection with the antenna; the terminal machine is in signal connection with the reader-writer, and the output end of the reader-writer is in signal connection with the input end of the terminal machine; the terminal machine is also provided with a first communication module; and the display device is in signal connection with the first communication module. According to the utility model, through a radio frequency reading technology, the device can rapidly identify information of employees or articles and upload the information to a cloud system in real time. According to the rapid and automatic information processing mode, the manual input requirement and the related error probability are greatly reduced, and the data processing efficiency and accuracy are improved.

A hybrid AI cloud system includes at least one distributed node configured to execute an artificial intelligence (AI) service, and a hybrid server configured to monitor the workload of a first distributed node configured to execute the AI service, and prepare in advance at least one second distributed node configured to assist or replace the first distributed node according to the result of the monitoring. The second distributed node is selected from a previously prepared distributed node pool including distributed nodes of the hybrid AI cloud system, or from a distributed node of an external cloud system. Through this, if there is a shortage of distributed nodes capable of executing AI services in the distributed cloud, distributed nodes included in a centralized cloud can be utilized, thereby achieving the advantages of both the distributed cloud and the centralized cloud.

The application provides an edge computing model training method and an edge-cloud cooperative system. The method comprises the following steps: after an edge node determines a first model to be used, the edge node determines the maximum number of trainable layers that the edge node can support for the first model; the edge node determines the required slice bandwidth of the edge node and reserves bandwidth resources according to the maximum number of trainable layers and a deterministic time delay; after the training is completed, the edge node sends model parameters obtained by training to a cloud node through the reserved bandwidth resources, the cloud node completes subsequent model layer training, and the edge model is updated through a back propagation algorithm; the cloud node aggregates the edge model corresponding to the edge node by using a federal aggregation algorithm, generates a global model, and sends the global model to the edge node; wherein the deterministic network guarantees the synchronization of training, the edge-cloud system solves the problem of insufficient computing power of the edge node, reduces the training pressure of the edge node, the federal learning guarantees the number of training, and improves the model training precision.

A multicloud system allows independent software vendors (ISVs) to install software in different ways, such as within the user space of the multicloud system's organizational tenant, within the ISV's space within the multicloud system, or within the multicloud system's own space. The packaging and installation capabilities of the multicloud system enable ISVs to easily specify how their code will be made available to users via the multicloud system, without having to perform the necessary configurations and create different configurations depending on the ISV's required usage.

A method, a system, and computer program product for customizing settings of cloud-based software applications using an external database as a source are provided. A request to generate a new database table corresponding to a new version of a database table associated with a cloud-based system hosting a cloud-based software application is received. The request is processed to determine a content of the new database table using a data structure of the database table. A version identifier corresponding to the new database table is generated. The version identifier and the new database table are provided, to an external database for storage. A deployment, to a productive system, of the new database table, is controlled for the new database table to be used when executing the cloud-based software application.

The embodiment of the invention provides an IP address statistical system and method, electronic equipment, a medium and a program product, and relates to the field of wireless communication. The system comprises a first service module used for receiving IP address demand basic information of each network element deployed in a network function virtualization (NFV) network cloud system platform; the second service module is used for receiving docking configuration information of each network element of the NFV network cloud and an external system; the third service module is used for receiving resource pool deployment parameters; the resource pool deployment parameter is used for indicating the actual deployment scale of each network element under different networking types in a single resource pool, and the actual deployment scale at least comprises the actual deployment number of each network element in the single resource pool; and the fourth service module is used for counting the number of IPv4 and / or IPv6 addresses of each NFV network cloud resource pool based on the IP address demand basic information of each network element, the docking configuration information and the resource pool deployment parameters.

Systems and methods for providing an identity power scoring system for cloud environments. Various embodiments include defining a plurality of admin categories associated with a cloud environment; deriving a category power score of an identity for each of the plurality of admin categories; and calculating a global power score of the identity based on the power score for each of the plurality of admin categories. The scoring system helps identify and prioritize risk associated with specific identities, allowing more optimized methods of protection for information in the cloud-based system.

The application relates to the computer technical field, in particular to a system fault-tolerant test method, an electronic device and a storage medium. In the system fault-tolerant test method, the function characteristic type information and the disaster simulation program are acquired first, then the cloud platform system to be tested is divided into levels based on the function characteristic type information, a cloud system hierarchical model corresponding to the cloud platform system is obtained, further, a data disaster environment is simulated in the cloud system hierarchical model based on the disaster simulation program, finally, the cloud system hierarchical model in the data disaster environment is tested for fault tolerance, and fault-tolerant indexes corresponding to the cloud platform system are obtained. Through the system fault-tolerant test method, the data disaster of the cloud platform system is simulated based on the cloud system hierarchical model, so that the fault-tolerant indexes of the cloud platform system are obtained through the fault-tolerant test, and the fault-tolerant indexes of the cloud platform system can be more accurately measured.

The invention discloses a cloud system based on a cloud computing technology and a charging method, and belongs to the technical field of cloud services. The cloud system comprises a resource charging module and a first cloud host, a first cloud instance and a resource allocation module run on the first cloud host, and an application and resource adjustment module runs in the first cloud instance. And the application sends the first hardware resource capacity expansion demand to the resource allocation module through the resource adjustment module. The resource allocation module allocates idle hardware resources matched with the first hardware resource capacity expansion demand on the first cloud host to the first cloud instance under the condition that the first cloud host has the idle hardware resources matched with the first hardware resource capacity expansion demand; and sending a first message that the first cloud instance has expanded idle hardware resources matched with the first hardware resource expansion demand to a resource charging module. And the resource charging module performs charging in response to the first message. According to the method and the device, the resource elastic scaling speed of the service realized by the first cloud instance is improved.

Data including current locations of candidate clouds to be seeded is obtained; based on same, a vehicle is caused to move proximate at least one of the candidate clouds to be seeded. Weather and cloud system data are obtained from a sensor suite associated with the vehicle, while the vehicle and sensor suite are proximate the at least one of the candidate clouds to be seeded. Vehicle position parameters are obtained from the sensor suite associated with the vehicle. Based on the weather and cloud system data and the vehicle position parameters, it is determined, via a machine learning process, which of the candidate clouds should be seeded, and, within those of the candidate clouds which should be seeded, where to disperse an appropriate seeding material. The vehicle is controlled to carry out the seeding on the candidate clouds to be seeded, in accordance with the determining step.

This invention relates to the field of cloud computing and big data technology, specifically providing a linked list-based method, system, terminal, and storage medium for integrating node data. The method includes: exporting node monitoring data from all partitions of a private cloud system; and integrating the node monitoring data from all partitions using a recursive processing model to obtain an integrated linked list with the node data arranged in sequence. This invention achieves rapid and automated integration of node data, improving the speed of application deployment, ensuring data center stability, reducing data center management costs, and enhancing the user experience.

This solution provides a way for users to directly interact with the underlying object data lake storage in a hyperscaler when accessing user data. [Solution] This feature is called direct access. Using this solution, the performance of user systems and cloud systems is improved without affecting functionality or increasing complexity. This also applies to Spark users, where the driver abstracts the communication path between the Spark application and the HDL file. This results in an opt-in solution that allows users to reduce the costs associated with increased complexity.

This application provides a vehicle-cloud collaborative instruction execution method and a vehicle-cloud system. The method includes: receiving a message sent from the cloud, wherein the message includes at least one instruction; extracting the message sequence number to obtain a message sequence number, and extracting the instruction sequence number to obtain an instruction sequence number; determining that there is no packet loss when multiple message sequence numbers, multiple instruction sequence numbers, complete instructions, and the execution of the corresponding preceding instructions are all satisfied, wherein complete instructions mean that the instruction content is not missing, the preceding instructions and the corresponding instructions have a dependency relationship, and the instruction sequence number of the preceding instructions is less than the instruction sequence number of the corresponding instructions; and, given that there is no packet loss, executing the instructions sequentially according to their priority, wherein the instruction priority is positively correlated with the importance of the instructions. This solution solves the problem of poor reliability in the execution of vehicle-cloud collaborative control instructions in existing technologies.

Methods, systems, and devices for data processing are described. A server host may receive a login request that includes a clear text password for an account associated with a tenant of a multi-tenant cloud platform. The server host may retrieve an encrypted payload associated with the account. The encrypted payload may include a hash value of a user-configured password for the account and an indication of a hashing algorithm used to transform the user-configured password into the hash value. The server host may obtain a wrapped symmetric key provisioned by a symmetric key distribution service. The server host may transmit a request that includes the encrypted payload, the clear text password, and the wrapped symmetric key. The server host may receive a response that indicates whether a hash value of the clear text password from the login request corresponds to the hash value of the user-configured password.

According to the medical equipment data acquisition intelligent scheduling method and the cloud system provided by the invention, the available traffic is pre-allocated for each medical equipment and the priority of each medical equipment is determined, so that the equipment with high priority can be transmitted preferentially, and the maximum transmission amount under the condition that the traffic runs on the same day is ensured; the data acquisition efficiency is improved, the data transmission is not interrupted due to the fact that the flow is pre-distributed, and the data transmission accuracy is guaranteed.