944 results about "Micro services" patented technology

The invention relates to the technical field of service architecture management, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a gateway component updating and service migration method which comprises the following steps: selecting a target gateway component, collecting an access configuration data set of a source gateway operation instance, and storing the access configuration data set; registering a service node to a micro-service registration center, generating a service node topology identifier, constructing an interface routing configuration table, and converting the interface routing configuration table into a traffic forwarding strategy mapping table; releasing the source gateway connection based on the mapping table and switching the flow to the target gateway component; and after the migration service node is accessed to the target gateway, executing service response verification, link integrity verification and abnormal trigger detection, and outputting a service availability state identifier, a link tracking log file and an abnormal event trigger record. By fusing topological structure information, protocol parameters and traffic policies, an automatic migration process of configuration synchronization, policy conversion and traffic takeover is realized, and the service stability and the gateway unified management capability are improved.

The invention relates to the technical field of micro-services, can be applied to the field of science and technology finance / digital medical treatment, and discloses a high-guarantee link automatic guarantee method and device, electronic equipment and a storage medium. The method comprises the following steps: performing series recording on a calling link of a service interface of a first micro-service through a proxy probe to obtain service interface calling link data; rendering a screening result of the calling link data as a high-guarantee link panorama; analyzing the high-guarantee link panorama to obtain a high-guarantee link dependency relationship, and calculating a micro-service dependency score of the first micro-service; screening the first micro-service according to the micro-service dependency score to obtain a second micro-service; when the service interface of the second micro-service generates an alarm, automatically collecting alarm associated data; and inputting alarm data to the high-insurance decision large model for analysis, and automatically issuing and executing an alarm self-healing strategy output by the high-insurance decision large model through the model context protocol server. According to the method, high-guarantee link automation and system stability are improved.

The invention discloses a fault root cause positioning method and system for a server cluster, and relates to the technical field of network fault diagnosis. According to the method, nanosecond-level synchronous acquisition of micro-service call chains, container indexes, physical nodes and network data is realized through a precise time protocol, and a consistent data set is constructed through entity association and standardized processing; a service-resource topological graph is dynamically constructed, and an inter-service calling edge weight model is innovatively designed: a real-time load factor and a historical fault index attenuation sum processed by a Sigmoid function are fused, and the weight is periodically updated to accurately quantify the inter-node influence intensity; converting the topological graph into a Bayesian network; when a fault occurs, a three-level assembly line compression alarm is adopted, frequent item sets are mined through bitmap indexes and parallel FP-Growth, and strong causal association item sets are screened in combination with topological edge weights and KL divergence; strong causal alarm is taken as evidence, probabilistic root cause sorting is output through reverse random walk sampling, and high-precision positioning of complex distributed system faults is achieved.

The invention discloses a micro-service dynamic weight load balancing method and related equipment. The method comprises the following steps: asynchronously acquiring a multi-dimensional real-time load index of a micro-service instance, obtaining a load prediction value by virtue of a model, fusing the real-time index, the prediction value and a service health degree, calculating a dynamic weight by depending on service load data, distributing a request according to the weight, and feeding back an adjustment coefficient. According to the method, by asynchronously collecting the multi-dimensional real-time indexes, an accurate basis is provided for weight calculation, so that the request allocates the actual load of the matched instance, and the situation that the response of a high-load instance is slow due to too many requests is avoided; by introducing a load prediction value, load change can be pre-judged in advance, a weight strategy is adjusted in advance, overload when an instance load suddenly increases is prevented, and the fluctuation resistance of the system is improved; the weight is calculated by fusing multi-dimensional data, so that the comprehensive load of the instance can be comprehensively reflected, and evaluation deviation caused by a single index is avoided; a feedback mechanism can enable weight calculation to continuously adapt to a system state, and load unevenness caused by weight fixing or adjustment lag is avoided.

The invention provides a cloud native system fault root cause positioning method and device, and the method comprises the steps: determining the abnormal performance index data of each instance and a server based on the micro-service instance of each target micro-service and the performance index abnormal score of the server, so as to construct the nodes corresponding to each micro-service instance and the server, according to a fault propagation direction between the abnormal performance index data, constructing an edge between the nodes to obtain an index-level cause and effect graph; normalizing the performance index anomaly score to obtain a target anomaly score of each node, and constructing a transition probability matrix; and obtaining the access frequency of each node in the causal graph by using a random walk algorithm so as to obtain fault root cause positioning result data of the cloud native system. According to the invention, the automation degree, efficiency and accuracy of fault root cause positioning of the cloud native system can be effectively improved, the efficiency and reliability of fault early warning and recovery of the cloud native system can be effectively improved, and the operation stability of the cloud native system can be improved.

The invention relates to the technical field of container arrangement, and discloses a configuration management method and system for container arrangement deployment, the method comprises the following steps: creating a container arrangement template, and defining global environment variables by using a preset configuration file in the container arrangement template, the global environment variables comprising public environment variables and private environment variables of micro-services; configuring a preset configuration file based on the container arrangement template, and determining a configuration mapping resource of the global environment variable; obtaining the name and the type of each micro-service to be configured, and determining the mapping relationship between each micro-service and the configuration mapping resource based on the name and the type; and creating a container corresponding to each micro-service, and injecting the associated configuration mapping resource into the corresponding container according to the mapping relationship to complete container deployment. According to the method, redundant configuration of parameters is reduced through centralized configuration management; through one-key deployment optimization, the configuration efficiency is improved, homomorphic extension is realized based on a dynamic configuration reference mechanism, and the compatibility is enhanced.

The invention relates to the technical field of micro-service operation and maintenance, and discloses a full-link intelligent fault simulation and assessment defense method in a micro-service scene. The method comprises the following steps: constructing a dynamic dependency graph based on a service registration center and real-time communication traffic; performing matching backtracking on historical faults according to the atlas, and generating a fault injection point list and a propagation path set sensed by the atlas; in the isolated environment, driving the programmable agent to perform multi-dimensional fault injection, and synchronously acquiring full-amount system response signals; performing multi-dimensional deviation calculation on the signal and the base line to form an observation record containing deviation intensity and propagation rate; and training a graph neural network model with time sequence dependence understanding capability based on the record, and carrying out fault mode identification and evolution prediction on an online real-time link, and outputting a risk assessment report. According to the method, the authenticity of fault simulation and the initiative of risk prediction are improved.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

The invention relates to the technical field of project management, in particular to an enterprise project management system and method oriented to multi-project collaboration. A resource scheduling module; a progress control module; a cost quantification management module; and a risk management module. According to the scheme, through a micro-service architecture and a distributed data storage technology, multi-project full-period collaborative management is realized. And each module can operate and expand independently, so that the flexibility and expandability of the system are remarkably improved. The distributed data storage can ensure the real-time synchronization and quick access of the data even under the condition that a plurality of projects run in parallel. By constructing the incidence relation matrix, the system can clearly present the relation between each task and the resource of the project, and solid data support is provided for subsequent resource allocation and task management. The optimal configuration of resources and the smooth promotion of projects are realized, and the management efficiency of enterprises and the project success rate are remarkably improved.

The invention discloses a BIM building intelligent integrated management system based on Internet of Things control, and relates to the technical field of networking technology and building intelligent management, and the system comprises a sensing layer, a transmission layer, a platform layer and an application layer. The sensing layer comprises a multi-source heterogeneous sensor cluster and is divided into an environment monitoring unit, an equipment state acquisition unit, a personnel positioning unit and a structure health monitoring unit, and each unit is provided with a corresponding sensor; the transmission layer adopts a LoRaWAN and 5G fusion architecture and comprises an edge computing gateway, a TSN switch and a data security encryption unit, and the gateway can perform frequency domain analysis and the like; the platform layer is based on micro-service and is provided with a multi-source data fusion engine and a BIM model dynamic updating module; the application layer provides an equipment fault prediction module, an energy optimization module and a safety risk early warning module, and all the modules adopt corresponding algorithms. According to the invention, a hierarchical collaborative architecture is constructed, a multi-source sensor, a heterogeneous network and the like are integrated, deep integration of multi-dimensional management of a building is realized, and the problems of subsystem independence and data obstruction of a traditional system are solved.

The invention relates to the field of data processing, and discloses a cloud computing-based data processing method, which comprises the following steps of: constructing a reference count management micro-service cluster at a cloud end, receiving a reference count request carrying a count type through an API (Application Program Interface) gateway, and analyzing the reference count request into a structured operation instruction; reading a first state identifier of the referenced object and a second state identifier of the reference object from the distributed cache system according to the metadata snapshot; according to the method, through dynamic scheduling of a reference counting management micro-service cluster and a container arrangement engine, efficient allocation and scheduling of computing resources can be realized, maximization of resource utilization is ensured, resource waste is avoided, and high-speed query capability of a distributed cache system and a memory database is utilized; the state identification of the object can be retrieved at a millisecond level, the system response speed is improved, and the performance requirement under a high-concurrency scene is met.

The invention provides a data synchronization method and device based on Spring Batch and a large model. Synchronously initializing a large model micro service and a Spring Batch framework, and establishing a model service hot standby mechanism; obtaining data source features through a metadata scanning engine driven by a large model, and generating an adaptive synchronization strategy; optimizing a query plan based on the synchronization strategy, dynamically controlling data fragmentation and executing streaming preprocessing and semantic mapping conversion; a target table structure is adapted, a three-level conflict detection mechanism is adopted to eliminate data conflicts, and meanwhile transaction processing and batch writing logic are optimized; the data quality is verified through three-level verification, performance parameters are dynamically adjusted and optimized, and fault-tolerant recovery and resource elastic expansion and contraction are achieved; metadata are synchronized, an execution report is generated, knowledge base self-learning is completed based on task full-link indexes, and a data consanguinity map and a reusable processing case library are constructed. According to the scheme, diversified and dynamic enterprise data synchronization requirements can be met.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a fault insight and self-healing method and system based on chain control. According to the chain control-based fault insight and self-healing method, indexes, logs and tracking data of a micro-service environment are dynamically collected and fused; the method comprises the following steps: dynamically constructing and visualizing a service dependent topology, realizing dynamic topology construction and intelligent visual rendering, performing intelligent root cause analysis and predictive diagnosis based on multi-dimensional data, realizing self-adaptive early warning, generating an intelligent decision, performing closed-loop learning optimization based on an execution result, and realizing automatic fault repair and continuous optimization. According to the fault insight and self-healing method and system based on chain control, a predictive early warning mechanism is established, automatic fault self-healing is realized, and through intelligent and automatic means, manual intervention is reduced, the operation and maintenance cost is reduced, the average repair time is shortened, the service continuity is guaranteed, and the system availability is improved.

The invention relates to the technical field of modularized laboratory project management, and discloses a modularization-based laboratory project management method, which comprises the following steps: S1, system architecture design: adopting a micro-service distributed architecture, and cooperatively working through a plurality of business modules; s2, module management and project creation; the invention also provides a laboratory project management system based on modularization, which comprises a project management engine module, an intelligent key node reminding system, a sample full life cycle tracking management module and a multi-party collaborative feedback management module, the project management engine module comprises three core business modules, namely, a project management module, a sample management module and a contract management module, standardized management and efficient operation of laboratory projects are achieved through a project module engine, an intelligent node reminding mechanism and a full-process tracking algorithm, the project management efficiency can be remarkably improved, and the project management efficiency is improved. And the time cost of a project preparation stage is saved.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent pre-auditing system and method for public data resource registration elements with a large model, and the method comprises a management side rule base construction step and an operation side auditing step. The method has the beneficial effects that automatic extraction, structured conversion and compliance pre-auditing of registration elements are realized by applying a multi-modal data analysis engine and a large model technology, and manual intervention links are reduced by more than 40%. A multi-dimensional rule knowledge base containing national standards, industry specifications and local regulations is constructed, dynamic loading and self-adaptive updating of verification rules are supported, and the adaptability to different types of data resources is improved. And a pre-auditing device based on the micro-service architecture is designed, and technical support is provided for marketization configuration of data elements. Through pre-auditing quality problem big data of process precipitation, the standard conformity of a data production end is reversely optimized, and a'registration-feedback-improvement 'closed-loop treatment mechanism is formed.

The invention provides a framework migration method and system for a heterogeneous micro-service architecture, and relates to the technical field of micro-service architecture.The framework migration method comprises the steps that migration priorities are determined through topological sorting based on service calling features, a unified service interface description file is generated, a link tracking probe is injected, and service adaptation layer codes are generated; flow control is carried out in combination with a variation gradient token bucket algorithm, and service fusing and degradation are realized by adopting an exponential backoff algorithm and a sliding time window mechanism. According to the method, the problems of complex service dependence, difficulty in flow regulation and control and the like in heterogeneous architecture migration are solved, the security and stability of framework migration are improved, and the operation and maintenance cost of the system is reduced.

According to the back-end system fault self-recovery method and device, the storage medium and the computer equipment provided by the invention, in the system operation process, multi-dimensional state analysis is performed on the operation data collected by the system micro-service in real time to obtain the operation state; when the operation state is abnormal, the fault analysis model is used for performing fault analysis on the operation data, so that the fault positioning efficiency is improved; after fault data output by the model are obtained, a self-healing strategy corresponding to the fault data is obtained through matching in the dynamic knowledge base, then self-healing operation is executed on the micro-service, and quick response of fault processing is achieved. If the self-healing result is failure, self-healing data in the self-healing operation process is obtained, the self-healing strategy of the micro-service is adjusted to be more adaptive to the actual fault condition, then the self-healing operation is executed on the micro-service again based on the new self-healing strategy until the self-healing result meets the self-healing ending condition, and therefore the flexibility of system fault self-healing is improved; and the influence of the fault on the system stability is reduced to the greatest extent.

The invention discloses a 1 + N + X layered architecture based on an industrial Internet of Things platform, and belongs to the technical field of industrial Internet of Things platforms, the platform adopts a micro-service architecture, supports distributed cluster deployment and elastic telescoping, and is divided into three layers: an Internet of Things data middle platform is used as a unified data hub, and is modularly accessed to industrial protocol equipment; data cleaning, storage and distribution are completed; the platform application realizes a core general service and interacts through a standardized API in an independent micro-service form; the application plug-in constructs a personalized extension function based on the first two layers of capabilities, and realizes rapid iteration through mechanisms such as framework development and containerization deployment. According to the architecture, module independence is improved through layered decoupling, heterogeneous data are processed in a centralized mode through a data platform, core services are packaged in a standardized mode, third-party development is supported through a plug-in mode, the problems that an existing architecture is insufficient in expansibility, poor in reusability and the like are solved, the requirements for industrial scene mass data processing and service quick response can be efficiently met, and the application prospect is wide. The method is suitable for large-scale industrial Internet of Things application.

The invention discloses a shared device replacement management method and system based on AI glasses code scanning confirmation. The method comprises the following steps: generating a replacement data packet containing a dynamic verification code request; generating a unique dynamic verification code based on the received returning data packet, and synchronously pushing the unique dynamic verification code to the renter mobile terminal and the returning point micro server; collecting a dynamic verification code displayed on a renter mobile terminal through an interactive interface of the AI glasses; and carrying out matching verification on the dynamic verification code and a corresponding dynamic verification code stored in the micro server of the returning point, automatically finishing returning confirmation operation of the shared equipment at the returning point after the verification is passed, and updating the state of the equipment. According to the embodiment of the invention, rapid confirmation and accurate matching of the shared device replacement can be realized, and the security and efficiency of the replacement process are improved.

The invention relates to an edge computing network-oriented micro-service and distributed database collaborative deployment system. The system comprises a database copy number dynamic optimization unit which obtains a dynamic optimization strategy of the copy number of a database based on a queuing gradient projection elastic scaling algorithm; a deployment strategy dynamic adjustment unit calculates end-to-end time delay based on the micro-service routing path and the database routing path; calculating a data inconsistency measurement index based on the number of each copy of the database; dynamically adjusting the deployment strategy based on the resource constraint between the end-to-end time delay and the data inconsistency measurement index and the dynamic optimization strategy of the number of copies of the database; the deployment strategy comprises micro-service instance deployment, database copy deployment and optimal routing path selection; according to the MEC scene-oriented distributed database design and fine-grained collaborative deployment optimization method, a message queue, a queuing theory and a Canal database incremental updating mechanism are integrated, and the micro-service application performance and the data query efficiency / reliability are improved.

The invention relates to the technical field of distributed computing and deep learning, and discloses a computing power pool intelligent integration and elastic scheduling method based on deep learning, and the method comprises the steps: constructing a multi-level graph convolution network, and carrying out the modeling of a dynamic interaction relation between micro-services; constructing a multi-dimensional scoring model based on the computing power state index and the network state index; dynamically adjusting the micro-service granularity according to the task characteristics and the resource state; predicting a service interaction mode and a resource demand change by utilizing graph reinforcement learning; searching an optimal service deployment scheme by adopting a multi-objective optimization algorithm; according to the method, the resource utilization rate is improved through dynamic service granularity adjustment and computing power aware routing, predictive resource isolation and service capacity expansion are realized based on graph reinforcement learning, dynamic balance of delay, throughput and resource efficiency is realized by adopting multi-objective optimization, the problems of resource fragmentation and performance interference are effectively solved, and the resource utilization rate is improved. And the stability and the reliability of the system are obviously improved.

The invention relates to the technical field of agricultural internet of things and intelligent regulation and control, in particular to a tobacco planting greenhouse microclimate real-time monitoring and regulation and control method based on a cloud platform, which adopts a'cloud-side-end 'three-level architecture, deploys a multi-source sensor network layout in a tobacco planting greenhouse, is connected with terminal equipment and is used for monitoring and controlling the microclimate of the tobacco planting greenhouse. The multi-source sensor network is a three-level sensor network, the data acquisition and processing module adopts LoRaWAN and 4G / 5G dual-mode redundancy transmission, deploys an LSTM prediction model at an edge computing node, inputs time sequence sensor data, adopts a micro-service architecture, adopts a gRPC protocol for inter-service communication, and constructs a virtual tobacco planting greenhouse model through a Unity 3D engine; according to the method, environmental parameters are collected in real time through a multi-source sensor network, a regulation and control strategy is dynamically optimized through a machine learning algorithm, and precise control over the tobacco growth environment is achieved.

An information technology system generally includes a cloud-based management platform with a micro-services architecture deploying a set of adaptive intelligence facilities that can be configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform and a set of data storage facilities that can be configured to store data collected and handled by the platform. The data can be related to at least one of the value chain network entities and the features of the platform. A set of monitoring facilities can be configured to monitor the value chain network entities. The platform can be configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

The invention relates to the technical field of communication, and provides a micro-service fault root cause tracing method, system and device and a storage medium, and the method comprises the steps: collecting a standardized log from a micro-service cluster; learning log normal features in different scenes based on the standardized log, and constructing a log normal feature model based on the log normal features; extracting a global unique identifier from a first abnormal log marked by the log normal feature model, and connecting the target service logs in series according to the global unique identifier to generate a fault feature map; analyzing the fault characteristic spectrum to obtain an abnormal propagation path, and performing identification based on the abnormal propagation path to obtain a root cause candidate log; obtaining a root cause candidate list based on the root cause candidate log; the root cause candidate list is used for indicating the target fault root cause. According to the method and the device, the micro-service fault root cause can be accurately traced, and the fault can be quickly positioned, so that the troubleshooting efficiency is improved.

The embodiment of the invention discloses a security deployment method and device for enterprise-level applications and a medium, and relates to the technical field of security deployment, and the method comprises the steps: obtaining application topological graph data generated by a preset visual development platform, so as to split the application topological graph data into an atomized micro-service unit set through a quantization deconstruction engine, generating a micro-service dependency tree; scanning the target deployment environment, extracting an environment feature fingerprint of the target deployment environment, and generating an environment adaptation layer code according to the environment feature fingerprint; injecting an environment adaptation layer code into each atomized micro-service unit to generate an environment perception type micro-service package, and constructing a cross-cloud communication link matrix based on the micro-service dependency tree; and based on the environment-aware micro-service packet and the cross-cloud communication link matrix, executing a deployment verification test in the sandbox environment, generating a deployment verification report, and according to the deployment verification report, distributing the environment-aware micro-service packet to a target deployment environment node.

The invention discloses a cross-number user portrait association and intelligent dialogue optimization method based on voiceprint recognition, and the method comprises the steps: receiving and processing an incoming call voice stream of a user, carrying out the detection of an adversarial sample after the voice preprocessing, and extracting a voiceprint vector from compliant voice through an ECAPA-TDNN model; the user is retrieved and matched through FAISS / Milvus to obtain the user, the portrait, the work order and the historical record of the user are inquired, and intelligent customer service personalized service guidance is achieved. Wherein noise reduction adopts an RNNoise model of a GRU and a dynamic threshold strategy for user matching, a user portrait is based on a hierarchical label system, a block chain and a private cloud are combined for storage, and the cross-number user identification efficiency and the service personalization level are improved through micro-service architecture deployment.

The invention relates to the field of micro-services, and provides an elastic resource scheduling method and device for micro-services and electronic equipment.The method comprises the steps that a to-be-scheduled micro-service system is determined, and the micro-service system comprises a server and a micro-service instance deployed on the server; constructing a long-term scheduling optimization model by taking the minimization of the long-term average request time delay of the micro-service system as a target; converting the long-term scheduling optimization model into a time slot scheduling optimization model in each time slot based on a Lyapunov optimization method; and obtaining an initial solution of the optimization model based on a near-end optimization strategy, then optimizing the initial solution for a dynamically changing request through a pre-trained deep reinforcement learning model, and performing elastic resource scheduling of the micro-service based on an optimization result. According to the elastic resource scheduling method and device for the micro-service and the electronic equipment provided by the invention, scaling and migration of the micro-service instance are performed aiming at the dynamic request arrival rate, and multi-objective optimization of micro-service scaling and migration cost and user request time delay is realized.

The invention discloses a data mapping method and system for tracking information during message conversion of different tracking protocols, and the method comprises the steps: respectively configuring a tracking application agent unit and a tracking application collection unit of corresponding types for link tracking applications of different protocol types; the tracking application collection unit is implanted into a tracking application program of a corresponding protocol type in a probe non-intrusive manner to collect tracking information of the micro-service, and the tracking application collection unit generates a message of the corresponding protocol type from the collected tracking information; the kafka middleware receives the messages of different protocol types sent by each tracking application collection unit, and uniformly generates kafka messages of different themes; the collecting and summarizing unit consumes kafka messages to distinguish messages of different protocol types according to different themes, uniformly converts the messages of different protocol types into messages of one protocol type, calculates to obtain call chain information, and stores the call chain information to the storage unit. Manual intervention is reduced, migration cost is reduced, migration time is shortened, and meanwhile accuracy and integrity of data are guaranteed.

The invention belongs to the technical field of intelligent operation and maintenance of micro-service architecture information systems, and particularly relates to a micro-service anomaly positioning method and system based on dynamic topology and multi-modal data, and the method comprises the steps: obtaining a dynamic service topological graph of a micro-service system; constructing a directed weighted graph model of the service nodes in the micro-service system based on the obtained topological graph; according to the constructed directed weighted graph model, determining a calling link between system micro-services; acquiring a multi-modal observation index of each service node in the micro-service system on the calling link; calculating an observation entropy value of the micro-service system based on the obtained multi-modal observation index of each service node and the sliding window; and judging the abnormal state of the service node of the micro-service system according to the obtained observation entropy value, and completing the micro-service abnormity positioning based on the dynamic topology and the multi-modal data.

The invention discloses a mental health monitoring and nursing robot with body-multi-dimensional intelligent cooperation. The robot comprises a mobile robot platform equipped with a differential driving chassis, a laser SLAM navigation module and the like; a multi-mode sensing network integrating an RGB-D depth camera, a thermal imager and the like processes data in real time through an edge computing unit; an intelligent decision-making engine based on a micro-service architecture covers a patrol recognition module, a vital sign monitoring module, a psychological support Transform dialogue model, a multi-modal emotion recognition module and the like; and the man-machine interaction interface is connected through an end-to-end encrypted 5G network. A medicine compliance management module, an RFID medicine box and a privacy protection scheme of medicine taking action recognition and federal learning and differential privacy are further arranged. The method has the advantages that nursing efficiency is improved by more than 40%, adverse event occurrence rate is reduced by 35%, psychological intervention coverage rate is improved, medicine taking compliance is improved, sublingual Tibetan medicine detection rate is improved to more than 95%, data privacy is safe and controllable, and intelligent upgrading of mental health nursing is promoted.