1287 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 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 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.

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 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 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 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.

The invention relates to the technical field of electric power engineering, and discloses an internet of things type box-type substation based on online monitoring, which comprises a sensor network layer, an edge intelligent node layer, a self-healing communication module layer and a cloud management platform layer, the sensor network layer is used for collecting the operation state and environmental parameters of equipment in the box-type substation in real time; the edge intelligent node layer is used for processing sensor data, executing self-adaptive monitoring and local diagnosis and communicating with the cloud; the self-healing communication module layer is used for ensuring the reliability and continuity of data transmission; and the cloud management platform layer is used for receiving the data uploaded by the edge nodes and carrying out long-term analysis, model training and remote management. Through the adaptive multi-parameter fusion monitoring technology, the sampling frequency of the sensor is dynamically adjusted (for example, the temperature is increased from one time per second to two times per second when the temperature is abnormal), and in combination with a lightweight machine learning model of an edge intelligent node, equipment faults (such as insulation degradation and overheating) can be accurately predicted.

The invention provides a data driving and multi-element cloud edge cooperative computing method for intelligent workshop construction, which comprises the functions of multi-protocol heterogeneous equipment sensing, edge node adaptive load balancing, lightweight real-time edge computing processing, cloud centralized management control, industrial intelligent application service deployment scheduling, cloud edge task unloading and the like. The system comprises a data acquisition and transmission middleware, an edge stream processing engine and a cloud service center. The method has the characteristics of real-time performance, low delay, flexibility, expandability, loose coupling, low resource occupancy, high availability and privacy security.

The invention relates to the technical field of distributed computing and networks, in particular to a cloud computing and edge computing collaborative system integration architecture and a collaborative computing method. The method comprises the following steps: an edge processing cache unit filters and caches received original data based on a self-adaptive data filtering strategy and an intelligent cache strategy; the cloud management cooperation unit constructs a time delay energy consumption optimization objective function based on the current cloud node and edge node states, solves the time delay energy consumption optimization objective function by using a multi-objective optimization algorithm, and dynamically cooperates task scheduling and resource allocation; the heterogeneous hardware acceleration unit adopts a hierarchical accelerator scheduling strategy to calculate an optimal hardware matching degree, introduces a calculation acceleration efficiency coefficient, and schedules heterogeneous accelerator resources. According to the cloud computing and edge computing collaborative system integration architecture and the collaborative computing method, efficient collaborative scheduling between cloud computing and edge computing is realized by combining intelligent matching of a multi-objective optimization algorithm and a heterogeneous accelerator.

The invention discloses a computing acceleration method based on a cloud computing technology and a cloud management platform, in the method, the cloud management platform can obtain a job task input by a tenant, and distributes processing logic, which can be applied to programmable computing equipment, in job processing logic carried by the job task to at least one programmable computing equipment for processing; and distributing other processing logics which can be applied to the server in the job processing logics to at least one server for processing. According to the scheme, redundant cloud resources can be provided for the tenants to use, so that higher income is obtained, and the utilization rate of the cloud resources is improved.

The invention provides an intelligent charging pile control system based on a new energy automobile. The intelligent charging pile control system comprises a communication module, a data processing module, a charging power adjusting module, a safety protection module, a user interaction module, a cloud management platform, a power supply checking and recording module, an information backup and transmission module and a second-hand power supply monitoring module. The power supply checking and recording module is connected with a new energy automobile power supply system, detects and records parameters such as battery capacity and internal resistance, generates a power supply state report, realizes comprehensive detection and recording of the power supply parameters, provides data support for battery maintenance and fault diagnosis, assists an automobile owner to know the battery condition, and improves the safety of the automobile. A basis is provided for reasonable use and maintenance of a battery, basic data is also provided for subsequent battery management, the information backup transmission module locally backs up a power supply state report and transmits the report to a vehicle merchant service platform, the module realizes backup and transmission of power supply information, and a vehicle merchant can timely obtain detailed information of a vehicle power supply.

The invention discloses a PEDOT sensor nonlinear response compensation optimization system, and relates to the technical field of PEDOT sensors, the system comprises a cloud management center, and the cloud management center is in communication connection with a data acquisition module, a feature extraction module, a response compensation analysis module, a cloud compensation module, a compensation deviation analysis module and a feedback adjustment module. The method combines the neural network model through the cloud, precisely learns the nonlinear characteristics of the sensor, carries out precise compensation, carries out initial linear compensation at the end side, reduces the data amount transmitted to the cloud, improves the real-time performance of the system, carries out the model training and optimization at the cloud through a large amount of historical data, and improves the real-time performance of the system. According to the PEDOT sensor, the error introduced by nonlinear response is remarkably reduced, and the measurement precision and accuracy of the PEDOT sensor are effectively improved in an end-cloud cooperation mode, so that the sensor can provide high-reliability measurement data under different environmental conditions, and the requirement of high-precision measurement is met.

The invention discloses a storage node cluster access method and a cloud management platform, which not only can enable a plurality of applications to share the same storage pool, but also can ensure the data security among the applications. The method comprises the steps that a tenant can input an identifier of an application of the tenant and an identifier of a computing node of the tenant to a binding interface provided by a cloud management platform; and then, the cloud management platform can create a binding relationship among the identifier of the application, the identifier of the computing node and the identifier of the storage space serving the application, and enables the computing node to run the application. The cloud management platform may then deploy the binding relationship in the proxy node. And then, when the agent node receives an access request from the computing node, the agent node can detect the access request based on the binding relationship, and if information carried by the access request conforms to the binding relationship, the agent node creates a storage space in the storage node cluster or completes data processing in the storage space.

The invention discloses an eye protection method and system of an intelligent terminal, relates to the technical field of eye protection terminals, and aims to solve the problem of child eye use safety monitoring, multi-source data such as child face images, eye distances, equipment postures and environmental stability are collected, and time sequence data in a unified format is formed through noise filtering and synchronous processing; the posture angle and equipment stability information is extracted through the visual recognition and motion recognition sub-module, and the unhealthy eye using state is comprehensively judged; dynamically updating an eye using distance and a posture threshold value according to the height and the vision condition of the child and parent feedback; real-time monitoring is carried out, friendly reminding is triggered, and feedback data are recorded for closed-loop optimization; through cloud management, a parent-side App can check data in real time, a protection mode can be remotely started, and personalized eye protection suggestions are provided based on big data analysis. Through real-time remote intervention, accurate monitoring and personalized management of eye using behaviors of children are remarkably improved, and the risk of myopia is effectively reduced.