299 results about "Cloud computing services" patented technology

The invention discloses an elastic management and optimal scheduling method and system for cloud computing resources, and belongs to the technical field of cloud computing resource management.The method comprises the steps that a cluster composed of multiple types of intelligent agents is constructed, and the intelligent agents comprise a resource evaluation intelligent agent, an elastic telescopic intelligent agent, a load balancing intelligent agent and a fault recovery intelligent agent; the intelligent agents are distributed and deployed on different nodes of a cloud computing data center, and information interaction is realized through a communication interface based on RESTful API and a distributed message bus constructed by a gRPC protocol; a unified agent management platform is set up and is responsible for registration, monitoring, scheduling and dynamic adjustment of agents, and orderly operation and efficient cooperation of the whole multi-agent system are ensured. According to the method, the functions of efficient elastic allocation, intelligent load balancing, rapid fault recovery, continuous optimization evolution and the like of resources in the cloud computing data center can be realized, and the quality and competitiveness of cloud computing services are effectively improved.

Aspects of the disclosure identify a pattern of features associated with a computing component indicating an increased probability that the component incurs an uncorrectable error. Historical component data, component features, server data, and server services are utilized to identify the patterns that correlate to an increased probability that the component incurs an uncorrectable error. For example, this data is used as input into a machine learning platform. Proactive and / or mitigating actions that reduce the probability of an uncorrectable error or its negative effects are presented and / or implemented to minimize or eliminate disruption in cloud computing services.

The present disclosure provides a resilient (radio) access network ((R)AN) slicing framework encompassing a resource planning engine and distributed dynamic slice-aware scheduling modules at one or more network access nodes, edge compute nodes, or cloud computing service. The resilient (R)AN slicing framework includes resource planning and slice-aware scheduling, as well as signaling exchanges for provisioning resilient (R)AN slicing. The intelligent (R)AN slicing framework can realize resource isolation in a more efficient and agile manner than existing network slicing technologies. Other embodiments may be described and / or claimed.

The invention particularly relates to a cloud computing resource allocation method and system. According to the cloud computing resource allocation method and system, historical business data is collected, demand data is collected and analyzed in real time, and the demand trend is predicted in combination with the historical business data; using conditions and states of various resources in the cloud computing environment are monitored in real time; formulating an optimized resource allocation strategy according to a demand analysis and prediction result in combination with resource state monitoring information; and executing resource allocation operation, monitoring the use condition of the cloud computing resources in real time, collecting user feedback information in real time, optimizing a resource allocation strategy in real time according to the resource monitoring data and the user feedback information, and dynamically adjusting the allocation of the cloud computing resources so as to improve the resource utilization rate. According to the cloud computing resource allocation method and system, the resource allocation rule is optimized through specific resource allocation, efficient utilization of resources is achieved, and the performance and cost effectiveness of cloud computing services are improved.

The invention relates to the technical field of forest fire management, in particular to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed in a cloud computing server cluster and is used for uniformly storing personnel information, fire scene information and geographic information data and realizing multi-source data fusion and authority management through a standardized API (Application Program Interface); the central intelligent analysis system is deployed in a high-performance computer, integrates a forest fire danger auxiliary decision-making model, a forest fire spreading trend prediction model, a force distribution dynamic display module and a collaborative plotting module, and supports fire spreading prediction and real-time decision-making instruction issuing based on multi-dimensional data such as weather, terrain and vegetation; the mobile intelligent analysis system realizes fire spreading trend analysis, mobile plotting and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves forest fire emergency response efficiency and resource scheduling accuracy.

The invention provides a risk assessment method for code change, and relates to the technical field of computers, in particular to the technical field of code change of financial software, medical health software, e-commerce software, cloud computing service and industrial internet. According to the specific implementation scheme, based on a change code, a knowledge graph of the change code is constructed; obtaining calling association information of a change code from the knowledge graph; and inputting the change code, the calling association information of the change code and a preset cue word into a large model for risk assessment to obtain a risk assessment result for the change code. According to the method, automatic risk assessment of the change code is realized, and the accuracy and efficiency of risk assessment are improved.

An indoor air cleaning system is disclosed and includes an air pollution treatment device, and a networked cloud computing service device. The air pollution treatment device includes an air cleaning device, a central control and regulation device and a communication module for detecting, locating, circulating and filtering the air pollution in an indoor field. A gas detection module detects and transmits air pollution data to the communication module. The central control and regulation device communicates with and controls the gas detecting module, to control the air cleaning device for filtration. The networked cloud computing service device receives and stores the air pollution data to form an database. A control command is intelligently selected, and transmitted to the gas detection module. Consequently, the air cleaning device is controlled to filter the air pollution, and a gas state in the indoor field reaches a clean room requirement of ZAPClean Room 1˜9.

The embodiment of the invention discloses a real-time monitoring method and equipment for a cloud computing server room and a medium, relates to the technical field of equipment supervision, and is used for solving the problems of multiple blind areas and low reliability and real-time performance in the existing monitoring. The method comprises the following steps: collecting multi-source data in real time through a heterogeneous sensor network deployed in a cloud computing server room, and preprocessing the multi-source data based on edge computing nodes to obtain multi-source data features; performing alignment processing on the multi-source data features based on a preset space-time calibration algorithm so as to construct a global dynamic topology matrix of the cloud computing server room based on a preset machine room digital twin model and the aligned multi-source data features; based on the historical operation data and the global dynamic topology matrix of the cloud computing server room, constructing a multi-dimensional prediction map of the cloud computing server room; and inputting the multi-dimensional prediction map into a preset space-time diagram convolutional neural network model to identify a current abnormal event of the cloud computing server room and carry out early warning.

A system for situation awareness guided energy harvesting (SAGEH) in vehicles includes a cloud computing server, and sensors capturing information about a host vehicle and remote vehicles, and their environments. The vehicles and server each have a controller that executes a SAGEH application including triggering collection of host and remote vehicle information and environmental information, continuously observing a traffic signal and traffic situation along a host vehicle path along a road segment, generating an estimate of a quantity of time for the traffic signal to change status and generating an estimated quantity of time for the host vehicle to stop at the traffic signal, in response to one or more of the estimated quantities of time, generating a control output command. The control output command causes the host vehicle to dynamically, automatically, and adaptively harvest energy by engaging one or more of a stop / start system, and a regenerative braking system.

The invention discloses an underground cable safety assessment method and system based on cloud edge collaboration and multi-agent deep reinforcement learning. The method comprises the following steps: acquiring temperature, vibration, deformation and image data of multi-end acquisition nodes of an underground cable; uploading to an edge service node, and extracting spatio-temporal features of video image data, temperature, vibration and deformation data at the edge service node by adopting a bidirectional long-short-term memory network; and uploading the feature data preprocessed by the edge service node to a cloud computing service center for multi-source feature data fusion, constructing a multi-agent double-adversarial deep Q network, taking the fused feature data as the state input of the multi-agent double-adversarial deep Q network, and training to obtain a cable safety state assessment model. Therefore, the safety state of the underground cable can be identified and evaluated. Effective feature extraction and fusion of underground cable multi-source data are realized, and rapid and accurate evaluation of the safety state of the underground cable is realized based on fusion features.

The invention belongs to the technical field of order management, and discloses an order management method based on cloud computing service. The method comprises the steps that the cloud platform receives a passenger position sent by a passenger terminal; acquiring real-time traffic data, defining a screening radius, and generating an order dispatching area for passenger positions; driver positions sent by all driver ends in the order sending area are received; road traffic information is obtained, the road traffic information, the passenger position and all driver positions are fused, the shortest driving time from each driver position to the passenger position is calculated, and a time sorting table is generated; the order is sent to the driver end with the shortest receiving time, and if the driver end does not receive the order, the order is sent to the driver end in sequence according to the time sorting table; according to the invention, cloud computing and big data analysis technologies are fully utilized, intelligent optimization and dynamic adjustment of order distribution are realized, the waiting time of passengers and the unloaded driving rate of drivers can be effectively reduced, the user experience is effectively improved, and the overall operation efficiency is improved.

The invention relates to the technical field of server management and cloud computing, in particular to a dynamic load-oriented intelligent identification heterogeneous server resource expansion method. The method aims at optimizing the resource management efficiency, the hardware expansion flexibility and the overall operation stability of the system in a data center and a cloud computing environment by integrating an advanced intelligent identification technology, a real-time data analysis capability and a self-adaptive resource scheduling mechanism. The method is particularly suitable for modern data center scenes requiring high concurrent processing capability, dynamic load adaptability and high reliability, such as a large-scale cloud computing service platform, an enterprise-level distributed storage system and an artificial intelligence computing cluster.

The invention discloses an equipment parameter adjusting method, system and equipment, a server, a product and a medium, relates to the field of servers, and aims to solve the problem that energy efficiency and delay of computing equipment cannot be balanced, and the method comprises the following steps: initializing positions of a plurality of particles in a particle swarm based on a plurality of batch processing sizes and kernel frequency combinations; for each particle in the plurality of particle swarms, according to the position of the particle in the current iteration, obtaining a predicted performance expression value and an actual performance expression value of the computing device in the current iteration, and updating the position of the particle in the next iteration based on the predicted performance expression value and the actual performance expression value; and taking the particle corresponding to the optimal actual performance value when the iteration ending condition is met as an optimal solution, and controlling the operation of the computing equipment according to the batch processing size and kernel frequency combination in the optimal solution. The energy efficiency and delay of the computing device can be balanced, the working efficiency of the computing device is improved, the operation cost is reduced, and the competitiveness of the cloud computing service is enhanced.

The invention discloses a resource unified scheduling method and device in a heterogeneous cloud environment and a medium, belongs to the technical field of cloud platforms, and aims to solve the technical problems of low resource utilization rate, unbalanced allocation and mismatching of service characteristics of the cloud platform in the heterogeneous cloud computing environment. According to the technical scheme, the method comprises the steps that virtual resource centers are established in a cloud platform, logic isolation and integration are conducted on one or more virtual data centers, the virtual resource centers are created and allocated to users for use, differences of calculation, storage and network resources of a bottom layer heterogeneous IaaS environment are shielded, the unified virtual resource centers are generated, and the virtual resource centers are distributed to the users for use. Unified computing, storage and network resources required by cloud computing service are provided for the upper layer of the cloud platform, and dynamic scheduling and multiplexing of the resources are supported; the method specifically comprises the following steps: accessing a virtualization environment; carrying out resource tagging management; creating a virtual resource center and a virtual data center; and scheduling service resources.

Embodiments of systems and methods are provided to trigger migration of a workload from cloud-based resources to local resources, or vice versa. In the disclosed embodiments, an orchestration service receives telemetry data from a client system associated with a user and cloud resource usage data corresponding to the user from a plurality of cloud service providers. Before the end of each cloud computing service billing cycle, the orchestration service: uses the cloud resource usage data and / or the telemetry data to determine a cloud resource usage, which is expected for the user at the end of the cloud computing service billing cycle; generates a trigger to migrate the user's workload from cloud-based resources to local resources, or vice versa, based on the expected cloud resource usage; and initiates migration of the user's workload if a trigger is generated. As such, the orchestration service can be used to effectively manage per-user cloud resource costs.

In one embodiment, a computer-implemented method comprises using a script automation processor that is hosted using a virtual compute instance and a virtual storage instance associated with one or more second non-transitory computer-readable storage media storing one or more second sequences of instructions defining an API implementation of the API, a graph server, and one or more programming language runtime interpreters, receiving a user pipeline automation script comprising one or more first sequences of instructions specifying one or more API calls to an application programming interface (API), and a user application; using the script automation processor, creating and storing one or more programmatic containers in memory of the script automation processor, the containers corresponding to functions that user pipeline automation script and user application invoke; using the script automation processor, creating and storing a directed acyclic graph (DAG) in the memory of the script processor, the DAG comprising nodes and edges corresponding to dependencies of the containers; using the script automation processor, interpreting the user pipeline automation script using a particular programming language runtime interpreter among the one or more programming language runtime interpreters; using the script automation processor, running the user pipeline automation script to automatically build, test, or deploy the user application in a cloud computing service.

A system for predicting a user plane using control plane features and Granger causality includes a host device a cloud computing server and controllers. The controllers execute control logic including a prediction application (PA) that obtains sensor data from the sensors, sends control and user plane data to infrastructure, and to the cloud computing servers, and accesses prior knowledge data stored within memory of the cloud computing servers. Additional control logic performs a Granger causality test on the user and control plane data, and utilizes a prediction model to generate a prediction from fused user and control plane data. A prediction verifier is applied to the prediction from the prediction model, and the PA enables the host device to make reliable predictions that allow the host device to adapt to dynamic wireless communications network conditions even when a best possible model is not fully trained.

An indoor air cleaning system with a networking mechanism is disclosed and applied in an indoor field (A) for detecting and cleaning air pollution completely. A real-time air quality monitoring in the indoor field (A) is achieved by arranging plural gas detectors (A), at least one gas molecule control hardware device (2), at least one air conditioning device (3) and a networked cloud computing service device (4) in the indoor field (A) and forming an intelligent linked system by the arranged gas detectors (1) in an outdoor filed and the indoor field (A). Meanwhile, the air pollution in the indoor field (A) is also detected and intelligently compared with an ambient air quality status, and an air guiding volume of an air guiding fan (21) is instantly controlled according to the air quality. Whereby an energy saving benefit of the effective controlling operation of the purification device is achieved.

The invention discloses a one-cloud multi-core heterogeneous resource hybrid scheduling method, which comprises the following steps of: calculating a node resource availability score RAS and a task type affinity score TAS, carrying out comprehensive scoring on nodes in combination with a weight factor so as to realize intelligent scheduling of Pod, and collecting and analyzing node resource data in real time by utilizing a Promeheus open source monitoring solution, so as to realize real-time scheduling of the Pod. By predicting the resource pressure index RP I and the resource pressure trend index RT I and optimizing the scheduling decision, the method is suitable for the cloud computing environment, and particularly, when complex and diversified computing tasks are processed, the resource management efficiency and the cluster performance can be remarkably improved, and efficient and reliable cloud computing services are provided for users.

The invention discloses a method and a system for optimally proportioning a microbial fertilizer protective agent and a preservative. The method is applied to a system comprising a cloud computing server, an edge computing node and a laboratory detection unit, environmental parameters such as temperature, humidity and pH value and bacterial activity data of the microbial fertilizer are collected through the edge computing node, and the data are uploaded to the cloud computing server. And the cloud computing server constructs a formula optimization model based on the collected data, calculates and outputs an optimal proportioning scheme, and controls the edge computing node to monitor and verify the bacterium activity stability of the optimal proportioning scheme. And dynamically adjusting the proportioning parameter of the protective agent and the preservative according to the verification result. The system integrates a high-precision sensor, data acquisition equipment and laboratory detection equipment, so that intelligent optimization of the ratio of the microbial fertilizer protective agent to the preservative is realized. The bacterial activity stability of the microbial fertilizer can be effectively improved, and the storage life is prolonged.

The invention relates to the technical field of sintering processes, in particular to a preparation method of low-cost low-carbon sinter, which comprises the following steps: establishing a perfect crude fuel ingredient database; establishing a cost performance evaluation calculation model; controlling the granularity of iron ore powder, solid fuel and flux; associating the cost performance model with batching software through a computer language to obtain crude fuel with high cost performance and sequencing the crude fuel; key parameters of sintering ore burdening are set; and a cloud computing service system is adopted, so that an optimal scheme for automatically optimizing ingredients is realized. The method has the advantages that the effects of reducing the batching cost and reducing the solid fuel consumption are obvious, and the annual benefit is more than 1,500-1,7500 million yuan; secondly, solid fuel is reduced, CO2 emission is reduced annually, and good social benefits are achieved; by means of the modern computer technology, ore blending software is wide in measuring and calculating range, short in period and easy to operate, decision basis is provided for manufacturing enterprises to reduce sinter cost and solid fuel consumption, and sinter quality is improved.

The present disclosure provides a resilient (radio) access network ((R)AN) slicing framework encompassing a resource planning engine and distributed dynamic slice-aware scheduling modules at one or more network access nodes, edge compute nodes, or cloud computing service. The resilient (R)AN slicing framework includes resource planning and slice-aware scheduling, as well as signaling exchanges for provisioning resilient (R)AN slicing. The intelligent (R)AN slicing framework can realize resource isolation in a more efficient and agile manner than existing network slicing technologies. Other embodiments may be described.

Malware detection logic executed by a secured device residing in a home network may receive a message from an unsecured device of a first unsecured network and intended for a destination device of the home network, the destination device comprising a security client. The malware detection logic may establish a secure communication channel between the malware detection logic of the secured device and the security client of the destination device. The malware detection logic may execute a validation test on the message to determine that the message includes malware. The malware detection logic may report an alarm to the security client of the destination device. The malware detection logic may transmit information related to the malware to a cloud computing server. The malware detection logic may prevent an application associated with the destination device from processing the message.

Systems, devices, and techniques are disclosed for detecting misconfiguration of guest account security permissions. User personas may be generated from user activity data generated by access using guest accounts to controllers of a cloud computing server system. Clusters of user personas may be generated from the user personas. Anomalous user personas may be identified based on the clusters of user personas. A database query that was made to a database of the cloud computing server system, is associated with an identified user persona, and requested sensitive data may be identified from database query logs. A size of a response from the database to the identified database query may be identified from the database query logs. The size of the response may indicate that the response included the sensitive data. The cloud computing server system may prevent use of the guest account associated with the user persona.

The invention relates to the field of cloud computing and server operation and maintenance monitoring, and discloses a cloud computing server operation fault state monitoring method which comprises the following steps: deploying a lightweight monitoring agent at each server node, and collecting performance index data of a server in real time; preprocessing the acquired performance index data; performing fault prediction on the preprocessed performance index data based on a deep learning model, and outputting a fault prediction result; according to the fault prediction result, an alarm threshold value is adjusted in combination with the current load condition of the server; based on the fault prediction result, the severity of the fault is evaluated, and the alarm level and the monitoring frequency are automatically adjusted; and adjusting the priority and resource allocation of the monitoring task through an optimization algorithm. Through real-time acquisition and intelligent analysis of server performance data, in combination with deep learning and optimization algorithms, the fault prediction accuracy and the response speed of a monitoring system are significantly improved, false alarms are effectively reduced, and the resource utilization efficiency is improved.

A user terminal emulation server establishes a secure channel connection with a first I / O user device using a session identifier and an identifier associated with the first I / O user device to determine a first I / O user device specific key generated from a master key, the first I / O user device specific key and the session identifier being used for secure communication of messages with the first I / O user device. An indication of an I / O user interface capability of the first I / O user device is received through the secure channel connection with the first I / O user device. The user terminal emulation server communicates with the first I / O user device to use the I / O user interface capability to provide at least part of the communication service for a user.

A system for optimizing performance, managing network traffic, and enhancing user experience includes a host device a cloud computing server and controllers. The controllers execute control logic including a prediction application (PA) that obtains sensor data from the sensors, sends control and user plane data to infrastructure, and to the cloud computing servers, and accesses prior knowledge data stored within memory of the cloud computing servers. Additional control logic performs a Granger causality test on the user and control plane data, and utilizes a prediction model to generate a prediction from fused user and control plane data. A prediction verifier is applied to the prediction from the prediction model, and the PA enables the host device to adapt to dynamic wireless communications network conditions.

The invention relates to an internet of things equipment intrusion detection method based on edge computing and machine learning, and belongs to the technical field of network security intrusion detection. In order to solve the problems that resources of Internet of Things equipment are limited and attack behaviors are difficult to quickly and accurately discover, a lightweight first-level intrusion detection model deployed on an edge computing server is designed, and the attack behaviors can be quickly detected; a secondary intrusion detection model deployed in a cloud computing server is further designed, the model is based on a convolutional neural network and a bidirectional GRU, multiple attention mechanism designs are fused at the same time, and attack behaviors can be accurately detected. According to the invention, the network flow collected by the terminal equipment is detected in a mode that the two-stage intrusion detection models on the cloud computing server and the edge computing server cooperate, and the first-stage intrusion detection model is optimized and updated by using an incremental learning mechanism, so that the network intrusion detection accuracy is improved, the false alarm rate and the missing report rate are reduced, and the network intrusion detection efficiency is improved. And the security of the Internet of Things equipment is enhanced.

The present disclosure relates to a computer-implemented method (100) at a processing unit (11, 203) for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system (1) of a liquefied natural gas, LNG, plant. The present disclosure also relates to server (200) for providing cloud computing services that is remotely located with respect to a thermodynamic system (1) and which comprises a processing unit (203) for determining one or more optimization parameters in order to optimize the operation of a thermodynamic system (1) of the LNG plant.

The invention discloses an intelligent navigation system and method for a tea leaf picking robot, and the system is characterized in that an industrial control terminal carries out the construction of a three-dimensional vector space for a tea garden based on a panoramic image and a structured light image obtained in real time, and carries out the planning of a picking path through a path planning model stored in local; and converting the generated planning result into a control signal and transmitting the control signal to an execution mechanism of the picking robot. According to the system and the method, the real-time performance of path planning and the fluency of picking work are improved; the cloud server can provide cloud computing service interfaces for a plurality of industrial control terminals at the same time, management is convenient, and the overall use cost is reduced. According to the system, other sensors except a camera do not need to be used, the hardware equipment and software processing cost is reduced, the path planning model is stored locally, data are processed through the local model, the data processing speed and the picking work fluency are improved, and large-scale application and popularization of mechanical picking are facilitated.