1602 results about "Micro services" patented technology

The invention discloses a transformer area intelligent fusion terminal data processing system based on edge calculation, relates to the field of edge intelligence, and solves the problems of poor real-time performance and poor data exception processing accuracy of an existing intelligent fusion terminal data processing system. According to the method, communication between micro-services is efficiently managed through the container arrangement engine and the service grid, the message transmission delay is reduced, and resource allocation is further optimized through a resource scheduling algorithm based on reinforcement learning, so that system resources are used most reasonably; according to the method, anomaly detection is rapidly and accurately carried out through a space-time heterogeneous fusion network model, hierarchical processing of data and on-cloud training of the model are realized through a cloud-edge collaborative architecture, edge nodes participate in model training through a federated learning mechanism, the model performance is optimized, the transmission time of the data in the network is shortened, and the problem positioning speed is increased; the real-time performance and the reliability of an existing system are greatly improved.

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 provides a distributed system fault positioning diagnosis method and system based on log analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting call data between micro-service nodes, constructing a service dependency graph, and calculating the influence weight of each node as a fault source; and modeling the service dependency relationship graph by using a graph convolutional neural network, and constructing a fault propagation prediction model. And when an abnormal node is detected, inputting the node into the prediction model, calculating a fault propagation path and an influence range, outputting an influenced service node list and an influence degree thereof, carrying out risk grade division and alarm, and meanwhile, visually displaying the fault propagation path. According to the method, the fault source can be quickly and accurately positioned, the fault propagation influence is predicted, and the reliability and stability of the distributed system 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 provides an intelligent memory leak prediction and tracking method and system for a micro-service architecture, and relates to the technical field of memory leak prediction, and the method comprises the steps: collecting the runtime data of a service node, constructing a service calling topological graph, extracting the time sequence and topological features, and carrying out the feature fusion. And constructing a deep learning prediction model by combining a bidirectional long-short term memory network with a multi-head attention mechanism and a graph attention network, and predicting a memory use trend and a risk score of a service node. Potential memory leak nodes are identified according to the risk score and the abnormal degree predicted value, a key propagation path is identified through a service calling topological graph and a minimum cut algorithm, and finally a root service of memory leak is positioned by using a belief propagation algorithm, so that accurate prediction and rapid positioning of the memory leak in the micro-service system are realized. And the system stability and reliability are improved.

The invention relates to an LLM-enhanced intelligent micro-service migration method and system in a cloud edge environment, and the method comprises the steps: analyzing and judging whether a current exception needs to be subjected to micro-service migration or not according to a historical exception event needing to be migrated through a to-be-migrated micro-service positioning method based on LLM and LSTM, and accurately positioning a to-be-migrated micro-service causing the exception if the micro-service needs to be migrated; a multi-target micro-service migration decision-making method based on dynamic GCN and BO is adopted, and appropriate nodes are intelligently selected for each micro-service to be migrated for migration according to the long-term performance and operation cost of the micro-service. According to the method, the accuracy of judging whether the current exception needs to be migrated or not is remarkably improved, and the to-be-migrated micro-service causing the exception can be accurately positioned. The problems of network congestion, service interruption, performance reduction, unbalanced resource utilization and the like possibly caused by frequent migration are effectively avoided, so that the long-term stability of the system performance after micro-service migration is realized, and the operation cost is remarkably reduced.

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.

Provided in the present invention is a node-type edge computing system. The system comprises a micro-service module, a CI / CD module, an application management module, an observation module and a security module. The node-type edge computing module in the present invention has the characteristics of having a low network delay, supporting mass data access, having an elastic infrastructure, etc. Moreover, in addition to reducing the transmission delay, the advantages brought about by shortening a spatial distance further comprise the delay of various PLCs, gateways, routing-transfer, and network device processing in a complex network is also reduced. In addition, since the probability of network link contention is greatly reduced, the overall delay can be significantly reduced. By means of the node-type edge computing module, a distributed capability is added to a traditional cloud center, some service logic is deployed on an edge side, and related data processing is completed, such that the pressure of transmitting data back to a center cloud can be greatly relieved. The node-type edge computing module can also provide an elastic virtualization capability, such as calculation, networking and storage, based on an edge location, and can truly realize "cloud-edge collaboration".

A dynamic micro-service migration and route redirection method based on topology awareness comprises the following steps: S1, acquiring cloud computing data center and edge server resource information, an application request stream set and an initialized mirror image deployment matrix in a dynamic scene, and dividing time slot-based input request stream features; s2, in each time slot, a multi-mirror-image preliminary deployment scheme of the micro-service is obtained in a marginal gain optimal mode, and a node routing scheme is redirected according to a mirror image deployment matrix of a physical server node; and S3, according to the resource cost, the multi-mirror-image preliminary deployment scheme and the node routing scheme, screening out topology dependence pairs with abnormal bandwidths in a request stream, and based on topology perception, executing migration operation for micro-service mirror images on server nodes of links with abnormal bandwidths to obtain an optimal joint decision of micro-service deployment and request routing. According to the method, topology-aware micro-service mirror image migration and online redirection probability routing are realized in a dynamic cloud computing data center scene.

The embodiment of the invention provides a micro-service flow anomaly monitoring method and device based on traffic data, and the method comprises the steps: receiving traffic data which is continuously uploaded by a road camera and comprises text data and image data, and storing and processing the traffic data through setting a micro-service, acquiring traffic log information when the traffic data is processed by the micro-service, calculating the volatility and the variation trend of the time sequence data, and constructing a model training data set by adopting a sliding time window method according to the traffic log information; collecting traffic log information of the micro-service per minute through a parallel mechanism, and inputting the traffic log information into the trained traffic data flow analysis model to obtain a peak ratio, a change rate and a duration output by the traffic data flow analysis model; processing different levels of early warning by adopting log recording, alarm pushing and automatic current limiting measures respectively; according to the invention, the micro-service flow anomaly monitoring efficiency and accuracy of traffic data can be effectively improved.

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.

A wireless communication system includes an external provider subsystem and an electronic network subsystem in operable communication with the external provider subsystem. The electronic network subsystem is configured to provide a first microservice and a second microservice different from the first microservice. The wireless communication system further includes an in-home subsystem (i) separate from the external provider subsystem, (ii) in operable communication with the electronic network subsystem, and (iii) including a first micronet and a second micronet different from the first micronet. The first micronet is configured to operably interact with the first microservice, and the second micronet is configured to operably interact with the second microservice. The wireless communication system further includes at least one electronic device configured to operably connect with one of the first micronet and the second micronet.

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 provides an industrial big data micro-service API structure optimization method, medium and system, and belongs to the technical field of industrial big data, the system comprises an analysis data acquisition API, a data preprocessing API, a data analysis API and a data display API, and is divided into a data access layer micro-service module, a data processing layer micro-service module and a data service layer micro-service module; analyzing an API (Application Program Interface) responsibility range of the data access layer micro-service module by adopting a first machine learning model, and extracting an API calling link index; performing in-participation and out-participation standardization processing on the micro-service module of the data processing layer; an API interface version management module is established, and API interface change records are collected; analyzing the API interface version evaluation model by adopting a second machine learning model; and an API optimization evaluation function is established, API structure recombination operation is executed, and the problem that existing API responsibility division is not reasonable enough is solved.

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 provides an asynchronous service deployment method and device for an edge cloud network, and the method comprises the steps: checking whether a current time slot meets a layer cache updating condition or not in response to a deployment request for a target micro service received in the current time slot; if not, for any deployable node, scheduling the scheduling information of any required mirror image layer and the data access rate between the nodes from any other node according to the mirror image layer required by the target micro-service, the memory space of the corresponding mirror image layer and the micro-service deployment information under the current time slot; and constructing a total layer loading time function that the target micro-service is deployed on the node and changes along with the time slot, so as to construct a service utility function that corresponds to the node and changes along with the time slot in combination with profit information and starting cost of the node, thereby determining a deployment strategy of the target micro-service, and starting the target micro-service based on a mirror image layer required by the target micro-service. The method effectively balances the service performance and the resource utilization rate.

The invention provides a work plan and task management system, which belongs to the technical field of work plan tasks and comprises a portal layer, an application program interface gateway layer, a business service layer, a public service layer and a data layer. And the application program interface gateway layer realizes intelligent management of the interface through a self-adaptive division module, wherein the intelligent management comprises dependency analysis, granularity calculation, aggregation optimization and recombination execution. The business service layer focuses on work plans and task management, and realizes collaboration through flexible interface interaction. The public service layer provides basic support for user authentication, log recording, message queue, service registration, configuration management and the like. And the data layer adopts a diversified storage strategy, integrates a relational database, a cache database, a time sequence database and distributed file storage, and meets data processing requirements of different scenes. The problem that in the prior art, interface definition and calling relations between micro-services are difficult to manage in a self-adaptive mode is solved.

The invention provides a graphical construction and edge deployment method for configuration software development for a micro-service architecture, which comprises the following steps of: constructing an interface and logic of configuration software by adopting a graphical interface through operations such as drag-drop, connection and the like; establishing a graphical component library comprising a plurality of predefined micro-service components, wherein the predefined micro-service components comprise data acquisition, processing, storage, display and the like; the predefined micro-service component supports conversion of the graphical component into an executable micro-service code, and configuration software is constructed; deploying the micro-service of the configuration software at a position close to a data source in combination with an edge computing node deployment strategy; the edge deployment framework automatically completes deployment and starting of the micro-service according to the configuration file and supports automatic deployment, monitoring and upgrading of the micro-service; the states of the edge computing nodes are monitored through a graphical interface, and remote configuration and management are carried out; and performing real-time viewing, log analysis, troubleshooting and the like on the node state. The development efficiency is improved.

The invention provides a plug-in service processing method and a Spring Boot service dynamic aggregation integration system, and relates to the technical field of computer software, and the plug-in service processing method comprises the following steps: receiving a plug-in service directory, and executing initialization operation; traversing the plug-in service directory by using the plug-in management framework, and generating a plug-in service package and a plug-in resource list corresponding to the plug-in service; the plug-in service package is loaded through the plug-in management framework, so that a key component of the plug-in service package is registered to Servlet Context of a Servlet container of a main service; and instantiating the service starting class of the plug-in service through the plug-in management framework, and executing starting operation for the registered plug-in service by utilizing the instantiated service starting class, so that a key component of the plug-in service monitors and responds to an external HTTP request. According to the method, flexible scheduling and efficient utilization of resources are realized, the micro-service deployment and operation and maintenance cost can be reduced, and the resource use efficiency can be effectively improved.

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 provides a professional online collaborative editing method and system for hydropower project cost schemes. The professional online collaborative editing method comprises a standard management process, an intelligent collaborative editing process and a release state synchronous updating process. The standard management process comprises the steps of performing automatic analysis and data standardization conversion on an industrial standard file based on a deep learning model, generating a system rule set and storing the system rule set in a database; and based on the system rule set, automatically generating a standardized filling template of each specialty. According to the micro-service architecture-based intelligent online collaborative editing method and system for the hydropower engineering, a natural language processing NLP technology, a deep learning algorithm and a transaction management mechanism are combined, and the system has the functions of real-time grammar and spelling inspection of filled content, intelligent recommendation, semantic understanding, real-time difference detection, multi-user conflict processing and version control; the problems that communication cost is high, version control is difficult, data verification is not strict and multi-user cooperation is not smooth in water-power engineering cost scheme compiling are solved.

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.