1160 results about "Micro services" patented technology

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

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 relates to the technical field of organic light-emitting display data processing, in particular to an adaptive adjustment method for active matrix organic light-emitting display. In combination with current stress parameter fluctuation captured by the programmable current sampling module and heat distribution data of the temperature sensor network, the dynamic aging model micro-service cluster is input for parameter coupling calculation, and time-varying degradation characteristic parameters are output; the containerized simulation micro-service constructs a virtual aging scene based on the parameters and generates a compensation coefficient matrix; according to the method, the problem of compensation deviation caused by the fact that a traditional static model cannot adapt to current stress, temperature bias and a material relaxation coupling effect is solved, dynamic balance of display brightness, color gamut and power consumption is achieved, and the service life of a display panel is prolonged.

The invention provides a building full life cycle data run-through fusion method, and belongs to the technical field of building data fusion, and the method comprises the steps: constructing an innovative architecture comprising a data collection micro-service cluster, a service registration center, a service monitoring center and a resource scheduling center, and configuring structured and unstructured data collection agents for different data sources. The system comprehensively collects the full life cycle data of a building, and constructs a service dependence model through micro-service operation index monitoring and resource dynamic scheduling. The technologies of deep data preprocessing, standard conversion, feature mapping and the like are adopted, and a deep neural network and a spectral clustering algorithm are utilized to construct a data consanguinity map and perform coupling degree analysis. By configuring a data fusion service cluster, formulating a data fusion execution scheme and a fallback mechanism and establishing a fusion quality evaluation system, intelligent acquisition, processing and management of building full life cycle data are realized, and the problem that the accuracy and consistency of fusion results cannot be ensured due to lack of a data fusion mechanism at present is solved.

The present invention belongs to the fields of network security, distributed systems, zero trust, and micro-services, and disclosed are a method for a micro-service dynamic access control policy under a zero trust architecture, and an apparatus. The method specifically comprises: obtaining context information in a micro-service runtime context event by means of an event tracker of a node; a context manager collecting and aggregating the context information and reporting same to a micro-service control plane global manager; the global manager determining whether a new micro-service instance is present according to a global context mapping table value, and if yes, updating a global and local tag mapping table; when a message is received by a node, a message validator obtaining a tag value carried by the message, and micro-service context information corresponding to the message being obtained by means of the local tag mapping table; after a local policy table and a global policy table are queried, running a decision algorithm, and executing a corresponding access control policy. The present invention uses context to identify a communication endpoint, and has the advantages of being dynamic, precise, and extensible.

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 provides a reliability-aware dependent task unloading method based on topology reconstruction in industrial internet edge computing, which comprises the following steps of: constructing a system model covering an edge cloud network platform, an industrial cloud platform and internet of things equipment, and establishing a transmission delay model and a reliability model; constructing a task unloading mathematical model with the maximum reliability level; the method comprises the following steps: modeling a micro-service dependency relationship into a weighted directed acyclic graph based on a network flow theory, and carrying out topology reconstruction on micro-services applied to the Internet of Things through a Ford-Fulkerson approximation algorithm to obtain a micro-service grouping structure formed by minimum cut division; the micro-service grouping structure serves as priori knowledge to be input into the deep Q network, the deep Q network is used for solving a task unloading mathematical model, a task unloading strategy is dynamically adjusted, resource allocation is optimized, and an optimal calculation unloading scheme in the industrial internet edge calculation environment is obtained. The micro-service deployment is optimized, the communication overhead is reduced, and the system reliability and the resource utilization rate are improved through real-time network state dynamic decision making.

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 belongs to the technical field of cloud computing, and discloses a method for integrating and sharing internal resources of digital media in a cloud environment. The method comprises the following specific steps: S1, multi-modal resource discovery and metadata standardization; s2, distributed resource pool construction and intelligent indexing; s3, a dynamic scheduling algorithm driven by AI; s4, a permission and traceability mechanism endowed by the block chain; s5, opening a containerized micro-service interface; s6, resource optimization driven by user behaviors; and S7, edge-cloud collaborative dynamic caching is carried out. The method has the advantages that on one hand, through multi-objective optimization, delay load regulation and control and reinforcement learning, cost is reduced, response is improved, high-quality tasks are guaranteed, and resource scheduling is optimized; on one hand, a trust foundation is built for resource management and copyright is protected from behavior recording and permission grading to tracking protection by utilizing the characteristics of the block chain; in addition, micro-services are packaged, APIs and SDKs are provided, third-party access and integration are promoted, the development efficiency is improved, and all-around enabling is achieved.

The invention relates to the technical field of photovoltaic energy storage, and discloses a photovoltaic energy storage intelligent management system based on a micro-service architecture, which comprises the following modules: an environmental parameter acquisition module in which G is irradiance, [delta] T is temperature difference, RH is humidity, [alpha], [beta] and [gamma] are attenuation coefficients, dynamic influence of environment on power generation efficiency is quantified, and the environmental parameter acquisition module is used for acquiring environmental parameters; outputting the power correction factor to the cooperative control module; a power generation efficiency correction module; an edge calculation optimization micro-service module; an optical-storage-network cooperative control micro-service module; a self-adaptive prediction micro-service module; the dynamic influence of environmental factors on the power generation efficiency is accurately quantified through a nonlinear coupling model; a power correction factor can be calculated in real time, the influence of complex working conditions such as environment humidity sudden rise and irradiance instantaneous change in cloudy weather on photovoltaic efficiency is effectively captured, a scientific basis is provided for system decision making, and it is ensured that anomaly detection and energy efficiency optimization are close to actual working conditions.

An information technology system generally including a cloud-based management platform with a micro-services architecture having a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform.

The invention discloses a hybrid teaching method and system and electronic equipment, and the method comprises the steps: collecting and preprocessing teaching environment point cloud data, generating a point cloud scene model through a decoupling sparse prior guide diffusion compression model, and improving the display quality through resistance diffusion compression. And deploying a teaching management system based on the micro-service architecture to realize multi-user real-time interaction, collecting interaction behavior data, and performing deep learning analysis to generate an evaluation report. Through the point cloud processing and image optimization method, the construction efficiency and image quality of the virtual teaching environment are greatly improved, distributed deployment and real-time interaction capability of the teaching platform are realized, and technical support is provided for hybrid teaching.

The invention provides a smart space multi-screen collaborative interaction method based on micro-service architecture and edge computing, and relates to the technical field of smart screens, comprising: determining a master-slave terminal relationship based on a terminal type and a resource state by receiving a screen interaction request of a smart space terminal; a data distribution center is constructed by adopting a distributed message queue, and directional pushing based on a message theme is realized; and deploying a lightweight task execution container at the slave terminal to finish screen content rendering, and dynamically updating the terminal mapping relationship. According to the method, real-time collaborative interaction among multiple terminals can be realized, the screen content rendering efficiency is improved, the fluency of interaction experience is ensured, dynamic joining and quitting of the terminals are supported, and the expansibility is relatively high.

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.

A distributed task scheduling system based on RabbitMQ, Redis and MongoDB. The task scheduling system comprises a task system service, Redis, MongoDB, RabbitMQ and an SDK, wherein the task system service comprises a data and object storage management module, a message queue management module, a task management module, a timing task module and a workflow module. By means of the design of a task system service, Redis, MongoDB, RabbitMQ and an SDK, the functions of an asynchronous task, a timing task and a workflow in a distributed and micro-service environment are provided. By means of the technical solution of a message queue, trigger-based scheduling of a task is realized, and a reliable and unique task scheduling capability exists; moreover, a timing task trigger capability exists, and thus the present invention is suitable for a scenario in which the execution of a certain task is triggered in a timing or delayed manner; a workflow capability is provided, and thus serial and parallel execution of a plurality of tasks can be performed; and capabilities such as task rollback and interruption are provided, and thus a system which unifies the asynchronous task, the timing task and the workflow is achieved.

The invention discloses a distributed component dynamic migration-based cluster intelligent fault-tolerant method, which comprises the following steps of: firstly, collecting historical load state data of each node, container and component service of a cluster through a distributed monitoring system, preprocessing the historical load state data as input of a deep learning model, training the model by using a deep learning algorithm, and performing fault tolerance on the model; based on a prediction result of the model, after the system generates an intelligent scheduling decision and executes scheduling, the system continuously monitors cluster resources and service states, discovers resource bottlenecks or faults in time and carries out adjustment, and finally, a scheduling strategy is adjusted through real-time monitoring data feedback and incremental training is carried out on the deep learning model. According to the method, deep learning modeling and prediction are carried out on historical load state data of the cluster, the load change trend is recognized in advance, scheduling decision making is carried out, and through the cluster self-healing mechanism and prediction scheduling, the system fault time is effectively shortened, the service continuity is guaranteed, and the system reliability is improved. The method can be applied to a cloud platform, a micro-service architecture and a large-scale distributed system.

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 provides an intelligent intelligent supervision system for civil aviation VoIP communication equipment, relates to the basic field of civil aviation air traffic control equipment, and solves the limitation problems of non-centralized supervision, imperfect supervision, non-autonomous supervision and the like existing in an existing supervision mode. The system comprises a system architecture module which adopts a layered modular design to construct a double-layer system comprising a top layer management architecture and a bottom layer operation architecture; the function service module comprises an equipment state monitoring unit, a risk assessment engine and an operation and maintenance management unit; the data processing architecture forms a multi-stage processing channel by a data acquisition layer, a data transmission layer, a data storage layer, a data calculation layer and a data application layer which are connected in sequence; the technical support system comprises a micro-service component group, a continuous integration platform and a security authentication module. The intelligent supervision network architecture of the VoIP communication equipment is subjected to brand-new autonomous design, transformation from passive response to active management is completed, and the supervision efficiency and the treatment efficiency are improved.

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 discloses a self-adaptive micro-service deployment resource optimization method for cloud edge-end cooperation. The method comprises the following steps: 1) modeling a system model; 2) modeling a communication model; 3) modeling a user demand processing delay module; 4) deploying resource consumption according to a micro-service type required by user demand processing, and modeling a micro-service deployment resource consumption model and a user demand processing resource consumption model; 5) performing formulation definition on the micro-service deployment resource optimization objective function to complete heterogeneous constraint high-quality characterization; 6) aiming at the difference dimension of the multi-objective optimization function, finishing normalization processing of different dimensions; and 7) solving a multi-objective optimization problem by adopting a tabu search method to realize effective utilization of resources in a deployment process.The invention belongs to the technical field of system resource optimization of the Internet of Things, and solves the problem that the Internet of Things in the prior art has defects in the aspects of application space demand coupling, decentralized coordination and service difference.

The invention discloses a power intelligent equipment operation and maintenance management method based on artificial intelligence, and the method employs a staged training strategy, combines a LSTM and CNN mixed structure to carry out fault prediction, effectively captures an equipment operation state change trend, recognizes a potential fault mode, remarkably improves the fault prediction precision, and meanwhile, improves the fault prediction efficiency. A flexible self-healing strategy is designed according to the fault type and severity, the adaptability of a self-healing process and the capacity of dealing with complex faults are enhanced, in addition, a trained equipment health assessment, fault prediction and self-healing model is packaged into RESTfu API service in a micro-service mode and is deployed to an edge computing module through Docker containerization, and the service performance of the edge computing module is improved. Intellectualization and integration of the operation and maintenance management system are promoted, the edge calculation module inferes and monitors the equipment state in real time and generates early warning information, real-time monitoring and early warning are achieved, once abnormity is found, self-healing operation is triggered immediately, manual intervention is reduced, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of edge computing and intelligent traffic, and discloses an intelligent street lamp data analysis system based on edge computing, which comprises the steps of realizing NPU / GPU computing power unit hardware-level isolation control through physical pin mapping of an edge chip; generating a cellular topology network based on geographic coordinates, and dynamically calling cameras, communication modules and computing power units of adjacent nodes to construct a micro-service chain; binary codes are generated through illumination, rain and fog and people flow parameters, and lightweight model plug-ins are triggered to be loaded as required; a bus protocol is improved to realize pin-level computing power cross-node exclusive occupation and microsecond-level response; and the event priority and the elastic release rule are defined to ensure abnormal self-healing. According to the method, the contradiction between resource scheduling stiffness and privacy delay of a traditional edge system is broken through, algorithm self-adaption in an emergency scene is achieved through cooperation of hardware isolation and dynamic coding, data safety is guaranteed through decentralized topology and geographical driving verification, and a complete solution which is high in real-time performance, high in reliability and low in power consumption is provided for an intelligent street lamp.