A cloud-based enterprise service management system

Abstract

This invention discloses an enterprise service management system based on a cloud platform. Its key features include a cloud-native architecture, a microservice deployment model, and construction based on distributed storage, AI algorithms, and event-driven architecture (EDA). The core system comprises a cloud platform foundation, core functional modules, auxiliary functional modules, an application interface layer, and a terminal access layer. Each layer and module works collaboratively to achieve bidirectional linkage governance of configuration and events. The core functional modules include an intelligent configuration governance module. This invention achieves a shift from "passive repair" to "proactive prevention": through dynamic verification of configuration fingerprints, it detects hidden drift in real time with a drift detection accuracy of ≥98%; through AI drift prediction, it avoids drift risks in advance, reducing the drift occurrence rate by ≥80%; through intelligent automatic repair, it reduces manual intervention, improving repair efficiency by ≥90%; and through integrated IAC management, it eliminates configuration restoration risks caused by ClickOps, significantly reducing maintenance workload.

Description

Technical Field

[0001] This invention relates to the field of cloud platform enterprise service management technology, specifically to an enterprise service management system based on a cloud platform. Background Technology

[0002] As enterprises continue to deepen their digital transformation, cloud-based Enterprise Service Management (ESM) systems have become the core support for enterprise operation and maintenance management and efficient business operations. Leveraging distributed architecture, microservice deployment models, and multi-environment collaboration (development, testing, and production) capabilities, they enable large-scale and flexible management of enterprise services. However, existing cloud-based ESM systems generally suffer from two major "hidden pain points" during long-term operation. Although the initial impact may not be significant, these issues continuously erode system consistency and traceability, ultimately leading to soaring operation and maintenance costs, exposure of compliance risks, and potential business interruptions. These issues have become key bottlenecks restricting the improvement of enterprise service management efficiency, as detailed below: I. Existing technical deficiencies in configuring drifting latent corrosion The core flaws of existing cloud platform enterprise service management system configuration management solutions lie in their "decentralization, passivity, and lack of linkage," specifically manifested as follows: 1. Fragmented configuration management: Configurations are scattered across code, databases, environment variables, and configuration files, lacking a unified control entry point. Synchronization of configurations across multiple environments (development, testing, and production) relies on manual triggering, which can easily lead to the problem of "tests pass, production fails." In particular, if production configurations are manually modified (i.e., ClickOps) but not synchronized to the IAC (Infrastructure as Code) template, subsequent automated deployments will revert the configurations, causing business interruptions without warning. This is a common vulnerability in existing IAC workflows.

[0003] 2. Passive and delayed drift detection: Existing technologies mostly adopt the "periodic verification" or "manual reconciliation" mode, which can only detect drifts that have already occurred and cannot predict potential drift risks; moreover, the detection granularity is coarse, making it difficult to detect subtle configuration deviations (such as parameter fine-tuning and hidden permission changes). Drifts continue to exist until they cause business anomalies before they are noticed. Even if some solutions (such as AWS CloudFormation) support drift awareness, they can only preview changes and cannot achieve automatic repair and risk prediction.

[0004] 3. Inadequate repair mechanism: Existing drift repairs mostly rely on manual operation and lack automated repair capabilities. Furthermore, no event records are associated with the repair, making it impossible to trace the cause of the drift and the repair process, resulting in repeated occurrences of similar drifts. At the same time, sensitive configurations mostly use fixed encryption methods, which cannot dynamically adjust the desensitization strategy according to the configuration scenario, posing a risk of leakage.

[0005] 4. Lack of linkage mechanism: Configuration changes and event tracing are completely independent. Business anomalies caused by configuration drift cannot be quickly linked to the corresponding configuration change records, resulting in low troubleshooting efficiency. Furthermore, configuration changes do not automatically trigger event records, making it difficult to achieve full-process traceability.

[0006] II. Existing Technological Deficiencies Regarding the Hidden Lack of Event Origin Tracing The core flaws of existing event management solutions lie in their "incompleteness, lack of correlation, and difficulty in replay," specifically manifested as follows: 1. Incomplete event logging: Only successful business operation events are recorded, while critical events such as failures, retries, and rollbacks are not recorded. Furthermore, configuration change events and system operation events are not included, making it impossible to form a complete event chain. At the same time, the event format is inconsistent, with significant differences in log formats between different modules, making cross-module event correlation analysis difficult.

[0007] 2. Disconnect between events and configurations: Event logs only include the operator, time, and operation content, without linking to the corresponding configuration information. When business operations are abnormal, it is impossible to determine whether it is caused by configuration drift. Troubleshooting requires cross-module comparison, which is time-consuming and labor-intensive. Existing security tracing solutions (such as Ruijie firewall tracing) also only focus on identity and network terminal, without linking to configuration information, and have the same limitations.

[0008] 3. Weak traceability: The "state overwrite" mode is adopted, where the new state directly overwrites the old state, without retaining all change trajectories, making it impossible to restore the system state at any point in time; event retrieval only supports simple keyword queries, lacks multi-dimensional and full-link retrieval capabilities, and cannot realize event time sequence playback, making it difficult to locate the root cause.

[0009] 4. Inadequate event storage: Logs are scattered across various microservice nodes without a centralized event repository, resulting in low query efficiency. Furthermore, the lack of time-series storage characteristics makes it difficult to meet the tracing needs of long-term, large-scale events.

[0010] In summary, existing technologies often treat configuration management and event tracing as independent modules, lacking collaborative linkage. Furthermore, configuration drift detection is mostly passively triggered and event recording is incomplete, failing to address the two major hidden pain points at their root. Therefore, there is an urgent need to design a novel and innovative system architecture to achieve deep integration of configuration governance and event tracing, thoroughly resolving the impact of these hidden pain points on enterprises and improving the intelligence, stability, and compliance of cloud platform enterprise service management. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a cloud-based intelligent configuration-event linkage enterprise service management system, which completely solves the problems of configuration drift and hidden corruption and the lack of hidden event tracing in existing systems, and achieves the following core objectives: 1. Achieve unified management, intelligent prediction, and automatic repair of configurations, proactively avoid hidden drift risks, ensure configuration consistency across multiple environments, and eliminate configuration restoration risks caused by ClickOps; 2. Enable full-link event tracing, covering all events such as configuration changes, business operations, and system operation, forming a complete event trajectory, and supporting system state restoration and event time sequence playback at any point in time; 3. Build a two-way linkage mechanism between configuration and events to achieve rapid fault location (event anomaly → configuration verification → root cause location), reducing fault troubleshooting time from hours to seconds; 4. Meet enterprise compliance requirements, achieve full traceability and auditability of configuration changes and event operations, and reduce compliance risks; 5. Based on cloud-native architecture, ensure system scalability and high availability, adapt to the service management needs of enterprises of different sizes, and reduce the workload of operation and maintenance personnel.

[0012] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: a cloud-based intelligent configuration-event linkage enterprise service management system. Based on a cloud-native architecture and employing a microservice deployment model, it is built upon distributed storage, AI algorithms, and event-driven architecture (EDA). Its core includes a cloud platform foundation, core functional modules, auxiliary functional modules, an application interface layer, and a terminal access layer. Each layer and module works collaboratively to achieve bidirectional linkage governance of configuration and events. The specific structure is as follows: (I) Cloud Platform Base It can be deployed in public cloud, private cloud or hybrid cloud, supports Kubernetes containerized scheduling to ensure elastic system expansion; it provides basic support such as distributed storage, network communication, and resource scheduling to provide a stable and efficient operating environment for upper-layer modules, and adapts to the deployment needs of enterprises of different sizes.

[0013] (II) Core Functional Modules The core functional modules are the creative core of this invention, used to achieve deep integration of configuration governance and event tracing, including an intelligent configuration governance module, a full-link event tracing module, and a linkage scheduling module, as detailed below: 1. Intelligent Configuration Governance Module To address the hidden corrosion problem caused by configuration drift, this approach overcomes the limitations of existing configuration management methods that rely on "passive detection and decentralized control." It introduces technologies such as dynamic configuration fingerprint verification, AI drift prediction, and automatic repair to achieve intelligent management and control throughout the entire configuration lifecycle. The core features include: Unified Configuration Center: Builds a centralized configuration repository, integrating configuration information from all microservices and multiple environments (development, testing, and production). It supports unified entry, modification, deletion, and synchronization of configurations, achieving "one-stop configuration, multi-device synchronization." Configurations are categorized and managed (business configuration, system configuration, and sensitive configurations). Sensitive configurations employ a dynamic encryption and desensitization mechanism, automatically adjusting the desensitization level based on configuration scenarios and access permissions, and supporting dynamic switching of encryption algorithms (such as AES and RSA). It introduces integrated IAC management, synchronizing all configuration changes to IAC templates such as Terraform and CloudFormation, eliminating configuration restoration issues caused by ClickOps.

[0014] Configuration fingerprint dynamic verification unit: Generates a unique dynamic configuration fingerprint for each configuration instance (a configuration set of a single environment and a single microservice). The fingerprint generation rule is "MD5 hash value + configuration context information (instance ID, environment variables, configuration version, modification time)", ensuring the uniqueness and high entropy value (entropy value ≥ 128 bits) of the fingerprint and the duplication rate ≤ 0.01%. The system verifies the fingerprints of all configuration instances in the cluster in real time (every 30 seconds) and compares them with the baseline fingerprint in the configuration center. When a mismatch is found (configuration drift), an alarm is triggered immediately and the drift details are recorded. Historical traceability of configuration fingerprints is supported.

[0015] AI Drift Prediction Unit: Introduces machine learning algorithms (random forest, LSTM) to build a configuration drift prediction model. Based on historical configuration data (configuration change records, drift records, business load, system operating status), it mines drift-related factors to achieve early prediction of drift risks. The model supports self-learning and optimizes parameters regularly (weekly) to improve prediction accuracy. Core functions include drift probability prediction, high-risk configuration change alerts, and drift spread range prediction.

[0016] Intelligent automatic repair unit: For detected configuration drift, it provides a hierarchical repair mechanism (automatic repair, semi-automatic repair, manual repair), builds a drift repair knowledge base, and realizes rapid matching and repair of similar drifts; after automatic repair is completed, it automatically verifies the fingerprint to ensure successful repair, and generates a repair event to be synchronized to the event tracing module; it supports repair rollback function, saves configuration snapshots, and supports one-click rollback and verification.

[0017] Multi-environment configuration consistency verification unit: Automatically compares configuration information across multiple environments, supports custom comparison rules, triggers alarms when discrepancies occur, provides one-click synchronization, and supports configuration synchronization permission control and process recording.

[0018] 2. End-to-end event tracing module To address the issue of hidden gaps in event tracing, and overcome the limitations of existing event management systems that are "incomplete, unrelated, and difficult to replay," this approach introduces technologies such as panoramic event modeling, configuration-event linkage, and time-series replay to achieve complete recording and accurate tracing of events across the entire chain. Its core features include: Event Panorama Modeling Unit: Constructs a unified event model covering three major categories: business operation events, configuration change events, and system operation events, achieving comprehensive event coverage; all events adopt a unified format specification, including core fields such as event ID, event type, event level, trigger time, associated objects, event details, and processing status, ensuring cross-module event correlation analysis capabilities.

[0019] Configuration-Event Two-Way Linkage Unit: Enables deep linkage between configuration changes and event tracing, forming a closed loop of "configuration → event → configuration"; when a configuration change occurs, a "configuration change event" is automatically generated, synchronized to the event tracing module, and associated with configuration fingerprints and other information; when an event is abnormal, the corresponding configuration information is automatically associated, triggering configuration fingerprint verification to investigate whether it is caused by configuration drift, and pushing drift details and repair suggestions; a complete event chain is built based on associated fields, clearly presenting the impact of configuration changes on business and the system.

[0020] Event panoramic tracing and time-series replay unit: Adopting the event tracing mode, it does not cover any event state, and stores all events in time-series order to a centralized event warehouse (time-series database InfluxDB + distributed log system ELKStack), retaining the original data and supporting system state restoration at any point in time; it supports multi-dimensional event retrieval (fuzzy query, combined query), event time-series replay, and event statistical analysis, with a query response time ≤ 1 second.

[0021] Event Audit and Compliance Adaptation Unit: Records audit logs for all events, supports full-chain traceability from "event-operator-operation time", meets compliance standards such as ISO20000, ITIL, GDPR, and SOX; supports custom audit rules, automatically identifies non-compliant events and triggers alarms, and generates compliance audit reports.

[0022] 3. Linkage Scheduling Module As the core hub for the two-way linkage between configuration governance and event tracing, it is responsible for coordinating the collaborative work of the intelligent configuration governance module and the full-link event tracing module, breaking through the limitation of the two modules operating independently in existing technologies. Its core components include: Linkage Engine: Builds a configuration-event linkage rule engine, with preset linkage scenarios (configuration change → event generation, event exception → configuration verification, drift repair → event update), supports custom linkage rules, and ensures real-time collaboration between the two modules.

[0023] Unified data bus: Employs Kafka message queues to enable real-time interaction of configuration data and event data, ensuring reliable and real-time data transmission, avoiding data loss or delay, and supporting the efficient operation of the linkage engine.

[0024] Operation and maintenance visualization dashboard: Integrates configuration status, event trajectory, drift warning, fault alarm and other information, and presents it in a visual chart (line chart, bar chart, topology diagram), supports large screen interaction, and improves operation and maintenance efficiency.

[0025] Anomaly Closed-Loop Management Unit: For issues such as configuration drift and event anomalies, it implements closed-loop management of "early warning → handling → verification → archiving", records the processing results of each step, and avoids the recurrence of similar problems.

[0026] (III) Auxiliary Function Modules This is used to ensure the safe and stable operation of the system, including a permission management module and a monitoring and alarm module: Access Control Module: Adopts a RBAC+ABAC+CBAC integrated access control model to achieve fine-grained access control; access allocation is based on "subject (user / role) + resource (configuration / event) + operation (view / modify / delete) + context (time, device, IP, risk level)", supports automatic expiration of temporary permissions and automatic adjustment of permissions when job positions change; all access operations are recorded as events and included in the event tracing module to support full traceability.

[0027] Monitoring and Alarm Module: Real-time monitoring of system operation status, configuration status, and event status; preset alarm thresholds for various types of alarms; when an alarm is triggered, alarm information is pushed through multiple channels (SMS, email, WeChat for Enterprise, system pop-ups), indicating the alarm level, cause, and handling suggestions; supports alarm hierarchical handling and escalation mechanisms; alarm events are synchronized to the event tracing module for easy fault investigation.

[0028] (iv) Application Interface Layer It provides standardized API interfaces (RESTful API, RPC interface) to support integration with existing enterprise business systems (ERP, CRM, work order system), cloud platforms (Alibaba Cloud, Huawei Cloud, AWS), and operation and maintenance tools (Jenkins, Prometheus) to achieve data interoperability and functional linkage; the interface supports HTTPS encrypted transmission and has security mechanisms such as identity authentication, permission verification, and interface rate limiting, and the interface operation log is included in the event tracing module.

[0029] (v) Terminal Access Layer It supports multi-terminal access, including PC (web browser) and mobile (mobile APP, WeChat mini program), achieving full terminal coverage of core functions; the mobile terminal supports alarm push and simple operation, while the PC terminal supports complex operation, adapting to different operation and maintenance and business scenario needs.

[0030] Compared with the prior art, the beneficial effects of the present invention are: 1. Completely resolve the hidden corrosion problem caused by configuration drift, realizing the transformation from "passive repair" to "proactive prevention": Through dynamic verification of configuration fingerprints, hidden drift is detected in real time, with a drift detection accuracy of ≥98%; through AI drift prediction, drift risks are avoided in advance, reducing the drift occurrence rate by ≥80%; through intelligent automatic repair, manual intervention is reduced, and repair efficiency is improved by ≥90%; through IAC integrated management, the configuration restoration risks caused by ClickOps are eliminated, significantly reducing the workload of operation and maintenance.

[0031] 2. Thoroughly resolve the issue of hidden gaps in event tracing, achieving full-chain, panoramic tracing: Through panoramic event modeling, all types of events are covered, avoiding event omissions; through configuration-event bidirectional linkage, rapid fault location is achieved, reducing fault investigation time from hours to seconds, and improving fault handling efficiency by ≥85%; through event time-series playback and multi-dimensional retrieval, system state restoration at any point in time is achieved, with root cause location accuracy ≥95%, meeting enterprise compliance requirements.

[0032] 3. Break through existing technological limitations to achieve deep linkage between configuration and events, forming a closed-loop governance: Unlike the existing model of independent management of configuration and events, a closed loop of "configuration change → event recording → event anomaly → configuration verification → drift repair → event update" is constructed to ensure configuration consistency and event traceability, and improve system availability to over 99.9%.

[0033] 4. Strong cloud-native architecture with high adaptability and scalability: It adopts microservices and containerized deployment, supports elastic scaling, and adapts to the needs of enterprises of different sizes; it supports multi-cloud deployment and integration with existing enterprise systems and tools, reducing the cost of enterprise digital transformation.

[0034] 5. Enhance compliance capabilities and reduce compliance risks: Achieve full traceability and auditability for configuration changes, event operations, and permission operations to meet various compliance standards and avoid compliance penalties; dynamically encrypt and de-identify sensitive configurations to improve data security. Attached Figure Description

[0035] Figure 1 This is a block diagram of an enterprise service management system based on a cloud platform according to the present invention; Figure 2 This is a flowchart illustrating the operation of a cloud-based enterprise service management system according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please see Figures 1 to 2 The present invention provides a technical solution: I. System Deployment Environment (a) Hardware environment Cloud server: Deployed using Kubernetes cluster. Recommended node configuration: CPU ≥ 8 cores, memory ≥ 16GB, hard disk ≥ 100GB (SSD). The number of nodes can be expanded according to the size of the enterprise (SMEs ≥ 3 nodes, large enterprises ≥ 6 nodes). Distributed storage: Distributed key-value storage (Redis) is used to store configuration fingerprints and hotspot configurations, time-series database (InfluxDB) is used to store event data, and distributed file storage (MinIO) is used to store configuration files and log files; Network environment: Supports Gigabit Ethernet to ensure smooth network connectivity between nodes and between terminals and servers; supports VPN access to ensure secure remote operation and maintenance.

[0038] (ii) Software Environment Operating System: Server nodes use Linux systems (CentOS 8.0+, Ubuntu 20.04+), and terminals support Windows 10+, macOS 12+, Android 11+, and iOS 15+; Containerization platform: Kubernetes 1.24+, container runtime: Docker 20.10+; Development languages ​​and frameworks: The backend uses Java (Spring Cloud Alibaba 2021.0.1.0) and Go (1.19+), while the frontend uses Vue 3.0+ and Element Plus; Databases and middleware: Redis 6.2+, InfluxDB 2.7+, MySQL 8.0+, Kafka 3.0+, ELKStack; AI algorithm framework: TensorFlow 2.10+ (drift prediction model training and inference); Security software: firewalls, intrusion detection systems (IDS), and data encryption tools ensure system security.

[0039] II. System Implementation Steps This system will be implemented according to the following steps to ensure stable deployment and functional compliance, as follows: 1. Step 1: Deployment of the cloud platform infrastructure (1-2 weeks): Build a Kubernetes cluster, configure basic environments such as distributed storage and network communication, complete environment testing, and ensure stable operation of the infrastructure; 2. Second step: Core module development and deployment (4-6 weeks): Develop the intelligent configuration governance module, the full-link event tracing module, and the linkage scheduling module in sequence. After completing the internal testing of the modules, deploy them to the cloud platform base and configure the linkage parameters between modules. 3. Third step: Auxiliary module and interface development (2-3 weeks): Develop the permission management module and monitoring and alarm module, develop standardized API interfaces, complete interface debugging and module integration testing, and ensure that the auxiliary modules work together with the core modules; 4. Fourth step: System integration and debugging (2-3 weeks): Integrate the system with the enterprise's existing business systems, cloud platform, and operation and maintenance tools, conduct full-process debugging, identify integration vulnerabilities, and optimize system performance; 5. Fifth step: Trial operation and optimization (3-4 weeks): Deploy the system to the trial operation environment, simulate real business scenarios, collect operation data, optimize AI drift prediction model parameters, repair strategies and system performance, and solve problems that occur during the trial operation; 6. Step Six: Formal Deployment and Delivery (1 week): Formally deploy the optimized system to the production environment, complete data migration, user training, deliver relevant documentation, and ensure the system is put into normal use.

[0040] III. Key Technical Challenges and Solutions (I) Key technical challenge 1: Ensuring the uniqueness of fingerprints and real-time verification efficiency Challenges: A large number of configuration instances (multiple microservices, multiple environments), frequent configuration changes, difficulty in ensuring the uniqueness of configuration fingerprints, and the potential for real-time verification to consume excessive system resources, affecting verification efficiency.

[0041] Solution: 1. Optimize fingerprint generation rules by introducing configuration context information (instance ID, environment variables) and combining it with the MD5 hash algorithm to ensure fingerprint uniqueness. The fingerprint length is controlled to 32 bits to reduce storage and comparison costs. 2. Adopt a distributed verification architecture, sharding configuration instances by microservice and environment. Each shard is assigned an independent verification node, and fingerprint verification is performed in parallel to improve verification efficiency. 3. Introduce a Redis caching mechanism to cache the baseline fingerprint in a Redis cluster, reducing the number of database queries and controlling the verification response time within 30 seconds, without affecting normal system operation.

[0042] (II) Key Technical Challenge 2: Accuracy of AI Drift Prediction Model Challenges: The factors associated with configuration drift are complex (configuration changes, business load, system operating status, etc.). When historical data is insufficient, the model prediction accuracy is low, and false alarms and false negatives are likely to occur.

[0043] Solution: 1. Initially, transfer learning is adopted, and the model is trained based on industry-standard configuration drift datasets to quickly improve the basic accuracy of the model; 2. A model self-learning mechanism is built to optimize model parameters regularly (weekly) based on newly added data in the system, improving the model's adaptability to the enterprise's own scenarios; 3. A multi-model fusion strategy is introduced, combining the advantages of random forest and LSTM algorithms to reduce false positive and false negative rates, with a target prediction accuracy of ≥90%; 4. Manual intervention is supported to adjust model parameters, manually mark false positive and false negative cases, and optimize the model's judgment logic.

[0044] (III) Key Technical Challenge 3: Real-time Performance and Reliability of Configuration-Event Linkage Challenges: High concurrency of configuration changes and event anomalies makes it difficult to ensure that the linkage logic is triggered in real time, which can easily lead to data loss, linkage delays, and affect the reliability of linkage.

[0045] Solution: 1. Use Kafka message queue as a unified data bus to support high-concurrency message transmission. Message delivery adopts a "at least once" mechanism to avoid data loss. 2. Build an asynchronous processing mechanism for the linkage rule engine. Non-urgent linkage scenarios (such as configuration change event generation) are processed asynchronously to avoid consuming core resources. Urgent linkage scenarios (such as event exception → configuration verification) are processed synchronously to ensure linkage latency ≤ 1 second. 3. Introduce a message retry mechanism. When linkage triggering fails, it will automatically retry (up to 3 times). If the retry fails, an alarm will be triggered to prompt the operation and maintenance personnel to intervene manually. 4. Regularly verify the consistency of linkage data to ensure that the correspondence between configuration changes and event records, and between event exceptions and configuration verifications is correct.

[0046] (iv) Key technical challenge 4: Efficiency of large-scale event storage and retrieval Challenges: After long-term operation, the amount of event data is enormous, making efficient storage difficult, and multi-dimensional retrieval and time-series playback inefficient.

[0047] Solution: 1. Use InfluxDB, a time-series database, to store event data, and combine it with the ELK Stack to achieve distributed collection and indexing of event logs, improving retrieval efficiency; 2. Introduce a tiered storage strategy for event data, storing recent events (within 3 months) on SSDs and older events (over 3 months) on low-cost distributed storage, reducing storage costs; 3. Build an event index optimization mechanism, creating composite indexes based on fields such as event type and related objects to improve query efficiency, with query response time ≤ 1 second; 4. Optimize the event time-series replay algorithm, adopting a segmented replay and cache reuse strategy to avoid replay stuttering.

[0048] IV. System Operation Flow Example Taking the configuration of drift detection and repair, and event tracing linkage as an example, the operation process of this system is explained as follows: 1. Configuration fingerprint verification: The configuration fingerprint dynamic verification unit of the intelligent configuration governance module performs fingerprint verification on all configuration instances in the cluster every 30 seconds and compares it with the baseline fingerprint in the configuration center; 2. Drift Detection and Early Warning: When a fingerprint mismatch (configuration drift) is detected, an alarm is immediately triggered. At the same time, the AI ​​drift prediction unit's intelligent analysis function for drift causes, combined with relevant events from the event tracing module and system monitoring data, identifies the cause of drift and pushes handling suggestions. 3. Linkage Event Generation: The linkage scheduling module triggers the linkage logic, the intelligent configuration governance module generates a "configuration drift event", which is synchronized to the full-link event tracing module and associated with configuration fingerprint, drift details and other information; 4. Drift Repair: The intelligent automatic repair unit executes the corresponding repair strategy (automatic repair, semi-automatic repair, manual repair) according to the drift level. After the repair is completed, the fingerprint is automatically verified to ensure the repair is successful. The repair operation is synchronously updated to the IAC template and a "repair event" is generated and associated with the event tracing module. 5. Event tracing: Operations personnel can use the full-link event tracing module to search for "configuration drift events" and "repair events," view the event chain, and use the time-series replay function to reconstruct the complete process of drift occurrence and repair, facilitating review and tracing. 6. Closed-loop archiving: After the repair is completed and the verification is passed, the anomaly closed-loop management unit archives the drift problem, records the handling results, and avoids the recurrence of similar problems.

[0049] This embodiment is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cloud-based enterprise service management system, characterized in that, Based on a cloud-native architecture and a microservice deployment model, it is built upon distributed storage, AI algorithms, and event-driven architecture (EDA). The core includes a cloud platform foundation, core functional modules, auxiliary functional modules, application interface layer, and terminal access layer. Each layer and module works together to achieve bidirectional linkage governance of configuration and events. The core functional modules include an intelligent configuration governance module, a full-link event tracing module, and a coordinated scheduling module, wherein: The intelligent configuration governance module is used to realize intelligent management and control of the entire configuration lifecycle and solve the problem of configuration drift and hidden corrosion. It includes a unified configuration center, a configuration fingerprint dynamic verification unit, an AI drift prediction unit, an intelligent automatic repair unit, and a multi-environment configuration consistency verification unit. The full-link event tracing module is used to realize full-link event panoramic tracing and solve the problem of hidden missing event tracing. It includes an event panoramic modeling unit, a configuration-event bidirectional linkage unit, an event panoramic tracing and time-series playback unit, and an event audit and compliance adaptation unit. The linkage scheduling module, as the core hub for the two-way linkage between configuration governance and event tracing, is used to coordinate the collaborative work of the intelligent configuration governance module and the full-link event tracing module. It includes a linkage engine, a unified data bus, an operation and maintenance visualization dashboard, and an anomaly closed-loop management unit.

2. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The unified configuration center is used to build a centralized configuration repository, integrate the configuration information of all microservices and multiple environments (development, testing, and production), and support unified input, modification, deletion, and synchronization of configurations to achieve "configuration in one place, synchronization across multiple devices". The configuration is classified into business configuration, system configuration, and sensitive configuration. Sensitive configuration adopts a dynamic encryption and desensitization mechanism, which automatically adjusts the desensitization level according to the configuration scenario and access permissions, and supports dynamic switching of encryption algorithms. At the same time, IAC integrated management is introduced to synchronize all configuration changes to the IAC template, eliminating configuration restoration issues caused by ClickOps.

3. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The configuration fingerprint dynamic verification unit is used to generate a unique dynamic configuration fingerprint for each configuration instance. The fingerprint generation rule is "MD5 hash value + configuration context information (instance ID, environment variables, configuration version, modification time)" to ensure the uniqueness and high entropy value (entropy value ≥ 128 bits) of the fingerprint and the repetition rate ≤ 0.01%. The system verifies the fingerprints of all configuration instances in the cluster in real time (every 30 seconds) and compares them with the benchmark fingerprint in the configuration center. When a mismatch is found (configuration drift), an alarm is immediately triggered and the drift details are recorded. It supports historical traceability of configuration fingerprints.

4. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The AI ​​drift prediction unit is used to build a configuration drift prediction model, introduce machine learning algorithms (random forest, LSTM), and mine drift-related factors based on historical configuration data (configuration change records, drift records, business load, system operating status) to achieve early prediction of drift risks. The model supports self-learning and optimizes parameters regularly (weekly). Its core functions include drift probability prediction, high-risk configuration change alerts, and drift spread range prediction. The optimized model can incorporate reinforcement learning algorithms to build a hybrid prediction model and adds an intelligent drift cause analysis function to automatically identify the core causes of drift and push disposal suggestions.

5. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The intelligent automatic repair unit is used to provide a hierarchical repair mechanism for detected configuration drift, including automatic repair, semi-automatic repair, and manual repair; build a drift repair knowledge base to achieve rapid matching and repair of similar drifts; automatically verify the fingerprint after automatic repair to ensure successful repair, and generate a repair event to be synchronized to the event tracing module; Supports repair and rollback functions, saves configuration snapshots (save period ≥ 90 days), supports one-click rollback and verification, rollback operation requires permission verification, and generates "repair and rollback event" after rollback.

6. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The event panoramic modeling unit is used to build a unified event model, covering three major categories: business operation events, configuration change events, and system operation events. All events adopt a unified format specification, including core fields such as event ID, event type, event level, trigger time, associated object, event details, and processing status, to ensure cross-module event correlation analysis capabilities.

7. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The configuration-event bidirectional linkage unit is used to achieve deep linkage between configuration changes and event tracing, forming a closed loop of "configuration → event → configuration". Specifically, it includes: automatically generating a "configuration change event" when a configuration change occurs, synchronizing it to the event tracing module and associating it with configuration fingerprint and other information; automatically associating the corresponding configuration information when an event is abnormal, triggering configuration fingerprint verification, checking whether it is caused by configuration drift, and pushing drift details and repair suggestions; and building a complete event chain based on the associated fields to clearly present the impact of configuration changes on business and the system.

8. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The event panoramic tracing and time-series replay unit is used to store all events in a centralized event repository (time-series database InfluxDB + distributed log system ELK Stack) in chronological order, without overwriting any event state, in an event tracing mode. It retains the original data, supports system state restoration at any point in time, and supports multi-dimensional event retrieval (fuzzy query, combined query), event time-series replay, and event statistical analysis, with a query response time of ≤1 second. The optimized version supports natural language search, one-click event chain search, precise playback, and playback recording functions.

9. The enterprise service management system based on a cloud platform according to claim 1, characterized in that, The linkage scheduling module's linkage engine is used to build a configuration-event linkage rule engine, pre-setting various linkage scenarios (configuration change → event generation, event exception → configuration verification, drift repair → event update), and supporting custom linkage rules; the unified data bus adopts a Kafka message queue to realize real-time interaction of configuration data and event data; the operation and maintenance visualization dashboard is used to integrate configuration status, event trajectory, drift warning, fault alarm and other information, presenting them in a visual chart and supporting interaction; The anomaly closed-loop management unit is used to achieve closed-loop management of "early warning → handling → verification → archiving".

10. A cloud-based enterprise service management system according to any one of claims 1-9, characterized in that, The auxiliary function module includes a permission management module and a monitoring and alarm module; the permission management module adopts a RBAC+ABAC+CBAC integrated permission model to achieve fine-grained permission control, and all permission operations are recorded as events. The monitoring and alarm module is used to monitor the system's various states in real time, preset alarm thresholds, and push alarm information through multiple channels when an alarm is triggered. The alarm event is synchronized to the event tracing module. The application interface layer provides standardized API interfaces, supports integration with existing enterprise systems, and has security mechanisms. The terminal access layer supports multi-terminal access, achieving full terminal coverage of core functions.

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