1651 results about "Cloud management" patented technology

The invention relates to the technical field of micro-grid control, in particular to a multi-layer distributed micro-grid control system and method based on edge cloud collaborative lightweight reinforcement learning. The method comprises the following steps: a cloud management unit trains a DQN model by using historical environment data, generates a lightweight DQN model through pruning and quantification, issues the lightweight DQN model to an edge node unit, and formulates day-level and weekly-level optimization scheduling strategies based on a function layering + layering cooperation strategy; the edge node unit monitors electrical quantity and environmental parameters in real time through a sensor, deploys a lightweight DQN model to monitor abnormal conditions and trigger local early warning and emergency strategies, and makes a fast decision by using the lightweight DQN model; the micro-grid subgroup units collect data of the edge node units. According to the invention, through close cooperation of the edge node layer, the micro-grid subgroup layer and the cloud management layer, a distributed cooperative control mode of edge node real-time decision, subgroup cooperative management and cloud global optimization is realized.

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention discloses a household Internet of Things integrated fire alarm system and a linkage control method thereof, and relates to the technical field of smart home security. A first sensing recognition module, a second deep recognition module, a linkage control module, a cloud management platform module and a system self-checking module are integrated; the problems that in an existing fire alarm system, the false alarm rate is high, linkage equipment is prone to false triggering, linkage control logic is complex, and errors are prone to occurring are effectively solved. According to the invention, multi-modal data fusion and a deep learning algorithm are adopted to carry out fire identification, a dynamic threshold adjustment algorithm is combined to optimize the sensitivity of a sensor, and intelligent linkage configuration and an anomaly detection function are provided through a cloud management platform; the accuracy of fire identification and the stability of the system are greatly improved, the credibility of a user to the system and the comprehensiveness of family safety protection are remarkably improved, and resource waste and unnecessary panic caused by false alarm are effectively reduced.

The invention discloses an operation and maintenance method and system of a multi-cloud management platform based on cloud edge collaboration, and the method comprises the following steps: S1, deploying cloud service and edge nodes, and initializing a cloud edge collaboration environment; s2, constructing an edge autonomous optimization network, adjusting and optimizing a task strategy, and predicting a task load of an edge node; s3, adjusting task priorities, and classifying task strategies; s4, constructing a task feature matrix, a cloud edge resource matrix and a task and resource matching model; s5, allocating resources to each task to generate a task resource allocation table; and S6, identifying operation abnormity through the time sequence abnormity detection model, and triggering task migration, resource switching or task redistribution when detecting that the edge node is abnormal. According to the method, the edge autonomous optimization network and the intelligent scheduling algorithm are utilized, dynamic task allocation and exception recovery are achieved, and the method has the advantages of being high in resource utilization rate, high in scheduling real-time performance and good in system stability.

The invention discloses a BIM-based building electromechanical full life cycle management platform and an optimization strategy thereof, relates to the technical field of building informatization management, and solves the defects of the existing BIM technology in the aspects of processing efficiency, model precision, data synchronization and change management. According to the invention, a data layer is used for data cleaning and standardization through a machine learning model, and distributed storage is realized through a cloud management system; the model layer constructs and updates a high-precision BIM model; the synchronization collaboration layer ensures the consistency and traceability of data by using a real-time synchronization mechanism and a block chain technology; the decision analysis layer performs data analysis and prediction through a deep learning algorithm; the full life cycle management layer integrates historical data and current data and automatically triggers a management process; according to the method, the data processing efficiency and the model precision are greatly improved, and the seamless sharing capability of information and the transparency of data synchronization and change management are remarkably improved, so that the overall collaborative efficiency and the management level of a project are enhanced.

The invention discloses a self-adaptive cloud management platform system based on intelligent resource scheduling and container arrangement, and relates to the field of computer information management. The system comprises a refined resource scheduling and adaptive optimization module, a containerized application life cycle management and dynamic container arrangement module, a high-precision operation and maintenance monitoring and self-healing mechanism module based on big data analysis, and a dynamic resource allocation and elastic scaling strategy module of an intelligent scheduling engine. The system takes a containerization technology as a core, realizes centralized management and monitoring of cloud computing resources, can realize dynamic intelligent resource scheduling and optimization, accelerates application deployment, improves system flexibility, strengthens operation and maintenance monitoring and platform safety guarantee, and comprehensively improves resource optimization and cost effectiveness. The resource scheduling efficiency is improved, the application deployment is simplified, the operation and maintenance monitoring is enhanced, and efficient resource management is realized through an adaptive optimization technology.

A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.

The invention provides an unmanned aerial vehicle battery installation and management method and system for an intelligent charging cabinet, and relates to the technical field of unmanned aerial vehicles, comprising the steps that the intelligent charging cabinet selects a matched standby battery according to battery specification information, and battery replacement is completed through a visual positioning system and a mechanical arm; collecting performance parameters of the standby battery, generating a charging strategy and performing real-time adjustment; evaluating the performance of the standby battery based on a deep learning algorithm to generate a health evaluation report; and when the evaluation result is lower than a safety threshold, triggering recovery processing, and uploading the historical performance parameters to the cloud management platform to update the battery state evaluation model.

The invention relates to the technical field of digital information processing, and provides a photovoltaic power station data operation and maintenance management system, and a data exchange network service management system comprises a server, a cloud management module, an edge calculation module, a data exchange module, a performance evaluation module, an intelligent routing module and a self-adaptive data synchronization module. The performance evaluation module is used for monitoring key performance indexes of the network state of the whole system, evaluating the current running states of the cloud, the edge nodes and the network state through the key performance indexes to form an evaluation result, and feeding back the result to the cloud management module, the edge calculation module, the self-adaptive data synchronization module and the intelligent routing module; and the data exchange module is responsible for data synchronization and exchange between the cloud and the edge nodes, so that the data consistency of the edge nodes in the system is ensured.

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention discloses an environmental protection facility state monitoring and diagnosing method and system based on vibration and infrared detection, and the method comprises the following steps: collecting vibration and temperature data of environmental protection facility equipment, removing noise and environment interference through a filtering algorithm, and carrying out the standardization processing; extracting features of the data through a space-time convolutional network; performing deep fusion on the features by using an adaptive feature transformation network; performing abnormal mode detection; analyzing potential reasons of the abnormal mode by using a causal inference model, and generating a fault diagnosis report; a real-time processing strategy is generated, and the operation state of the environmental protection facility equipment is controlled in real time; and the fault diagnosis result and the instant processing strategy are uploaded to a cloud management platform, a notification is sent to related maintenance personnel, and remote monitoring and collaborative maintenance are realized. According to the method, the space-time convolutional network and the causal inference model are adopted, the faults of the environmental protection facility equipment are effectively identified, a proper emergency processing strategy is generated according to the real-time monitoring data, and the accuracy and response efficiency of equipment fault diagnosis are improved.

The invention discloses an equipment access authentication system, method and equipment of an electric power internet of things, and a medium. The system comprises a cloud management platform and electric power side end equipment, the cloud management platform is used for acquiring an access request corresponding to the power side-end equipment, verifying identity information of the power side-end equipment according to the access request, generating an access token corresponding to the power side-end equipment, and issuing the access token to the power side-end equipment; the power side end equipment is used for acquiring the access token issued by the cloud management platform and communicating with the cloud management platform based on the access token; and the cloud management platform and the power side end equipment communicate by adopting a transport layer security protocol. Based on the technical scheme, the request source is ensured to be correct and not tampered by verifying the legality of the equipment identifier and the signature, and after the identity verification of the edge equipment is successful, the cloud generates the access token and performs verification and regular updating in the subsequent request, so that the dynamic property and effectiveness of access control are ensured.

The invention provides a distributed collaboration-based computing network intelligent agent system and a resource dynamic scheduling method thereof, and belongs to the technical field of computing network integration. The system comprises an edge agent used for sensing local computing power, network bandwidth and task load in real time, predicting task demand fluctuation by using a lightweight neural network, adjusting resource allocation weight in real time in combination with network topology change, and executing a preliminary task scheduling decision; the regional collaborative agent is used for aggregating multiple edge node states based on federated learning, generating a cross-node resource scheduling strategy, verifying the credibility of a computing power transaction smart contract and determining a cross-domain resource allocation scheme; and the cloud management agent is used for constructing a global resource portrait model according to the information provided by the edge agent and the regional collaborative agent, performing long-term strategy optimization, issuing global strategy information, and constructing and updating a computing power transaction smart contract based on a preset computing power transaction smart contract template. According to the invention, multi-level refined scheduling of computing network resources is realized.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

According to the grid-connected line electric energy quality on-line detection device, the system comprises a broadband sensing module which is deployed on a grid-connected line to form an original analog signal; the conditioning module forms a standardized digital sampling signal; the edge calculation module receives the standardized digital sampling signal, analyzes a voltage effective value, a harmonic distortion rate, a sag depth and an event duration index in real time through a parallel calculation architecture, and forms an electric energy quality characteristic data set; the intelligent analysis module receives the electric energy quality characteristic data set to form an abnormal event report; and the communication interaction module receives the abnormal event report, encapsulates the abnormal event report into a standardized data frame through a configurable protocol stack, synchronously pushes the standardized data frame to the local monitoring terminal and the cloud management platform, receives an external control instruction and feeds back the external control instruction to the intelligent analysis module to optimize an analysis strategy. The grid-connected line electric energy quality on-line detection device can solve the problem that complex disturbance cannot be recognized in real time and cannot be accurately traced to the source.

A cloud management platform provides an identity and access management (IAM) service and a security token service (STS). The IAM service is used to confirm an enclave instance type input or selected by a first tenant and a conversion rule input or selected by the first tenant. The conversion rule is used to convert, into a tenant-customized attribute, an enclave instance attestation document provided by an enclave instance corresponding to the enclave instance type. The STS is used to receive a token obtaining request sent by an application program in the enclave instance.

The invention relates to the technical field of distributed energy storage and intelligent power grid control, in particular to an intelligent distributed energy storage cluster collaborative management system. Comprising a state acquisition module used for acquiring local power grid state parameters of an edge computing node in real time; the power grid state parameters comprise power grid frequency, power grid voltage and a heartbeat communication state with the cloud management platform; the risk calculation module is used for calculating quantified island risk indexes; the mode judgment module is used for determining a current operation mode decision; the mode switching module is used for switching the system from a vertical cooperative mode to a horizontal autonomous mode when the island risk index is greater than an island switching threshold value; and when the island risk index is smaller than or equal to the island switching threshold value, the system is maintained to operate in the vertical cooperation mode. According to the system, misjudgment and missed judgment of a traditional single threshold value mode are overcome, it is ensured that the mode switching module can be switched to the horizontal autonomous mode from the vertical cooperative mode in time under the real risk, and decision making is accurate and reliable.

The invention discloses an intelligent water level monitoring system and a data transmission method thereof, and belongs to the technical field of Internet of Things sensing. The system comprises a distributed water level sensing node, an edge computing gateway and a cloud management platform, wherein the sensing node is integrated with a piezoresistive water level transmitter and an adaptive Kalman filtering module; the data transmission method comprises a dynamic threshold triggering mechanism and a compression algorithm based on improved Huffman coding, and low-power-consumption and high-reliability transmission is realized by optimizing transmission frequency and a data packet structure. The edge computing gateway is internally provided with an LSTM water level prediction model, historical data are analyzed through an LSTM neural network, and an early warning signal is generated in real time. And the cloud management platform stores the key data by adopting a block chain technology. Through multi-sensor data fusion and layered encryption transmission, the water level monitoring error rate is reduced to + / -0.5 cm, the data transmission energy consumption is reduced by 40%, and the monitoring efficiency and the data safety are remarkably improved in flood early warning and reservoir management scenes.

The invention provides a house waterproof maintenance system based on an intelligent detection and self-repairing technology, and the system comprises an intelligent sensing layer which is used for collecting multi-dimensional physical parameters of building waterproof key nodes in real time; the edge calculation analysis layer is used for performing fusion analysis and risk prediction on the perception data; the dynamic self-repairing execution layer is used for triggering a multi-stage repairing mechanism according to an analysis result; the cloud management layer is used for realizing visual management and control and strategy optimization of the whole system, and relates to the technical field of building waterproofing, the building state is monitored in real time through a multi-mode sensing network, water leakage risk prediction is realized in combination with an edge calculation algorithm, and collaborative repair of a microcapsule repair agent, a shape memory alloy grid and an electrodialysis module is triggered; the system remarkably improves the timeliness and reliability of waterproof maintenance, reduces the manual intervention cost, and is suitable for intelligent maintenance of residential buildings, industrial buildings and historical protection buildings.

The invention relates to the technical field of livestock breeding, in particular to a cow manure automatic scoring system and method.The cow manure automatic scoring system comprises an image acquisition unit, a physical property sensor, a chemical component detection module and a data processing unit.The image acquisition unit is used for capturing the form, color and fiber residue of manure; the physical property sensor is used for measuring the water content of excrement and evaluating the viscosity and hardness of the excrement; the chemical component detection module comprises a near infrared spectrum analysis unit and an electrochemical sensor unit; and the data processing unit is used for integrating an AI algorithm, realizing multi-source data fusion and scoring, and uploading the data to the cloud management platform through the communication module. Therefore, the problems that in the prior art, manual scoring is subjective, low in efficiency and difficult to quantify, diseases are difficult to find early due to the lack of rapid component detection means, and accurate nutrition regulation and control are difficult to achieve due to the fact that scoring data are difficult to integrate into a breeding system are solved.

The invention discloses an automatic fare collection method and system based on multi-modal identity recognition, and relates to the technical field of rail transit automatic fare collection systems and multi-modal biological recognition fusion, and the method comprises the following steps: 1, enabling a user to approach a recognition area, carrying out the multi-modal data collection, and carrying out the quality detection and preprocessing; 2, feature extraction and credibility preliminary calculation are carried out; 3, environment data are collected and subjected to standardization processing; step 4, adaptively predicting a weight through machine learning, and constructing weight smoothing and time domain constraints; step 5, carrying out D-S evidence step-by-step fusion, and calculating comprehensive credibility; and step 6, comparing the comprehensive credibility with a preset threshold value, if the comprehensive credibility is greater than or equal to the preset threshold value, determining that the identification is successful and the gate is opened for passing, otherwise, determining that the credibility is insufficient, and performing alternative / manual processing. The system comprises a front-end identification terminal, a read-write industrial control all-in-one machine and a cloud management platform.

The invention relates to the technical field of equipment management, in particular to a medical beauty equipment cloud management system and management method. The method comprises the following steps: acquiring a medical beauty equipment number and medical beauty equipment parameters; carrying out cloud platform data uploading on the medical beauty equipment parameters based on the medical beauty equipment number, and generating medical beauty equipment monitoring data; consumable information digital registration is carried out on the medical beauty equipment monitoring data, and a beauty equipment consumable digital registry is generated; the method comprises the following steps: acquiring beauty equipment consumable data through a beauty equipment consumable digital registry to obtain beauty equipment consumable sensing data; consumable loss influence analysis is carried out on the beauty equipment consumable sensing data, and a consumable state monitoring data set is generated; and performing consumable service life prediction on the consumable state monitoring data set, thereby generating consumable service life early warning data. According to the invention, through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform, the accuracy and comprehensiveness of cloud management of the medical beauty equipment are improved.

The invention discloses a new energy automobile battery health prediction method and system, and relates to the technical field of monitoring analysis, and the method comprises the steps: carrying out the real-time collection of the operation state parameters of a power battery pack corresponding to a new energy automobile, obtaining a battery state data set corresponding to the new energy automobile, carrying out the adaptive noise suppression processing of the battery state data set, and obtaining a new energy automobile battery health prediction result. Generating a de-noised battery state feature map, performing feature extraction on the de-noised battery state feature map to obtain a multi-dimensional coupling feature set, constructing a dynamic weight distribution matrix, performing feature fusion on the multi-dimensional coupling feature set, determining a battery health state comprehensive index, and triggering a grading early warning mechanism based on the battery health state comprehensive index; and constructing a battery health prediction model, carrying out long-term modeling on the battery health state comprehensive index, predicting a battery health state evolution trend in a future charging and discharging period, generating a balance maintenance strategy, and pushing the balance maintenance strategy to the vehicle-mounted terminal and the cloud management platform. The method has the effect of improving the accuracy of battery health prediction.

The invention provides a method and a device for creating a cloud service instance, relates to the field of computers, and aims to improve the balance of utilization of computing resources in a hybrid resource pool, ensure the expected stability of resources of various heterogeneous services deployed in the hybrid resource pool, improve the anti-risk capability of the hybrid resource pool and reduce the operation cost. The method comprises the steps that a cloud management platform receives a service request of a tenant, the service request is used for creating a target cloud service instance by using computing resources in a physical resource pool, the physical resource pool is configured to be a plurality of logic resource pools, and the plurality of logic resource pools are used for providing different types of cloud service instances; the proportions of computing resources required by all types of cloud service instances are different; in response to the service request, determining a target logic resource pool matched with the type of the target cloud service instance from a plurality of logic resource pools of a physical resource pool; and creating a target cloud service instance by using the computing resources in the target logic resource pool.

The invention discloses an electric vehicle power battery health degree assessment method and system based on cloud data analysis, relates to the technical field of battery health degree assessment, and enables battery health assessment to be more scientific by performing feature extraction on a health data set to obtain a residual capacity change rate delta Qre, a battery polarization degree Pba and a charging and discharging lag factor CD. In combination with the health factor HI, the system can automatically judge the health state of the battery, distinguish normal, decline, abnormal and other states, and identify possible fault modes, such as capacity attenuation, electrode aging, short circuit risk, thermal runaway trend and the like. Through cloud data analysis, the system can gather battery operation data of different vehicles under different working conditions, a larger-scale data set is formed, and higher generalization ability is provided for model training and health prediction. An operator can remotely monitor the battery health states of all vehicles through the cloud management platform, the motorcade scheduling is optimized, and the utilization rate of battery assets is improved.

The invention relates to a control method based on a cloud PC server and a cloud PC management platform. The method comprises the following steps: starting the cloud PC management platform, and establishing and keeping link between a computing blade and the cloud PC management platform through a host blade management module; a user logs in a preset host through an access layer of the cloud PC management platform, and then remotely accesses the preset host by establishing an audio and video transmission channel and responding to a keyboard and mouse event; every preset time interval, the cloud PC management platform detects the working state of the cloud server through the equipment management module and updates the database; and if the user has an upgrading demand, accessing the computing blade through the host blade management module to execute upgrading. By configuring a special audio and video channel, the audio and video experience of a user is improved, and meanwhile, the stability of the cloud server is improved by detecting the working state of the cloud server in combination with the equipment management module and upgrading the computing blade by the host blade management module.

The invention relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

The invention belongs to the technical field of cognitive disorder rehabilitation, and particularly relates to a dynamic self-adaptive middle-aged and elderly cognitive and mental health intervention system which comprises a VR interaction module, a physiological data acquisition and processing module, a dynamic self-adaptive module and a cloud management platform. Interaction scenes of the VR interaction module comprise an occupational scene and a nostalgic scene; the physiological data acquisition and processing module acquires and processes multi-modal physiological data of a user in real time; the dynamic self-adaptive module is used for calculating a cognitive load index and an emotion pressure index based on physiological data, dynamically adjusting occupational task complexity through a fuzzy logic controller, triggering restalgic scene switching, and embedding a mild cognitive task in a restalgic scene to maintain training continuity; and the cloud management platform stores user historical data, and optimizes a scene recommendation strategy through a machine learning model. A personalized intervention strategy can be customized according to the actual situation of the user, and the user experience is improved.

The invention provides a small carrier for manned and logistics and an unmanned intelligent driving system of the small carrier. The system is applied to the technical field of automatic driving and comprises a sensor module used for collecting the information of a carrier and the environment of the carrier, a path planning module used for performing target detection, road recognition, obstacle detection and positioning and map construction according to the information of the carrier and the environment of the carrier by using a multi-modal sensor fusion method, and obstacle avoidance and automatic path navigation are realized based on an improved path planning algorithm. The cloud management platform is used for monitoring the state of the carrier in real time, storing navigation data and vehicle health information through the cloud and supporting multi-vehicle collaborative scheduling; and the voice interaction module supports voice instruction input and is used for analyzing the voice instruction of the user, dynamically responding and re-planning the path by using the path planning module. According to the invention, the transportation efficiency and the intelligent level of the small-sized carrier for manned and logistics are improved.

A cloud resource access control method includes obtaining and recording a first resource control policy that is configured by an administrator of a target organization and that is for a target cloud resource in the target organization; obtaining a first resource access request that is triggered by the user outside the target organization and that is for the target cloud resource in the target organization; and allowing or denying, based on the first resource control policy, the first resource access request for accessing the target cloud resource.