3103 results about "Application layer" patented technology

The invention discloses a cloud monitoring service operation and maintenance dynamic optimization system and method based on an AI intelligent agent, and relates to the technical field of cloud computing intelligent operation and maintenance. The method is used for solving the problems of hybrid cloud cross-layer data splitting, hidden fault association failure and operation and maintenance action conflict. Heterogeneous data of a physical layer, a virtual layer and an application layer are collected through a containerized probe, and a standardized cross-layer index is constructed through layered tagging and dynamic time sequence alignment. And constructing a fault propagation map through incremental correlation analysis and a dynamic time window, and identifying space-time coupling nodes. And based on the directional disturbance verification causal relationship, combining with the resource scheduling and fault chain incidence matrix fitting coupling degree index, and generating a root cause positioning instruction. And optimizing an action priority based on a propagation cost gradient and an asymmetric game strategy, and updating the model and the rule through a feedback closed loop. According to the invention, cross-layer data fusion and accurate fault positioning are realized, and the cloud service stability and the resource utilization rate are improved.

The invention discloses an information security management method based on data processing, and particularly relates to the field of information security management, comprising multi-source heterogeneous data acquisition, multi-dimensional feature index calculation, comprehensive threat detection calculation and adaptive threat decision. According to the method, the covert communication channel is accurately identified by constructing the encrypted traffic multi-mode detection model and combining protocol fingerprint entropy and traffic self-similarity analysis; an improved entropy weight fusion algorithm is adopted, collaborative analysis of system process abnormity, network behavior offset and hardware degradation is achieved, and a cross-dimension attack chain is reconstructed; a storage medium physical feature-cryptographic module runtime joint monitoring mechanism is created for the first time, hardware layer physical attacks and cryptographic side channel vulnerabilities are synchronously defended through temperature-delay correlation evaluation and key derivation density analysis, a complete attack surface from a physical layer to an application layer is covered, and the security is improved. And the detection precision and the response real-time performance of the complex threats are obviously improved.

The invention discloses a network threat multi-modal detection method based on a large model, and belongs to the technical field of network security, and the method comprises the steps: collecting three types of heterogeneous data of NetFlow flow of a network layer, a system call chain sequence of a host layer and a protocol load of an application layer, and carrying out the desensitization processing and feature coding to generate a unified tensor format; the method comprises the following steps: through network security threat intelligence and MITRE ATTamp; performing supervision fine tuning on the large model by using a CK attack chain sample, and constructing a network threat identification special model; cross-device behavior characteristics are extracted through a model self-attention mechanism, and a dynamic behavior map is constructed; and finally, comprehensively evaluating an attack mode matching degree, a node vulnerability mean value, historical alarm association and an attack path risk by adopting a weighted fusion algorithm, and triggering a high-confidence alarm when a comprehensive score exceeds 0.8. According to the method, through multi-modal data fusion and dynamic graph analysis, the detection precision and response efficiency of the complex attack chain are improved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a database migration method, device, equipment and medium. And deploying a double-write control module of the application program layer to write the source database and the target database in parallel, and completing consistency verification through a data verification comparison task. And controlling a target database write-in channel according to a difference analysis result, closing a source database write-in channel in combination with a service takeover state, and completing a migration process from the source database to the target database. According to the method, full migration, application layer double writing, differential data verification and dynamic write-in channel control are combined, data consistency of the source database and the target database in the migration process is guaranteed, the source database and the target database are safely switched to after the service takeover capacity is verified, and the migration safety and continuity are improved.

The invention discloses a JDK8-based heterogeneous service large model dynamic adaptation and intelligent scheduling method and system, and belongs to the technical field of artificial intelligence service integration, and the method comprises a unified service module which provides standardized HTTP / WebSocket access for an application layer and shields the difference of multiple platforms at the bottom layer; the dynamic routing module is used for dynamically selecting service nodes based on service quality indexes and supporting weight calculation, fusing recovery and flow dyeing; the service execution module is used for loading a platform adapter through a dual-mode service factory, integrating a tool calling function and realizing protocol conversion and authentication injection; and the configuration management module supports dual-configuration source hot loading of the static file and the dynamic database and provides versioning rollback and consistency verification. According to the method, the problems of multi-platform protocol difference, service rigid expansion, tool calling coupling and the like can be solved, the complexity of multi-platform management is remarkably reduced, and the flexibility and maintainability of the system are improved.

The invention discloses an ocean holographic environment comprehensive digital twinning system, and relates to the technical field of ocean monitoring, the digital twinning system comprises a data layer, a model layer, an application layer, an environment layer, a governance layer and a service layer, multi-scale spatio-temporal data is used as a substrate, and a virtual-real mapping and intelligent simulation technology is used to simulate the ocean holographic environment comprehensive digital twinning system. A full-dimension virtual mirror image covering the seabed, the middle sea and the sea surface is constructed, and the system serves core scenes such as a supervisor, scientific research cooperation and ocean engineering. According to the digital twin system, a six-layer layered architecture is adopted, edge computing and cloud computing collaboration are combined, a data-model-service-governance-environment multi-dimensional collaboration system is formed, data-driven decision making and dynamic optimization are achieved, and the marine monitoring efficiency and accuracy are improved.

The invention relates to the technical field of data processing, in particular to an industrial environment data acquisition device and method, and the device comprises a sensing layer, an edge calculation layer, a cloud platform layer, and an application layer. Compared with a traditional scheme in which a single sensor is adopted to work independently, software synchronization is relied on, and signal conditioning parameters are fixed, the technical scheme of the invention realizes significant breakthrough through collaborative design of a multi-mode sensor array and an intelligent signal conditioning system, microsecond-level clock synchronization is realized by adopting a hardware-level PTP protocol, and the signal conditioning precision is improved. The program control amplifier is combined with the self-adaptive anti-aliasing filter, so that the conditioning circuit can be self-adaptive to the working condition change of equipment; meanwhile, nanosecond-level real-time processing of signals is achieved through an FPGA + ARM heterogeneous processor architecture, abnormal high-speed sampling is dynamically triggered in cooperation with an ARM, compared with a traditional scheme, the real-time performance is improved by three orders of magnitude, meanwhile, invalid data transmission is reduced by 80%, and a high-timeliness and low-redundancy intelligent monitoring solution is provided for early failure early warning of industrial equipment.

The invention discloses an adaptive protocol stack reconstruction and transaction-level data control method for 5G communication, and aims to solve the technical problems of fixed protocol stack structure, coarse transmission control granularity and the like of an existing communication module. The method comprises the following steps: firstly, analyzing an application layer communication session initialization instruction, generating a communication task label, acquiring 5G network environment state data in real time, and constructing a network state feature vector; then, the communication task label and the network state feature vector are input into a protocol mapping decision map, a protocol reconstruction instruction is generated, loading and unloading of a protocol module in a protocol stack resource pool are controlled, a dynamically-adaptive protocol path is constructed, and a transaction identifier is allocated to the protocol path; and establishing a transaction-level data buffer area based on the transaction identifier, performing priority marking on the data packet, and executing differentiated dynamic flow control operation according to the network state feature vector and the priority label, thereby realizing high-reliability and low-delay 5G communication data management.

The invention discloses a manufacturing enterprise data center system and method based on a large model, and the system comprises a data collection layer which is used for constructing a multi-source heterogeneous data collection system; the data storage layer is used for constructing a data warehouse system comprising multiple layers and realizing data lake storage; the data processing layer and the service layer are used for deploying a model and integrating an anomaly detection module, a multi-modal fusion engine and a prediction model; the service layer is used for an interface group, integrating a Drive / ClickHouse query engine and a Redis cache, and supporting multi-condition combined query and millisecond response; deploying a visual service module and an RBAC / ABAC authority control system to realize data grading sharing; and the application layer is used for developing a production optimization application, a supply chain management application and a CRM application. Through a uniform data acquisition interface and an efficient data acquisition tool, mass data inside and outside an enterprise can be integrated, data islands are broken, centralized management and sharing of the data are realized, and availability and value of the data are improved.

Various embodiments of the technology described herein cause an LLM to intelligently process data based on a user query and a schema determined for a data set. Certain embodiments programmatically leverage an LLM and utilize its output based on a user query. In this manner, data is processed without the LLM having to access an entire data set, and instead only utilizes information associated with the user query and the schema. The schema comprises a textual description, such as a string of alphanumeric characters, that describes the data, data types, and / or data structure of the data set. Embodiments of the technology described herein are performed by an LLM interface layer separate from a user device layer and an LLM layer. The LLM interface layer is positioned between an LLM abstraction layer and an application layer by which a user can interface with the LLM interface layer.

The WTRU may determine that it has a need to request a NAS network service and may generate an application layer NAS message that comprises the request. The WTRU may determine, based on configuration information, one or more network nodes configured to provide NAS network services. The WTRU may determine, based on analytics data and policy data, a first network node in the one or more network nodes to which the application layer NAS message may be sent. The WTRU may determine, based on the policy data, one or more parameters for sending the application layer NAS message to the first network node. The WTRU may determine an endpoint address associated with the first network node. The WTRU may send the application layer NAS message to the first network node using the endpoint address and the one or more parameters.

The invention relates to an implementation method and device for an EtherCAT master station system based on a non-operating system, and the method comprises the steps: achieving the adaptation of an SOEM protocol stack to a hardware abstraction layer of an EtherCAT master station through the byte order processing based on a bit mask and a staged shift strategy and the EtherCAT frame receiving and transmitting control based on annular buffer area index management; on the basis of non-real-time task priority scheduling processing of a hardware timer, main circulation driven by a single-thread event and overtime monitoring of a blocking communication synchronization mechanism in combination with the timer, adaptation of an SOEM protocol stack to an EtherCAT master station operating system abstraction layer is achieved; through network scanning and communication parameter configuration, a memory management mechanism based on CoE standard mapping and a periodic position mode of a slave station, adaptation of an EtherCAT master station application layer is realized. According to the invention, through optimization and adaptation of the SOEM protocol stack, operation-system-free and microsecond-level communication jitter of the EtherCAT master station system is realized.

An intention-driven low-orbit satellite network SRv6 routing management and control method comprises the following steps: step 1, receiving and analyzing a user service demand from an application layer, and generating a corresponding intention label according to a service type; step 2, collecting state information of a satellite network in real time, tracking and recording satellite node position change and link state conversion, and establishing a complete network state database; 3, calculating a routing path meeting the service quality requirement by adopting an improved breadth-first search algorithm; 4, monitoring the state change of the inter-satellite link in real time; step 5, continuously monitoring fault events in the network, immediately starting a pre-calculated standby path when a fault is detected, updating a corresponding SRv6SID list, and performing rapid path switching; and step 6, dynamically adjusting the network flow. The method not only can adapt to the dynamic characteristics of the low earth orbit satellite network, but also can effectively meet the differentiated service requirements, and has the rapid fault recovery and load balancing capability.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention discloses a quality management and control system for fabricated decoration construction, and the system comprises a sensing layer which constructs a multi-modal data collection network, carries out the three-dimensional real-time data synchronous collection through combining logistics API docking and an OCR recognition system, and constructs a construction process digital twin bottom plate; in the edge calculation layer, an edge node carries out lightweight processing on the original data; the cognitive layer is used for calling a Prolog rule through a process knowledge graph engine to reasone the feature snapshots, carrying out defect instant diagnosis and dynamic constraint propagation, outputting a root cause path with probability weight through three-stage verification, and quantifying intervention influence; the decision-making layer is used for constructing a dynamic prediction model based on a bidirectional LSTM and an attention mechanism, automatically activating a compensation mode when key interference is detected in combination with an anti-fact memory bank and a case-based reasoning compensator, and generating an alternative scheme of optimal cost / optimal construction period / comprehensive balance through a multi-target optimizer; and in the application layer, a Unity engine is utilized to develop the digital twinborn billboard.

The invention provides a multi-screen cabin entertainment system, and relates to the technical field of embedded systems and intelligent cabins, and the multi-screen cabin entertainment system is characterized in that an application layer is deployed with a plurality of cabin entertainment applications, and when the application layer is started, content rendering requests are sent out respectively; the hardware layer is provided with a development board, is provided with a CPU core cluster and a coprocessor, and is connected with different cabin display screens through different video output interfaces. The system layer respectively distributes an independent thread and an independent frame buffer area for each content rendering request on the CPU core cluster, and establishes a mapping relation between each frame buffer area and each video output interface; and sending operation result data of each independent thread to a coprocessor for rendering, correspondingly storing a rendering result in a frame buffer area, and then sending the rendering result to a cabin display screen for display through a video output interface. The method has the advantages that on the premise that the number of hardware coprocessors is not increased, complete separation of multi-screen rendering threads and frame buffer is achieved, and multiple cockpit entertainment applications can run at the same time and do not interfere with one another.

The invention relates to the technical field of power distribution network operation optimization, and discloses an intelligent power distribution network distributed power scheduling system based on edge computing, which comprises a data acquisition and preprocessing module, an edge computing and local scheduling module, an SDN network control module, an application layer global scheduling module and a scheduling execution and control module, according to the invention, by introducing the edge computing node, local preprocessing of data and generation of a preliminary scheduling strategy are realized, delay of data transmission to the central server is significantly reduced, and the system can dynamically adjust the scheduling priority in combination with the improved particle swarm optimization algorithm, so that the scheduling efficiency is improved. Three core indexes of renewable energy utilization rate, power distribution network loss and voltage stability are comprehensively considered, and a more accurate and efficient local scheduling strategy is generated. And meanwhile, the SDN network control module ensures that high-priority data can still be efficiently transmitted when the network is congested by calculating a link priority coefficient, so that the real-time performance and the reliability of the system are further improved.

Embodiments include methods for a user equipment (UE) configured to report quality of experience (QoE) measurements in a radio access network (RAN). Such methods include monitoring performance of one or more services, of a UE application layer, that communicate with corresponding one or more application servers via the RAN. Such methods include, based on the monitoring, detecting one or more incidents that negatively affect user QoE and classifying each of the incidents as one of a plurality of categories of severity. Such methods include sending to a RAN node a message that includes the following: a QoE incident report that includes at least one count of incidents during a measurement period, with each count corresponding to a different category of severity; and results of radio measurements performed by a UE radio layer during the measurement period. Other embodiments include complementary methods for a network node or function (NNF).

The invention discloses an intelligent operation and maintenance distributed cloud native application computing method, and relates to the technical field of cloud computing, and the method comprises the steps: collecting heterogeneous data of an application layer, a middleware layer, a container layer and a service governance layer, constructing a dynamic multilayer dependency graph, and explicitly modeling a cross-layer dependency relationship and implicit dependency caused by resource competition; based on the spatio-temporal features of the spatio-temporal diagram neural network learning map, predicting a cascade fault risk and outputting a high-risk sub-graph; candidate operation and maintenance operations are generated through reinforcement learning, indirect negative influences of the operations on non-target components are evaluated in combination with a causal inference model, and risk filtering operations are verified through a multi-dimensional safety threshold value; and after the safety operation is executed, continuously optimizing the model and the atlas through a feedback closed loop. According to the method, deep insight, accurate risk prediction and self-adaptive decision making of side effect minimization can be carried out on a complex dependency relationship, and the system stability and the operation and maintenance efficiency are remarkably improved.

The invention belongs to the technical field of network security, and discloses an encrypted enterprise network data security access method and system, and the method comprises the steps: constructing a biological recognition unit and an equipment binding unit, recognizing multi-dimensional biological data through the biological recognition unit, and carrying out the identity authentication of a user through the combination of a dynamic token generation time binding mechanism; the equipment binding unit associates the identity authentication result with the hardware fingerprint of the security access terminal to generate a digital identity certificate; a basic key is generated and updated in a rolling mode through a quantum random number generator; dual protection is carried out on the multi-dimensional biological data through a hierarchical encryption unit, the hierarchical encryption unit comprises an application layer and a transmission layer, the application layer adopts an SM4 algorithm to encrypt service content data in the multi-dimensional biological data, and the transmission layer adopts an SM2 algorithm to generate a session key to carry out forward secrecy; the security access of enterprise network data is ensured, and the security protection capability of the enterprise network is comprehensively enhanced.

The invention relates to a QUIC protocol-based distributed node unified identity authentication method and system, belongs to the technical field of network security, is applied to a client, and comprises the following steps: registering a client domain name identity through a certificate authority, obtaining a client certificate, and pre-storing a server certificate chain; generating a QUIC initial data packet according to the target server domain name identifier and the client certificate, and sending the QUIC initial data packet to the server; receiving a QUIC handshake data packet returned by the server; verifying the legality of the server certificate based on the server certificate chain, and if the server certificate is legal, calculating a pre-master key according to the client DH private key and the server DH public key; deriving a 1-RTT session key based on the pre-master key; generating a signature verification message, and sending the client certificate and the signature verification message to a server; and synchronously using the 1-RTT session key to encrypt the application layer data with the server, and transmitting the data to the server in the 1-RTT encryption space of the QUIC protocol. According to the invention, the reliability of node identities and the confidentiality of data are ensured.

The invention provides a digital twin hydrological station system which is characterized in that the system comprises a layered architecture system and a multi-dimensional safety protection mechanism, the layered architecture system is composed of a sensing layer, a transmission layer, a data layer, a model layer, an application layer and a display layer from bottom to top, the sensing layer comprises a heterogeneous hydrological monitoring equipment cluster, and the transmission layer comprises a heterogeneous hydrological monitoring equipment cluster. The equipment cluster is provided with a water level sensor array, a Doppler flowmeter, a spectrum water quality analyzer and an environment monitoring terminal, and total element acquisition of hydrological element data, equipment operation state data and environment data is realized; the transmission layer adopts a hybrid communication protocol stack, integrates an ultra-short wave communication module, an NB-IoT narrowband communication module and an optical fiber communication module, and constructs a multi-path redundant transmission channel, and a key data transmission channel is configured with a dynamic link selection algorithm; the data layer comprises a distributed storage architecture, adopts a mixed storage mode of a relational database cluster and an unstructured database, and is internally provided with a data cleaning engine and a spatio-temporal data fusion module.

The invention discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.

The invention relates to the field of network security, and discloses an identity authentication unloading method and system based on an intelligent network card. The method comprises the following steps: presetting a master key in an intelligent network card; presetting a master key in the intelligent network card; receiving client request data forwarded by the application layer and extracting a service identifier; generating a private key of the corresponding service through the service identifier and the master key; acquiring a local authentication message, signing the local authentication message by using the private key, generating a server signature value, and sending the server signature value to the client through the application layer to trigger a client authentication process; receiving client certificate data forwarded by the application layer and extracting a certificate signature value; and verifying the certificate signature value by using a preset certificate issuing mechanism public key and generating a verification result, and outputting feedback information based on the verification result. According to the method, a hardware-level unloading function in an identity authentication process is realized through the intelligent network card, and the problems of high calculation overhead and poor expansibility of traditional TLS bidirectional authentication are solved.

The invention relates to the technical field of network flow analysis, in particular to a non-intrusive network flow real-time analysis method and system based on an eBPF, and the method comprises the steps: deploying an eBPF program in a kernel mode, and capturing a network data packet entering a kernel protocol stack in real time; identifying an application layer protocol type and analyzing a key field; the structured data is transmitted to the user mode through the annular buffer area; and the user state carries out TCP stream recombination and session-level aggregation statistics on the structured data. According to the invention, on the premise that application codes are not modified, real-time and structured protocol analysis can be carried out on various common application layer protocols in a kernel layer; end-to-end delay is accurately disassembled through a full-link delay disassembling mechanism, and a performance bottleneck link is positioned; the analysis result is output in the form of structured data, second-level performance statistics and abnormal behavior detection are supported, and the operation and maintenance analysis efficiency and the system observability are remarkably improved.

The invention discloses a reliable communication system based on a publishing and subscribing mode in a satellite-ground weak network environment, and aims to solve the problems of high round-trip delay, limited link bandwidth, high instantaneous packet loss rate, easiness in blocking and switching influence of links and the like in a data transmission process between a low-orbit satellite and a ground station. A publishing and subscribing model is introduced into an application layer to realize service logic decoupling, deep fusion with an underlying enhanced QUIC transmission protocol is realized, and mechanisms such as message persistence, breakpoint resume, forward error correction and connection migration are combined, so that stable, orderly, complete and safe transmission of service data can still be ensured under the conditions of link instability, quality fluctuation and communication interruption, and the service data transmission efficiency is improved. And high availability and continuity of a satellite-ground communication task under a weak network condition are realized.

The invention provides a carbon emission digital management system based on a cloud platform. The carbon emission digital management system comprises a data acquisition layer, a hardware resource layer, a data management layer, a platform algorithm layer, an application layer and a display layer, according to the system, through multi-source data acquisition and data analysis based on an AI large model, artificial filling errors are avoided, meanwhile, carbon emission factors are dynamically adjusted through introduction of machine learning and automatic calculation, and the carbon emission accounting precision is comprehensively improved; secondly, the system is combined with a digital twinning technology to help enterprises to realize multi-level carbon emission trend analysis of groups, enterprises, processes, equipment and the like, comprehensive strategy optimization is carried out through multi-level data arrangement, and wider application services are provided; besides, the system is in butt joint with international standards such as the international carbon market and CBAM, can help enterprises to monitor carbon price fluctuation and predict in real time, intelligently adjust carbon tax and provide a compliant carbon transaction scheme, and helps the enterprises to reduce cost, thereby realizing comprehensive upgrading of carbon management from static statistics to intelligent prediction and optimization.

The system comprises a data acquisition layer, a data processing layer, a model layer and an application layer, the data acquisition layer is used for acquiring multivariate heterogeneous data in real time, the data processing layer is used for processing the acquired data, the model layer is used for training the processed data, and the application layer is used for applying the trained data to the data processing layer. And the application layer is used for performing dual drive by adopting a time sequence processing block and point prediction and probability prediction, the time sequence processing block is a double-branch parallel backbone network integrating a Decoder-Only Transform network and a multi-scale graph neural network, and the application layer is used for evaluating the running state of the electric energy metering device according to a predicted value and a predicted probability calculated by the model layer at a certain moment. According to the charging pile operation state prediction system based on time sequence analysis, the operation state of the electric energy metering device can be monitored more comprehensively and accurately, potential abnormity and fault hidden dangers can be found in time, early warning is carried out in advance, and economic losses caused by metering errors and equipment faults are effectively reduced.

The invention discloses an intelligent facial skin care monitoring management system which is composed of a hardware layer, a data layer, an algorithm layer and an application layer, the hardware layer collects facial skin images and external environment data through an RGB camera, a UV light sensor, a depth sensor and an environment sensor, and the data layer achieves encryption transmission and cloud storage of the collected data. The algorithm layer performs skin state detection, classification and trend prediction based on multi-modal data fusion, a deep convolutional neural network and a time sequence model, and the application layer provides skin care scheme suggestions and virtual trial functions through a personalized recommendation algorithm. The system can detect the multi-dimensional features of the skin at high precision, generate a dynamic skin care scheme, predict the change trend of the skin state, and improve the user experience through a visual interface and an interaction function. The method has the advantages of being high in detection precision, scientific in recommendation scheme, accurate in trend prediction, safe in user privacy and the like, and can be widely applied to skin care product research and development, personalized skin care management and beauty industries.