685 results about "Micro services" patented technology

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 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 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 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 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.

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 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 provides a cloud native micro-service splitting and reconstructing method based on runtime behaviors. A three-layer architecture design is adopted, and the system comprises a dependency network graph construction module, a micro-service division module and a code reconstruction module. The dependency network diagram construction module models specific project source codes and user access operation into abstract representation capable of being processed by an algorithm, the micro-service division module divides a network diagram, obtains a splitting scheme for the dependency network diagram through visual page interaction with a user and manual modification, and inputs the splitting scheme into the code reconstruction module; and a code reconstruction module performs code reconstruction based on the splitting scheme and the dependency network diagram to obtain a complete and operable micro-service architecture code, so that the effect of automatically analyzing Spring Boot project source codes and monitoring the running project to generate the dependency network diagram is realized.

The invention provides an online interactive programming education system based on a micro-service architecture, and the system comprises an infrastructure layer which serves as a bottom layer computing power and provides an elastic computing power through a cross-cloud deployment technology and a dynamic resource scheduling technology; the platform core layer constructs a stateless micro-service cluster through a service registration discovery module, a load balancing module and a fusing mechanism module depending on elastic resources of the infrastructure layer, and the infrastructure layer provides physical resource guarantee for operation of the micro-service cluster of the platform core layer; the platform support layer is used for integrating an authority management component, a workflow engine component and a general service component based on a stateless micro-service cluster constructed on the platform core layer, and providing resource isolation and high-concurrency task scheduling support in a multi-tenant environment for the programming education system; the platform core layer is combined with distributed transaction management to provide stable service scheduling and communication capability for an authority management component, a workflow engine component and a general service component of the platform support layer; and the platform application layer combines service scheduling and communication capabilities, realizes teaching functions such as multi-language development, code debugging visualization, AI personalized suggestion and collaborative learning, and directly serves programming learning of students and teaching management requirements of teachers.

The invention discloses a method and a system for controlling a Docker container starting sequence based on a Shell script. The method comprises the steps that for a container deployed through a docker run command, a reset parameter of the container is modified from always to on-failure, so that the container is triggered to be restarted only when a non-zero exit code exits; constructing a ready detection mechanism based on TCP port detection; compiling a Shell script, starting containers in sequence according to a preset topology, and waiting for the dependency service to be ready; and packaging the script into a Systemd service to realize automatic triggering of the system. According to the method, on the premise that Docker Compose is not introduced, the problem of micro-service dependency failure caused by Docker default concurrent disordered starting is solved, and the method has the advantages of being light in deployment, flexible in configuration, high in automation degree, high in maintainability and the like and is suitable for containerized environments independently deployed based on docker run.

The micro-service processing system provided by the embodiment of the invention comprises an event identification module which is used for defining a business domain event type and registering an event attribute structure; the event bus module constructs a distributed communication network containing a dual-channel message queue, and the event bus module implements partition routing according to event types; the event processing module is composed of an event producer and a consumer which are executed asynchronously, the event producer packages service actions into a standardized event data packet, and the consumer processes subscription events through a thread pool; the event storage module adopts a hierarchical log database for persistence of an event sequence and supports reestablishment of a service state according to a timestamp; and the security control module is used for implementing service authentication and event content encryption on a transmission layer. Through event bus dual-channel design, sender box transaction binding and hierarchical storage optimization, millisecond-level event processing delay is realized while the reliability of distributed transactions is ensured, and second-level state recovery can be realized based on a complete event log when a service fault occurs.

The invention relates to a system calling ptrace-based operating system micro-service security protection system and method, which are characterized in that the running state of micro-service is monitored in real time through calling of a ptrace system in a user state, and dynamic integrity verification is realized in combination with an SM3 hash algorithm. The method comprises the following steps: calculating an SM3 hash value of a code segment of a micro-service when the micro-service is started, storing the SM3 hash value as a static reference, periodically reading memory data of a target process through ptrace in a running process, recalculating a dynamic hash value, and comparing the dynamic hash value with a reference value; if it is detected that the hash values are inconsistent, it is judged that code injection attacks exist, and a defense mechanism is triggered. According to the method, the system stability risk caused by kernel module injection is avoided, meanwhile, real-time interception of tampering attacks during operation is achieved through high-frequency dynamic comparison, the lightweight design enables the method to be suitable for high-reliability scenes such as spaceflight with limited resources, the integrity of a micro-service code segment can be guaranteed, low performance loss of the system can be maintained, and the system reliability is improved. The balance blank between dynamic defense and system stability in the prior art is effectively filled.

The invention discloses a geophysical network multi-algorithm fusion data processing method and system, and relates to the technical field of geophysical information processing, and the method comprises the following steps: S1, constructing a micro-service cluster based on a SpringCloud framework, respectively deploying data conversion, data preprocessing, conventional product calculation, data visualization and task scheduling functions as independent micro-services, and an elastic service architecture with a transverse expansion capability is established to support parallel cooperative operation of each function service. Through micro-service architecture, an intelligent recognition engine, dynamic visualization, security access control and operation monitoring closed-loop management, the automation, intelligence and security of geophysical data processing are comprehensively improved, and the system has the advantages of high concurrent processing, quick response, expandability, high reliability and the like, and effectively supports earthquake monitoring and geophysical service requirements.

The invention provides a micro-service scheduling method, system and device and a readable storage medium. The method specifically comprises the following steps: collecting communication flow among micro-services, and constructing a micro-service topological graph; generating a plurality of micro-service chains based on the micro-service topological graph, wherein each micro-service chain takes the entrance micro-service as a starting point and comprises a complete calling path in one micro-service topological graph; based on a preset reinforcement learning model, an optimal micro-service chain deployment strategy is generated, the micro-service chain deployment strategy comprises the optimal edge node for deploying each micro-service chain, and a reward value of the reinforcement learning model is configured to be a linear combination of cross-machine communication overhead when different deployment strategies are applied and resource utilization rate after the micro-service chains are deployed. Compared with the prior art, the method has the advantages that the application topology is decomposed into a plurality of complete calling paths by dividing the micro-service chains, so that the micro-service chains of the same request are distributed on the same node, the cross-machine flow is reduced fundamentally, and load balancing and improvement of the resource utilization rate are realized.

The invention discloses a hospital inquiry system based on the Internet. The hospital inquiry system comprises a business platform layer, a multi-terminal application layer, a composite interaction layer and a verification layer, the service platform layer is constructed by adopting a Java Springboot and SpringCloud micro service architecture, and a plurality of core functions can be packaged into an independent service process; the multi-end application layer can select an optimal technology stack to form a multi-end application based on operation habits and performance requirements of different terminal users, and each end performs front-end and back-end separated interaction with the service middle station through an API gateway; and the composite interaction layer is used for establishing a communication link between a doctor and a patient so as to carry out a full-service process of online inquiry, prescription making and medicine distribution. According to the system, a one-stop business process of online inquiry, prescription extraction, medicine purchasing and distribution is realized, meanwhile, doctors are supported to receive diagnosis through a mobile terminal or a computer anytime and anywhere, and the problem that the licensing behaviors of the doctors are bound to fixed places and time of physical hospitals is solved.

The invention provides an intelligent gridding comprehensive anti-misoperation locking system for a power distribution network, and the system comprises an optimization module which achieves the hierarchical decoupling design of the system through a distributed communication middle platform, an intelligent logic analysis engine and a programmable execution control unit. The deployment module is used for modularly deploying functional components based on the micro-service architecture, reducing the system coupling degree by using an asynchronous message queue as a cross-component communication bus in combination with a domain event-driven mechanism, and completing local logic judgment at an edge computing node through an FPGA (Field Programmable Gate Array); the network reinforcement module is used for constructing a heterogeneous communication network and guaranteeing the communication quality through a dual-channel redundancy transmission technology; the intelligent decision-making module is used for forming a self-adaptive decision-making model based on reinforcement learning and a knowledge graph technology; and the digital twinning verification module is used for establishing a power grid virtual mapping system through a digital twinning technology. Cooperation among modules of the system can be balanced, instruction processing time delay is shortened, communication reliability is improved, and efficient implementation of an operation scheme is guaranteed.

The invention relates to the technical field of containers and micro-service architecture, and discloses a K8s-based Juser Notebook container arrangement method, which comprises the following steps of: S1, packaging Notebook into a Docker mirror image file; s2, mounting a Notebook configuration file on the K8s cluster through a ConfigMap (Configuration Map); s3, deploying and defining the number of copies of the Notebook application through Deployment; S4, configuring a service network and an ingress network; S3, deploying and defining the number of copies of the Notebook application through Deployment; s5, a cloud storage mode is adopted, and calculation and storage are separated; and S6, K8s API automatic management is carried out. The deployment process is simplified, the maintenance and management difficulty is reduced, and the resource utilization rate is improved; a cloud storage mode is adopted, separation of calculation and storage is achieved, and environment configuration is simplified; the Ingress provides network functions such as flow management and reverse proxy, so that the Notebook service is easier to access and integrate; the K8s cluster can ensure the high availability of the Notebook service; and when a fault occurs, quick recovery is realized, service interruption time is reduced, monitoring and management are easy, and management and operation and maintenance efficiency is improved.

The invention provides a micro-service unified access control and governance system in a hybrid cloud environment, and the system comprises a unified entrance layer which takes an Istio gateway as a unique access point of external flow, and receives all business requests from the outside; according to the authentication and authorization intermediate layer, the Istio Gateway forwards the service request to the Oathkeeper, and Kratos authentication and SpiceDB authentication are carried out. The rule routing layer is used for performing routing decision on the service request passing the Kratos authentication and the SpiceDB authentication based on a predefined dynamic rule set, and generating a routing instruction; and the heterogeneous service layer comprises a VM environment and a K8S environment, and forwards the request to a corresponding target service instance according to the routing instruction. According to the method, the problems of flow entrance dispersion, authentication splitting and non-uniform routing rules in a mixed cloud environment are solved, and uniform, safe and efficient access control and management of heterogeneous micro-services are realized.

The invention relates to the technical field of micro-service gateways, and discloses a multi-source heterogeneous service system integration method and platform based on a micro-service gateway, and the method comprises the steps: receiving a source request message, executing protocol conversion, and generating a standardized request message and a protocol conversion record; constructing a distributed routing rule table, extracting multi-dimensional matching features, screening an optimal routing rule and a specific service instance, and generating a forwarding request message carrying authentication information; performing intelligent flow control on the message, receiving to-do items of a service system, performing cross-system aggregation, and generating a to-do processing track; constructing a cross-system service behavior graph based on a routing log, a protocol conversion record and a to-do processing track, predicting a service operation path, and executing cross-system resource intelligent prefetching and collaborative optimization; according to the invention, the bottleneck of multi-source heterogeneous system integration is effectively broken through, efficient collaboration and optimal resource configuration of the system are realized, and the integrated management requirements of enterprises are met.

The invention discloses a micro-service fault diagnosis method and system based on a large language model and a knowledge graph. The method comprises the steps that a service dependency graph is automatically generated through static topology construction; converting the heterogeneous log into a standardized entity relationship triple by utilizing a semantic fusion engine; a diagnosis sub-graph associated with attributes is created through a dynamic sub-graph generation mechanism; and a workflow inference engine is adopted to realize multi-attribute collaborative root cause positioning, and a diagnosis report containing fault root causes and diagnosis results is generated. According to the method, operation and maintenance personnel can realize cross-service, high-fidelity and self-adaptive fault diagnosis in a fragmented observation data environment, and the diagnosis efficiency and accuracy are remarkably improved.

The invention discloses a business processing method and device based on micro-service, electronic equipment and a storage medium, relates to the technical field of data processing, and compared with the related technology, according to the application, insurance business requests are accurately distributed to corresponding micro-service modules according to the types of the insurance business requests, so that different types of businesses are independently processed by special modules; decoupling and specialized division of system functions are realized, and mutual dependence among modules is reduced, so that the technical problems of high system coupling degree, high maintenance cost, low task processing efficiency and performance bottleneck during high concurrency in the existing insurance service system can be solved; the technical effects of improving the insurance service processing efficiency, reducing the system maintenance cost, enhancing the ability of the system to deal with high-concurrency services, and further improving the insurance service processing quality and the customer experience are achieved.

The invention provides a KPIs anomaly detection method based on MVMD decomposition. The KPIs anomaly detection method comprises the following steps: S1, obtaining multi-dimensional key performance index KPIs time sequence data in a micro-service system; s2, decomposing the normalized multivariable KPIs time sequence data into K intrinsic mode function (IMF) components; s3, dividing the time sequence of each IMF component into a plurality of fixed-length subsequences through a sliding window; s4, respectively constructing and training a variational auto-encoder VAE model, and calculating a reconstruction error of each IMF component at each time point; s5, obtaining a comprehensive abnormal score of each time point; s6, based on a grid search method, determining an optimal anomaly score threshold value for anomaly judgment; and S7, comparing the comprehensive abnormal score with an optimal abnormal score threshold value, and if the comprehensive abnormal score at a certain time point exceeds the threshold value, judging that the KPIs at the time point is abnormal. According to the invention, the modeling capability of abnormal modes of different frequency components can be enhanced, so that the accuracy of overall anomaly detection is improved.

The invention discloses an intelligent sand table identification system based on an artificial intelligence multi-modal technology, which belongs to the field of sand table identification and comprises a multi-modal data acquisition module, a multi-modal feature fusion module, an intelligent analysis and interaction module and a system integration optimization module. According to the method, the problems of insufficient recognition robustness, poor dynamic analysis real-time performance, weak cross-scene generalization ability, high data labeling cost and single interaction mode caused by dependence on a single sensor and lack of multi-dimensional perception ability in the prior art are solved, the limitation of traditional single-mode recognition is broken through by collecting vision, force sense, voice and environment data, and the recognition efficiency is improved. The real-time intelligent analysis and dynamic interaction technology is adopted, real-time intelligent analysis and dynamic interaction of sand table operation are achieved, the system further has strong generalization ability, a meta-learning framework and an active learning strategy are adopted, dependence on annotation data is reduced, the system is reasonable in architecture, distributed collection, heterogeneous calculation and micro-service deployment are adopted, and the system has good application prospects. And high-efficiency operation and expandability are ensured.

A system (100) for the secure MCP-mediated use of tools by AI agents and generative AI / LLM services in cloud-native distributed applications, wherein the system (100) comprises: a KL agent interface (1) configured to receive natural language commands and application events from a variety of client applications and to generate appropriate tool call commands for one or more generative KL or Large Language Model (LLM) services; an MCP mediator service (2) that is configured to: (a) to convert the tool request requests into messages compatible with a model context protocol (MCP); and (b) to maintain the conversation context, including at least one of the following: user identity, tenant identity and application identity; a tool register (3) that stores a plurality of tool descriptions, each tool description defining at least a tool identifier, an input and output scheme, an endpoint location and allowed functions, wherein the tool register (3) is accessible to the MCP mediator service (2); a policy and security manager (4) configured to evaluate each MCP tool call against one or more security and access policies based on the conversation context and the corresponding tool description, and to issue a decision to allow, modify or block the tool call; a tool connector layer (5) comprising a plurality of tool adapters, each tool adapter being configured to communicate securely with a corresponding external tool, service or data source using credentials and permissions restricted according to the decision of the policy and security manager (4); an observation and audit manager (6) configured to record, for each tool call, at least a timestamp, the calling KL agent, the tool identifier, the policy decision, and a summary of the tool response, and to provide audit logs and metrics for monitoring and compliance purposes; and a cloud-native deployment controller (7) configured to provide the MCP mediator service (2), policy and security manager (4), tool connector layer (5) and observation and audit manager (6) as distributed microservices with network isolation between tenants in a cloud-native environment.

The embodiment of the invention provides an intelligent operation and maintenance method and system based on a large language model. According to the method, firstly, at least M multi-modal original logs generated by a micro-service cluster are collected, compression processing is conducted on the multi-modal original logs by means of a large language model, at least N multi-modal target logs are obtained, the log data volume can be greatly reduced, and the effective information density is improved; synchronously collecting system operation indexes of timestamps corresponding to the multi-modal target logs and determining an association relationship between the system operation indexes to reduce invalid diagnosis workload; a multi-type heterogeneous operation and maintenance topological graph is constructed based on the incidence relation, a current fault is accurately positioned, the problem that root causes are difficult to quickly position through traditional manual analysis of mesh topology is solved, and the root cause recognition time is shortened; and finally, a preset historical fault knowledge base is called, a target repairing script matched with the current fault is generated in combination with the large language model, repairing of the current fault is achieved, time consumed by manual intervention is shortened, and therefore the fault diagnosis efficiency is remarkably improved.

The invention discloses an unstructured document multi-modal analysis and semantic metadata deep extraction method and system based on multi-agent collaboration. The method comprises the steps that multi-modal elements such as texts, images and tables are extracted in parallel through a multi-format unified analysis engine; a four-agent (table structure analysis, multi-modal image analysis, semantic text understanding and metadata extraction) collaborative architecture is adopted, and task decomposition, asynchronous scheduling and result fusion are realized based on a multi-agent collaborative scheduling algorithm; the performance is improved by combining dynamic concurrency control, memory optimization and an intelligent cache mechanism; and finally generating structured output. The system integrates the modules and supports micro-service architecture and stability guarantee. According to the method, multi-modal deep semantic understanding is achieved, the processing efficiency is improved by 3-5 times compared with that of a traditional method, the text / table analysis precision reaches 98% and 95% respectively, localized efficient processing and incremental updating are supported, and a high-quality data source is provided for enterprise knowledge management and content retrieval.