163 results about "Cloud data center" patented technology

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention discloses a heterogeneous resource scheduling method and device of a cloud data center, a medium and a product, and relates to the technical field of cloud computing, and the method comprises the following steps: receiving a scheduling task of an application instance containing source architecture resource information; screening the processing nodes according to the computing power demand of the application instance and the specification reference values of the processing nodes, and determining candidate nodes according to the screening result; constructing an application performance portrait of the application instance according to the performance data of the application instance; according to the application performance portrait and the scheduling task, equivalent resource configuration corresponding to deployment of the application instance on the candidate node is determined, a score is determined based on the equivalent resource configuration and the specification reference value, and a target node is determined based on the score; and modifying the resource request of the application instance based on the equivalent resource configuration to obtain a modified resource request, and deploying the application instance to the target node, so that the target node performs resource configuration based on the modified resource request to complete heterogeneous resource scheduling. And equivalence quantization and intelligent scheduling of heterogeneous resources are realized.

The invention discloses a cloud data center full life cycle monitoring and early warning system based on digital twinning, and the system comprises a multi-source collection and preprocessing module which is used for collecting and preprocessing multi-source data; the twinborn modeling and simulation module is used for constructing a digital twinborn body and completing virtual-real mapping and mechanism simulation; the time sequence modeling module is used for constructing a state space-Kalman enhancement time sequence evolution twinborn model and carrying out continuous time sequence modeling; the virtual-real residual error self-correction closed loop module is used for generating a virtual-real residual error and updating the digital twinborn body and time sequence evolution twinborn model; and the risk and linkage module is used for executing risk identification, grading and life cycle linkage according to the corrected time sequence characteristic representation. According to the invention, a state space-Kalman enhanced time sequence evolution twinborn method is adopted, virtual-real self-correction and full-life-cycle early warning of the cloud data center are realized, and the method has the advantages of high precision, strong robustness and self-optimization.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention relates to a PLC-bridging gateway internet-of-things automatic identification method, system and device and a medium. The method comprises the following steps: sequentially sending handshake detection signals to a plurality of PLC devices and receiving response messages; forming a candidate handshake data combination set, and completing handshake matching through a priority algorithm and an adaptive polling mechanism; verifying the response frame, analyzing the equipment information, and confirming the active state and communication parameters of the PLC equipment; generating a drive binding strategy according to a matching result, and distributing a session identifier for the drive binding strategy; generating a session identifier of the target PLC equipment, and updating a session routing table in the bridging gateway; and translating the industrial data collected by the target PLC device into an Internet of Things protocol data packet and uploading the Internet of Things protocol data packet to a cloud data center. According to the invention, automatic identification and rapid adaptation of various interfaces and protocols can be realized; the communication stability and real-time performance are improved through dynamic scheduling and route updating; uniform data uploading is supported, and efficient deployment of cloud analysis and the industrial Internet of Things is facilitated.

The invention discloses a river valley area bridge earthquake vulnerability intelligent evaluation and earthquake resistance optimization analysis method, and relates to the technical field of bridge structure earthquake resistance analysis. The method comprises the following steps: constructing a geological model of an area where a bridge is located and a three-dimensional structure model of the bridge; a plurality of monitoring points are arranged in a bridge and a river valley area where the bridge is located, and collected data are uploaded to a cloud data center through a wireless transmission subsystem; carrying out seismic oscillation time history analysis based on the digital twinborn body, and visualizing the weak part of the structure through a cloud picture by adopting an artificial wave and actual measurement wave mixed input mode; and generating a multi-objective optimization scheme according to the vulnerability curve. According to the method, seismic oscillation time history analysis is carried out based on the digital twin, an artificial wave and actually measured wave mixed input mode is adopted, a weak part of a structure is visualized through a cloud picture, a multi-target optimization scheme is generated according to monitoring data, the seismic monitoring efficiency and supervision strength of a bridge in a river valley area are improved, and a bridge reinforcement method is rapidly provided.

The application relates to a service scheduling and deployment method for Internet of Things edge computing node resources, which is realized based on an Internet of Things edge computing system. The system comprises a cloud data center, a convergence layer core network and a plurality of access layer switch networks connected in sequence. Each access layer switch network is in communication connection with a plurality of edge computing nodes, and each edge computing node is in communication connection with a plurality of data acquisition terminals. The application comprehensively considers the current running state of the edge computing system, network delay problems and running results after deployment, and performs service scheduling and deployment of Internet of Things edge computing node resources on each real-time data stream computing application. The application is suitable for the decision environment of the real-time computing task load and bandwidth dynamic change of the Internet of Things edge computing node, realizes global optimization from the system level, takes into account the single node load capacity, and realizes the optimal scheme and application planning and deployment decision suggestion of the long-period stable operation and real-time task flexible scheduling strategy.

The invention discloses a cloud data center virtual machine adaptive integration method based on Autoformer and an enhanced double-Q network, and relates to the technical field of cloud computing resource management and optimization, the resource utilization rate of each physical host is monitored in real time, historical load time sequence data is collected, a pre-trained Autoformer model is loaded, and double-Q network parameters and environment state variables are initialized; and predicting a host resource utilization rate based on an Autoformer model, and dividing an overload host, a low-load host and a normal host in combination with the current host resource utilization rate. According to the method, the Autoformer model is adopted for load prediction, trend terms and period terms are decomposed through an autocorrelation mechanism, the multi-scale periodicity in the cloud load is effectively captured, compared with a traditional LSTM model, the Autoformer is higher in modeling capacity for a complex time sequence mode, the misjudgment rate of an overload / low-load host can be remarkably reduced, a more accurate host state detection basis is provided for virtual machine integration, and the method has the advantages of being high in practicability and easy to popularize. Therefore, a resource allocation strategy is optimized.

The invention discloses a video image generation method and system driven by edge computing, and relates to the technical field of computer vision and edge computing, and the method comprises the steps: a terminal device initiates a generation task and perceives resources; dynamically dividing the generated task into a plurality of sub-tasks through a decision model based on the perception information, and deciding the execution position of each sub-task at the terminal, the edge node or the cloud; the nodes cooperatively execute the distributed subtasks and exchange intermediate data; and a final synthesis result is fed back to the terminal. The system comprises a terminal module, an edge node module, a cloud data center module and a collaborative management module. According to the invention, through terminal-edge-cloud cooperative computing, delay of a generation task and cloud bandwidth consumption are effectively reduced, data privacy protection is enhanced, system resource utilization efficiency and expandability are improved, and the method is especially suitable for video image generation application scenes with high real-time performance requirements.

The invention discloses a target detection pre-labeling method based on a fast algorithm, and relates to the technical field of computer vision and geometric processing, and the target detection pre-labeling method based on the fast algorithm comprises the following steps: S1, data acquisition and preprocessing; and the collected image data is transmitted to a cloud data center in real time through wireless transmission. According to the target detection pre-labeling method based on the fast algorithm, image information can be locked and recognized more accurately through operation of two times of pre-labeling, so that the collected image information can be recognized and screened more accurately, a certain workload can be reduced for subsequent manual auditing and correction, and the accuracy of target detection and pre-labeling is improved. And meanwhile, it is also ensured that the pre-labeling model can carry out accurate labeling processing on a relatively fuzzy or shielded image, and through operation of a YOLOv5 algorithm, the pre-labeling model can be more efficient and faster when carrying out image recognition.

The invention discloses an industrial Internet of Things data processing method and device, equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining first to-be-processed data accessed by terminal equipment through an edge computing node, performing standardization processing on the first to-be-processed data based on the data communication protocol of the first to-be-processed data to obtain second to-be-processed data; performing data processing on the second to-be-processed data by using the edge computing node to obtain target data, generating a decision response corresponding to the target data based on the target decision model, and processing the target data based on the decision response; uploading the target data and the decision response to a cloud data center based on a target data uploading strategy by using the edge computing node; and receiving and storing the target data and the decision response uploaded by the edge computing node through the cloud data center, and generating a quality evaluation result by using a preset data quality evaluation model. According to the invention, the processing efficiency and quality of the data in the industrial Internet of Things environment can be improved.

The invention belongs to the technical field of engineering construction, and particularly relates to a full-process construction method for a marching type deductive project, which comprises the following steps of: acquiring construction foundation, construction equipment, technical standard, historical similar projects, cultural content associated data and performance plot data of the deductive project; constructing a project risk early warning library by using a fusion early warning algorithm, storing the project risk early warning library in a cloud data center, issuing an early warning threshold and data to an edge computing gateway, and completing communication adaptation with a construction equipment sensor, a deduction equipment controller and a team terminal; the process is divided into stages through a WBS algorithm, a task generation algorithm is used for issuing a marching type construction task book, deploying three IMUs, collecting construction data and comparing the construction data with a risk early warning library threshold value for each stage, and if the threshold value is exceeded, early warning is triggered, and the construction task book is updated; and after construction is finished, stage acceptance and whole-process rechecking are carried out, and the project experience library is updated. Therefore, the problems of difficulty in project management and control, weak monitoring capability and the like in the prior art are solved.

The invention discloses an urban and rural pavement maintenance monitoring system based on intelligent well lid data linkage, and the system comprises a data collection module which detects the acceleration data, angular motion data and inclination angle change data of a well lid in real time through a three-axis acceleration sensor, a gyroscope and an inclination angle sensor which are disposed on the well lid; the edge calculation module is used for calculating a vibration value of the well lid according to the acceleration data, the angular motion data and the inclination angle change data of the well lid and correcting the vibration value; the cloud data center obtains a risk index of a road surface where the well lid is located through a pre-constructed association model according to the corrected vibration value and the position of the well lid corresponding to the vibration value; and the maintenance decision terminal module performs maintenance decision on the road surface where the well lid is located according to the risk index of the road surface where the well lid is located. According to the invention, the well lid on the road surface is taken as the basis of data acquisition, and the maintenance condition of urban and rural road surfaces is detected in real time by acquiring the data on the well lid, so that the maintenance condition can be accurately obtained in real time.

The invention relates to the technical field of cloud computing, and provides a cloud resource workload prediction method based on multi-scale self-adaption in a computing network environment, and the method comprises the steps: carrying out the data collection of a workload sequence of a cloud data center through employing a sample division method based on a sliding window, and obtaining the cloud workload data of different time scales; carrying out noise reduction processing on the cloud working load data by adopting an adaptive threshold noise reduction method based on wavelet multi-scale decomposition, and carrying out Fourier transform to extract frequency domain features; carrying out sample division on the processed cloud working load data by adopting an adaptive mode clustering algorithm, and outputting a mode data set; adopting a Mixup data enhancement method based on mode driving to generate a mixed sample, adding the mixed sample into the mode data set, and constructing an enhanced mode data set; and obtaining a cloud resource workload prediction result by adopting a similarity alignment dynamic integrated prediction method. Finally, an ablation experiment proves that the method has higher prediction precision.

The invention relates to the technical field of cloud computing, in particular to a cloud tenant bearing scale optimization method and device, equipment, a medium and a product, and the method comprises the steps: recognizing a sender and an access purpose of an access request, and determining a data flow direction corresponding to the access request; in response to the condition that the sender is the virtual machine, address conversion and message encapsulation are performed on the access request through the virtual switch, and the encapsulated access request is sent to the cloud gateway; and in response to the fact that the sender is the Underlay network or the public network, the cloud gateway is determined based on the sender, the cloud gateway confirms the virtual private cloud based on the access purpose, the access request is subjected to message packaging, and the access request is sent to the virtual switch. According to the technical scheme of the invention, the original address translation is sunk to the virtual switches corresponding to the virtual private clouds from the cloud gateway for processing according to the data in the cloud-out direction and the cloud-in direction, so that the memory space occupied by the NAT table items can be released, the detailed routing capacity of the virtual machines is improved, and the expansion of the cloud tenant scale borne by the cloud data center is facilitated.

The application discloses a kind of deep neural network gas identification method and system based on internet of things electronic nose, gas sample response signal obtained by gas sensor array collection is sent to cloud data center by terminal equipment with sensor array and internet of things module.In the pre-processing of data to microcontroller MCU, gram angle field transformation is carried out, so that the two-dimensional sensor response data received by host computer becomes three-dimensional data that can be input into convolutional neural network after dimensionality increasing, and the gas label is output through classifier, which can represent gas category and concentration grade, realizes the real-time, anti-interference and high detection accuracy of remote monitoring.Gives play to the advantages of strong feature extraction ability, fast model convergence and high recognition accuracy of convolutional neural network.It can be widely applied in environmental detection, industrial production, medical treatment and safety fields.

The application belongs to the technical field of intelligent management, and discloses a kind of hydropower station intelligent management method and system.The method comprises the following steps: cloud data center, data analysis model and hydropower station intelligent management model are constructed;Intelligent management base station, according to the physical entity data of target physical entity, constructs OPC UA instance;Data acquisition device, real-time monitoring data are collected and written into OPC UA instance;Intelligent management base station, using data analysis model, carries out data analysis;Intelligent management base station, using hydropower station intelligent management model, carries out hydropower station intelligent management;Intelligent management base station, real-time OPC UA information data, real-time data analysis result and real-time hydropower station intelligent management strategy are encrypted and uploaded to cloud data center for distributed storage.The application solves the problems of insufficient data processing capacity, lack of adaptive ability and excessive manual intervention in the prior art.

The application belongs to the technical field of fault identification, and discloses an equipment processing device fault identification method and system based on artificial intelligence. The method comprises the following steps: in a cloud data center, using an artificial intelligence algorithm, an equipment processing device fault identification engine is constructed, and a multi-modal data acquisition device is connected through an edge computing gateway; using the multi-modal data acquisition device, real-time monitoring multi-modal data of the equipment processing device is acquired, and is uploaded to the cloud data center through the edge computing gateway; in the cloud data center, according to the real-time monitoring multi-modal data, using the equipment processing device fault identification engine, fault identification is carried out, and real-time fault identification results and real-time fault maintenance strategies are obtained. The application solves the problems of single data dimension, limited model depth and precision, and lack of response mechanism in the prior art.

The invention discloses a map construction method and system based on air-ground collaborative fusion air-ground perception, and belongs to the technical field of automatic driving and environment perception. In order to solve the problems that closed-loop feedback only stays at a data layer for correction, perception errors cannot be radically eliminated and system robustness is insufficient in existing air-ground collaborative map construction, perception differences, observation angles, speeds and other perception parameters are accurately bound to form parameterized difference data; the cloud data center unit performs periodic iterative optimization on the end-side semantic understanding model based on the data and issues update parameters to realize error source calibration; according to the method, bidirectional cross validation, unmanned aerial vehicle height adaptive adjustment and a double-weight layered grid fusion algorithm are fused, and the system adopts an end-cloud-end architecture and is matched with 5G + V2X dual-mode communication to guarantee air-ground end-cloud real-time interaction. According to the scheme, the map construction precision and the system robustness are effectively improved, the closed-loop feedback delay is low, the obstacle avoidance decision error rate is remarkably reduced, and the method is suitable for complex unstructured environments such as mine exploration and field rescue.

The invention belongs to the technical field of current and voltage monitoring, and discloses a current and voltage monitoring method and system based on a metering device. The method comprises the following steps: acquiring real-time multi-modal monitoring data of target monitoring equipment by using a corresponding metering device according to a preset multi-modal data feature engineering space, and transmitting the real-time multi-modal monitoring data to an edge computing gateway; an edge computing gateway is used to preprocess the real-time multi-modal monitoring data, and the obtained preprocessed real-time multi-modal monitoring data is uploaded to a cloud data center; and in the cloud data center, current and voltage monitoring is carried out on the preprocessed real-time multi-modal monitoring data of the plurality of target monitoring devices, and a generated real-time abnormity maintenance strategy is sent to the corresponding target monitoring device. According to the invention, the problems of single monitoring dimension, insufficient real-time performance, low intelligent degree and poor adaptive ability in the prior art are solved.

Provided in the present application are a virtual instance management method based on cloud computing technology, and a cloud management platform. The cloud management platform is used for managing a cloud data center, wherein the cloud data center comprises a plurality of servers that access a high-speed interconnect bus inside the cloud data center. The method comprises: receiving a virtual instance memory creation request that is input by a tenant; in response to the virtual instance memory creation request, selecting a first server from a plurality of servers and sending a first control command to the first server; receiving a first virtual instance memory binding request; and in response to the first virtual instance memory binding request, sending a second control command to the first server to instruct the first server to provide, by means of a high-speed interconnect bus, a virtual instance memory located on the first server to a first virtual instance located on a second server for use. By means of the technical solution, memory sharing across servers for virtual instances can be realized, thereby improving data communication efficiency and memory utilization efficiency.

The embodiment of the invention provides a task allocation method and device for a cloud service system, equipment and a medium, the cloud service system comprises a cloud data center and a plurality of fog nodes, and the method comprises the steps that after the fog nodes receive tasks submitted by Internet of Things equipment, a plurality of initial allocation schemes are generated; acquiring state information of the cloud data center and the plurality of fog nodes; iterating the plurality of initial allocation schemes based on a differential evolution algorithm according to the state information to generate a plurality of candidate allocation schemes, and stopping iteration when an iteration termination condition is met; and determining a target allocation scheme from the plurality of candidate allocation schemes, and allocating tasks according to the target allocation scheme. According to the embodiment of the invention, on the basis of considering the state information of the cloud data center and the fog node, the task allocation scheme is subjected to multi-round iterative optimization through the differential evolution algorithm, and the allocation scheme considering efficiency and resource utilization is generated. The task processing delay is reduced, and the real-time performance of data processing of the cloud service system is improved.

The invention relates to a computing power-electric power joint optimization scheduling method for a cloud-edge collaborative system considering heterogeneous characteristics, and the method comprises the steps: taking an upper cloud data center as a scheduling main body, and carrying out the scheduling of the computing power-electric power joint optimization scheduling of the cloud-edge collaborative system based on the number of delay tolerant tasks, the unloading amount of delay sensitive tasks and a local electric energy cost characteristic curve in each scheduling time period; by taking maximization of the total operating profit in the whole scheduling period as an optimization target, optimizing to obtain a calculation service unit cost signal of the scheduling period, and issuing the calculation service unit cost signal to each edge base station; and each edge base station at the lower layer receives the time-sharing electric energy cost signal from the power grid, independently optimizes the real-time task unloading amount by taking the maximization of the comprehensive utility as an optimization target in combination with the calculation service unit cost signal received from the upper layer, and uploads the real-time task unloading amount to the cloud data center. Compared with the prior art, the method has the advantages that the modeling heterogeneous characteristics of cloud and edge computing power facilities are considered, and computing power collaboration of a cloud-edge system is effectively realized.

The application provides a cloud data center load prediction method and system based on improved TimesNet, multi-period feature extraction is performed on time series of resource utilization data, a plurality of most significant periods are determined, two-dimensional time series change modeling is performed, change data of the time series on different time scales is obtained, features of the most significant periods are extracted, and the features are adaptively fused into TimesBlock based on ResNet; a plurality of TimesBlock are combined and stacked to form an improved TimesNet network, so that the resource demand load of the cloud platform in the future time is predicted, the plurality of periods and fluctuation of the load can be more accurately captured, the accuracy of long-term prediction is improved, ResNet is used for two-dimensional convolution, the rapid change of the cloud data center load can be more quickly adapted, the resource scheduling lag or redundancy is reduced through accurate load prediction, the resource utilization efficiency is improved, the operation cost caused by default and waste is reduced, and the load prediction has better adaptability and generalization ability.

The invention belongs to the technical field of power grid control, and particularly relates to a power grid control strategy method and system based on a flexible interconnection device, and the method comprises the steps: collecting the operation data of a flexible interconnection system, combining the operation data with the operation characteristics of the flexible interconnection device, comprehensively considering the coupling relation between an information system and a physical system, and building a flexible interconnection system model; the information system acquisition module acquires operation data of the flexible interconnection system in real time and transmits the operation data to the control center through an information line, and the control center is used for operating a multi-time and multi-spatial scale double-layer control strategy to regulate and control the system and storing the data into the cloud data center; judging a corresponding selection control strategy of the operation mode of each transformer area according to the collected data; and selecting a corresponding control strategy to establish a corresponding objective function and a general constraint condition after determining the operation mode of each transformer area, solving the model by utilizing NSGAII and outputting a control instruction, adopting a hierarchical control strategy in system scheduling, and fully considering mutual power complementation of each unit.

The utility model discloses equipment for real-time acquisition and calibration of satellite signals, which comprises a shell, and a data processing module, a satellite signal transceiving module, a fakra connector and a USB (Universal Serial Bus) connector which are arranged in the shell and are connected through a circuit, the satellite signal transceiving module is mounted at the bottom of the data processing module; the fakra connector and the USB connector are arranged at the two ends of the data processing module respectively. The device and the vehicle-mounted recorder can use the GPS antenna at the same position to control the variable of the detection process, and the built-in network module of the handheld device can carry out data mutual transmission and bidirectional verification with a cloud data center, so that the positioning of the GPS is accurately calibrated.

The present application provides a method for solving a system of linear equations, a cloud management platform, and a related apparatus. The method comprises: acquiring first equation information of a first system of linear equations; acquiring first solution algorithm information of the first system of linear equations, the first solution algorithm information comprising a first solution algorithm for solving the first system of linear equations; obtaining first resource configuration information on the basis of the first equation information and the first solution algorithm information, the first resource configuration information comprising at least one of the following: the specifications of first virtual instances and the number of first virtual instances; and creating, in at least one cloud data center, virtual instances matching the first resource configuration information, and solving the system of linear equations by means of the virtual instances. By means of the method, the cloud management platform, and the related apparatus, hardware resources are adaptively configured on the basis of the specific requirements of solving tasks and algorithm characteristics, solution algorithms and algorithm parameters are intelligently selected, and dynamic resource allocation is performed when computing load fluctuates, thereby improving solving efficiency and reducing solving costs.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an Agent AI-based edge gateway system, which is deployed between terminal equipment and a cloud data center and comprises a computing power layer, a data layer, a capability layer, a service layer, an edge service scheduler, a privacy data coprocessor and an edge memory. According to the system, hardware resource perception and intelligent task scheduling are realized through an edge service scheduler, and AI tasks are dynamically allocated to local or cloud for execution; secure cooperative processing of data out-of-domain desensitization and return-to-domain restoration is realized through a privacy data cooperative processor; encrypted storage and access control of interaction data are provided through the edge memory. According to the method, the technical problems that the computing power is insufficient, the energy is limited and the data security and the model performance are difficult to consider when the terminal equipment deploys the AI application are effectively solved, and the high-performance, self-adaptive and sustainable AI service is provided on the premise of ensuring the data privacy.

The invention belongs to the technical field of intelligent control, and discloses an intelligent on-off control method and system based on a Bluetooth technology. The method comprises the following steps: carrying out initialization; based on the cloud data center, collecting real-time laboratory reservation information, and constructing a real-time laboratory reservation form in the edge computing gateway; acquiring real-time laboratory access information based on an edge computing gateway, and performing retrieval matching in the real-time laboratory reservation form; according to the real-time laboratory access information, using an intelligent on-off control model to generate a first real-time intelligent on-off control scheme, and sending the first real-time intelligent on-off control scheme to laboratory equipment through Bluetooth MESH networking; and according to the real-time equipment operation data, using the intelligent on-off control model to generate a second real-time intelligent on-off control scheme, and returning the second real-time intelligent on-off control scheme to the laboratory equipment through Bluetooth MESH networking. According to the invention, the problems of low transmission efficiency, poor stability, poor practicability, low intelligent degree and low data security in the prior art are solved.