Service relationship management system and method

Abstract

The invention provides a service relationship management system and method, and is applied to the technical field of micro-service application. The system comprises a server module and a client module, and the server module comprises a multi-stage consumption relation management device, a cloud platform registration discovery device, a routing performance analysis labeling device, a consumption relation analysis labeling device and an intelligent routing identification labeling and recommendation device. And the client module comprises a routing information registration device, a routing performance index collection device, a routing data service discovery device and a calling performance index collection device. According to the invention, by combining the routing address resources of the cloud native platform, a multi-level consumption relationship management mechanism is established, service relationship fine management and differentiated routing configuration based on the cloud native architecture are realized, and the efficiency and accuracy of service discovery are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of microservice application technology, and in particular to a service relationship management system and method. Background Technology

[0002] With the widespread adoption of cloud computing and microservices, service registration and discovery have become crucial for the stable operation of systems.

[0003] However, traditional centralized service registration centers suffer from performance bottlenecks and single points of failure in large-scale microservice environments, impacting system responsiveness and reliability. Furthermore, existing technologies lack granular management of microservice consumption relationships and cannot leverage actual call dependencies within business processes, resulting in inefficient service discovery and significant resource waste. Traditional methods often employ full service information broadcasting, neglecting multi-layered dependencies across systems, modules, and internal calls, making it difficult to achieve differentiated and dynamic service routing. Simultaneously, insufficient deep integration of service discovery mechanisms with cloud-native platforms limits the realization of automation and elasticity capabilities.

[0004] Therefore, how to achieve refined management and optimization of service relationships based on cloud-native architecture, and improve the efficiency and accuracy of service discovery, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present invention provides a service relationship management system and method that overcomes or at least partially solves the above problems, the technical solution of which is as follows:

[0006] A service relationship management system includes a server module and a client module. The server module includes a multi-level consumer relationship management device, a cloud platform registration and discovery device, a route performance analysis and labeling device, a consumer relationship analysis and labeling device, and an intelligent route identification, labeling, and recommendation device. The client module includes a route information registration device, a route performance indicator collection device, a route data service discovery device, and a call performance indicator collection device.

[0007] The multi-level consumption relationship management device is used to centrally manage consumption relationships at the system level, module level, and microservice level, and to build a unified dependency graph view.

[0008] The cloud platform registration and discovery device is used to connect to multi-cloud platform APIs, integrate cross-cloud platform service routing collection and registration centers, and support multi-tenant and multi-data center deployment.

[0009] The routing performance analysis and labeling device is used to dynamically calculate service weights based on service response time and error rate, and to label the importance, availability, and priority of service routes.

[0010] The consumption relationship analysis and annotation device is used to construct a directed graph of microservice calls, dynamically analyze the weight and coupling degree of service relationships, and annotate them.

[0011] The intelligent route identification, labeling, and recommendation device is used to detect abnormal call patterns based on graph algorithms and machine learning techniques, and to provide optimization suggestions and route recommendations.

[0012] The routing information registration device is used to identify the operating environment and automatically add environment tags to the service metadata to complete the service registration;

[0013] The routing performance metric collection device is used to collect the performance metrics of the service provider and synchronize them to the registration center;

[0014] The routing data service discovery device is used to perform degraded service discovery when the registration center is unavailable by employing a multi-level caching mechanism.

[0015] The call performance metric collection device is used to collect call performance data and synchronize it to the registry center.

[0016] A service relationship management method, applied in a service relationship management system, the method comprising:

[0017] The routing information registration device in the client module collects the runtime environment information of the microservice instance and obtains service metadata containing environment tags.

[0018] The service metadata is processed by the cloud platform registration and discovery device in the server module to obtain service instance information that has completed registration in the cloud platform registration center;

[0019] The service instance information is analyzed by the multi-level consumption relationship management device in the server module to obtain a multi-level consumption relationship graph at the system level, module level, and microservice level.

[0020] The service operation performance data is collected by the routing performance indicator collection device and the call performance indicator collection device in the client module to obtain the service routing performance dataset.

[0021] The performance dataset is dynamically analyzed and calculated using the routing performance analysis and annotation device in the server module to obtain the weight score of each service route.

[0022] By combining the consumption relationship analysis and annotation device in the server module with the multi-level consumption relationship graph and the weight score, a service dependency relationship graph with dependency strength is obtained.

[0023] The intelligent route identification, labeling, and recommendation device in the server module performs intelligent analysis on the service dependency graph to obtain an optimization strategy that includes route recommendation and anomaly detection results.

[0024] The routing data service discovery device in the client module performs routing configuration based on the optimization strategy to obtain a customized service route list;

[0025] Service discovery and invocation are performed based on the service routing list, enabling intelligent service governance with weighted distribution.

[0026] Optionally, the step of processing the service metadata through the cloud platform registration and discovery device in the server module to obtain service instance information that has completed registration in the cloud platform registration center includes:

[0027] The service metadata and service basic information are merged to obtain registration request data;

[0028] Submit the registration request data to the cloud platform registration center to obtain information on successfully registered service instances;

[0029] Periodically check the liveness status of registered instances, and re-register and update the service instance information when changes are detected.

[0030] Optionally, the step of performing consumption relationship analysis on the service instance information through the multi-level consumption relationship management device in the server module to obtain a multi-level consumption relationship graph at the system level, module level, and microservice level includes:

[0031] The service instance information is parsed hierarchically to obtain three-layer structure data including system, module, and microservice;

[0032] Based on the three-layer structure data, a directed dependency relationship is constructed to obtain a multi-level consumption relationship graph.

[0033] Optionally, the step of dynamically analyzing and calculating the performance dataset through the routing performance analysis and annotation device in the server module to obtain the weight score of each service includes:

[0034] Perform response time and error rate statistical analysis on the performance dataset to obtain the basic performance indicators of the service routing;

[0035] By combining the service resource status and health status of the service route, the basic performance indicators are weighted and calculated to obtain the weight score of the service route.

[0036] Optionally, the step of analyzing the service dependency graph with dependency strength by combining the multi-level consumption relationship graph with the weighted score through the consumption relationship analysis and annotation device in the server module includes:

[0037] The weighted scores are mapped to the corresponding nodes of the multi-level consumption relationship graph to obtain weighted dependency data.

[0038] The dependency data is analyzed in depth using a coupling degree algorithm to obtain a dependency strength score;

[0039] Based on the dependency strength score, the corresponding service relationships are labeled to generate a service dependency graph.

[0040] Optionally, the step of intelligently analyzing the service dependency graph using the intelligent route identification, labeling, and recommendation device in the server module to obtain an optimization strategy that includes route recommendations and anomaly detection results includes:

[0041] Anomaly detection results are obtained by using a routing weight model to perform anomaly detection on the service dependency graph.

[0042] Based on machine learning models, predictive analysis is performed on service call patterns in the service dependency graph to obtain prediction results of potential problems.

[0043] Using the anomaly detection results and the potential problem prediction results, an optimization strategy that includes route recommendations and anomaly detection results is generated.

[0044] Optionally, the step of configuring routes based on the optimization strategy using the routing data service discovery device in the client module to obtain a customized service route list includes:

[0045] Based on the weight allocation scheme in the optimization strategy, the available service routes are prioritized to obtain the route ranking result.

[0046] The routing ranking results are filtered based on consumer-side environmental constraints to obtain a service route list.

[0047] Optionally, the step of performing service discovery and invocation based on the service routing list to achieve intelligent service governance with weighted distribution includes:

[0048] Initiate a service call based on the service route list;

[0049] During the service call process, the performance data of the call is collected in real time by the call performance indicator collection device to obtain a real-time performance snapshot, and the real-time performance snapshot is synchronized to the registry center to form performance data for feedback and optimization model.

[0050] Optionally, the method further includes:

[0051] When service calls encounter anomalies or performance degradation, the routing data service discovery device, based on a predefined degradation strategy, filters out abnormal instances from the service routing list to obtain a degraded list of available routes. The call performance metric collection device then synchronizes the abnormal event data to the intelligent route identification, labeling, and recommendation device. Based on the received abnormal event data, the intelligent route identification, labeling, and recommendation device triggers a rapid correction of the routing weight model and generates an updated optimization strategy to complete the self-healing closed loop.

[0052] By utilizing the above technical solution, the present invention provides a service relationship management system and method. The system includes a server module and a client module. The server module includes a multi-level consumer relationship management device, a cloud platform registration and discovery device, a route performance analysis and labeling device, a consumer relationship analysis and labeling device, and an intelligent route identification, labeling, and recommendation device. The client module includes a route information registration device, a route performance indicator collection device, a route data service discovery device, and a call performance indicator collection device. The method includes: collecting the runtime environment information of microservice instances through the route information registration device in the client module to obtain service metadata containing environment tags; processing the service metadata through the cloud platform registration and discovery device in the server module to obtain service instance information registered in the cloud platform registration center; performing consumer relationship analysis on the service instance information through the multi-level consumer relationship management device in the server module to obtain a multi-level consumer relationship graph at the system level, module level, and microservice level; collecting service runtime performance data through the route performance indicator collection device and the call performance indicator collection device in the client module to obtain a service route performance dataset; and collecting service runtime performance data through the route performance indicator collection device in the server module. The analysis and annotation device dynamically analyzes and calculates the performance dataset to obtain the weight score of each service route. The consumption relationship analysis and annotation device in the server-side module combines a multi-level consumption relationship graph with the weight score to obtain a service dependency graph with dependency strength. The intelligent route identification, annotation, and recommendation device in the server-side module intelligently analyzes the service dependency graph to obtain optimization strategies including route recommendations and anomaly detection results. The route data service discovery device in the client-side module configures routes based on the optimization strategies to obtain a customized service route list. Service discovery and invocation are then performed based on the service route list, achieving intelligent service governance with weighted distribution. This invention, by combining the routing address resources of a cloud-native platform, establishes a multi-level consumption relationship management mechanism, realizing refined management of service relationships and differentiated route configuration based on a cloud-native architecture, effectively improving the efficiency and accuracy of service discovery.

[0053] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0054] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0055] Figure 1 This invention illustrates a schematic diagram of the system structure of a service relationship management system provided in an embodiment of the present invention.

[0056] Figure 2 A schematic diagram of a typical deployment architecture of the service relationship management system provided in an embodiment of the present invention is shown;

[0057] Figure 3 The diagram illustrates a flowchart of one embodiment of the service relationship management method provided by this invention. Detailed Implementation

[0058] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0059] With the widespread adoption of cloud computing and microservice architectures in enterprise applications, service registration and discovery have become crucial for ensuring stable and efficient system operation. However, with the surge in the number of microservices and the increasing complexity of system architectures, traditional service registration and discovery methods have gradually revealed numerous shortcomings, primarily manifested in performance bottlenecks, single point of failure risks, and a lack of management over service consumption relationships.

[0060] Existing centralized service registry solutions typically rely on service instances registering their information with the center upon startup, allowing consumers to perform full service queries and discovery through the registry. While this mechanism works effectively in small-scale environments, in large-scale dynamic deployments, the registry can easily become a performance bottleneck, struggling to handle high-concurrency access requests and impacting system responsiveness and scalability. Furthermore, centralized architectures are susceptible to single points of failure; if the registry becomes unavailable, the entire service discovery mechanism will collapse, severely affecting system reliability and business continuity.

[0061] Furthermore, current mainstream technologies generally neglect the management of complex business consumption relationships between microservices. In real-world enterprise applications, service call dependencies are often recorded by business process systems or configuration management systems. However, existing service discovery mechanisms do not fully utilize this real consumption relationship data, resulting in service discovery being based solely on static or full-scale information, failing to reflect actual business dependencies. The lack of precise management of multi-level consumption relationships (such as cross-system, different modules within the same system, and microservice calls within a module) leads to a large amount of invalid data transmission and resource waste in service calls, and makes it difficult to achieve dynamic and differentiated service routing and discovery.

[0062] Traditional service discovery methods suffer from the inefficiency of broadcasting service information across the entire system. Consumers are forced to frequently query and process numerous irrelevant service instances, increasing network overhead and system load. Simultaneously, the lack of intelligent filtering and optimization based on business consumption relationships poses significant challenges to system operation and resource scheduling. Furthermore, existing technologies lack deep integration with cloud-native platforms, failing to fully leverage the advantages of automation, elastic scaling, and multi-tenancy support in a cloud-native environment.

[0063] In summary, traditional service registration and discovery technologies have significant shortcomings in terms of automation, performance scalability, multi-level dependency management, and accurate service discovery combined with enterprise-level business data. There is an urgent need to propose a new technical solution based on cloud-native architecture that can achieve automatic service registration, dynamic management of consumption relationships, and intelligent routing, in order to improve the stability, reliability, and operational efficiency of the system.

[0064] Based on this, this invention provides a service relationship management system and method that enables enterprises to efficiently and dynamically manage service registration and consumption relationships during automated service management, ensuring the efficiency and reliability of service calls. It solves the performance bottlenecks and single points of failure in traditional methods, and achieves refined optimization of complex microservice architectures through multi-level consumption relationship management, significantly improving the engineering efficiency and system reliability of enterprises.

[0065] like Figure 1 As shown in the figure, this invention provides a schematic diagram of a service relationship management system. The system may include a server module 100 and a client module 200. The server module 100 includes a multi-level consumer relationship management device 101, a cloud platform registration and discovery device 102, a route performance analysis and labeling device 103, a consumer relationship analysis and labeling device 104, and an intelligent route identification, labeling, and recommendation device 105. The client module 200 includes a route information registration device 201, a route performance index collection device 202, a route data service discovery device 203, and a call performance index collection device 204.

[0066] The service relationship management system provided in this embodiment of the invention is deployed on a cloud platform. It primarily acquires consumption relationships and routing information between microservices, marks routes, and distributes them to each microservice application. Simultaneously, it collects and aggregates data such as call relationships, call performance, and service performance. Combined with an intelligent analysis module, it achieves automatic identification and labeling of service calls.

[0067] Figure 2 The diagram shown is a typical deployment architecture of the service relationship management system provided in this embodiment of the invention. The service relationship management system includes two parts: a server and a client. The server is deployed independently and interfaces with the cloud platform API, while the client is integrated into the microservice application or can be deployed via a service mesh and embedded in the sidecar of a container.

[0068] The multi-level consumer relationship management device 101 is used to centrally manage consumer relationships at the system level, module level, and microservice level, and to build a unified dependency graph view.

[0069] The main function of the multi-level consumer relationship management device 101 is to centrally manage consumer relationships at different levels (including system level, module level and microservice level).

[0070] System-level consumption relationships refer to the service calls and dependencies between various business systems within an enterprise. The multi-level consumption relationship management device 101 can identify and record which systems have service interactions, helping enterprises to streamline their overall business architecture.

[0071] Module-level consumption relationships refer to the service call relationships between different business modules within the same system. The multi-level consumption relationship management device 101 aggregates the dependencies between modules, making it easier for development and operations personnel to understand module coupling and optimize design.

[0072] Among them, microservice-level consumption relationship refers to the calling relationship between individual services in a microservice architecture. The multi-level consumption relationship management device 101 can accurately track the consumption relationship of each microservice, including the number of calls, interface dependencies, and performance status, to achieve high-granularity service management.

[0073] The multi-level consumer relationship management device 101 provided in this embodiment of the invention can automatically construct a complete dependency graph view by uniformly managing these three levels of consumer relationships. The dependency graph view intuitively displays the call chain and dependency network of all enterprise services, which facilitates technical personnel to perform architecture analysis, problem localization, performance optimization and risk control, and also provides a data foundation for automated operation and maintenance, service governance and security management.

[0074] The cloud platform registration and discovery device 102 is used to connect to multiple cloud platform APIs, integrate cross-cloud platform service routing collection and registration centers, and support multi-tenant and multi-data center deployment.

[0075] The cloud platform registration and discovery device 102 can interface with different types of cloud-native platforms through standardized or customized APIs, automatically collecting service routing and registration information from each platform. Regardless of the cloud service provider or hybrid cloud architecture used by the enterprise, the device achieves compatibility and integration. By collecting routing information of microservices on various cloud platforms, services from different sources can be unified and integrated into a central registration center, facilitating subsequent service discovery and invocation. This ensures seamless interoperability of business systems regardless of the cloud platform on which services are deployed. The cloud platform registration and discovery device 102 can differentiate and manage different tenants (such as multiple business departments, subsidiaries, or customers), ensuring the isolation and management of their respective service information and improving the platform's security and scalability. For enterprise deployment needs across regions and data centers, it supports multi-data center synchronization and disaster recovery, ensuring the consistency and reliability of service registration and discovery. This allows enterprises to deploy microservices globally and ensure efficient and continuous service invocation. The cloud platform registration and discovery device 102 also supports labeling and grouping the same microservice according to different metadata (such as version, environment, tenant, tags, etc.), facilitating fine-grained management and traffic scheduling, and enabling advanced operational needs such as canary releases and environment isolation.

[0076] The cloud platform registration and discovery device 102 provided in this embodiment of the invention supports multi-tenant and multi-data center scenarios by centrally connecting to and managing the service registration and routing information of multiple cloud platforms, greatly improving the service governance and automated operation and maintenance capabilities of enterprises in a multi-cloud environment.

[0077] The routing performance analysis and labeling device 103 is used to dynamically calculate service weights based on service response time and error rate, and to label the importance, availability, and priority of service routes.

[0078] The routing performance analysis and labeling device 103, through its interface with the cloud platform, acquires various performance data of microservice routes in real time, including service health status (such as survival rate), resource usage (such as CPU and memory), and service performance metrics (such as response time and error rate). Simultaneously, it collects performance feedback from the client side regarding service routes, forming a comprehensive performance data foundation. Based on the collected performance metrics, the routing performance analysis and labeling device 103 dynamically calculates the weight of each service route. The calculation rules can consider factors such as response time, error rate, and resource utilization to comprehensively evaluate the actual operational quality of the service. The weight value reflects the current performance and reliability of the service route.

[0079] The routing performance analysis and labeling device 103 can automatically assign multi-dimensional labeling information to each route based on the calculated weights, such as importance, availability, priority, and business value.

[0080] Importance reflects how critical the route is to the business process. Routes with high importance are usually core service links.

[0081] Availability is used to indicate the current health and stability of the service route, indicating whether it can be reliably accessed.

[0082] Priority is used for scheduling and routing decisions, and services with higher priority will be given more priority in calling.

[0083] Among them, business value is used to evaluate the business contribution of service routing in combination with business context, and to assist in business optimization.

[0084] The routing performance analysis and labeling device 103 provided in this embodiment of the invention dynamically analyzes key indicators such as service response time and error rate, scientifically calculates service weights, and then intelligently labels the importance, availability and priority of routes, helping enterprises achieve refined and efficient microservice governance and operation and maintenance.

[0085] The consumer relationship analysis and annotation device 104 is used to construct a directed graph of microservice calls, dynamically analyze the weight and coupling of service relationships, and annotate them.

[0086] The consumer relationship analysis and annotation device 104 collects detailed data on microservice calls by connecting to the client-side data collection system. This includes, for example, call chain information and call relationships, performance metrics of the call requests (such as response time and throughput), error conditions (error codes and error rates), abnormal events during the call process, and the impact on the business chain.

[0087] The consumer relationship analysis annotation device 104 can combine the system-level, module-level and microservice-level call dependency information provided by the multi-level consumer relationship management device 101 to achieve deep data fusion and ensure that consumer relationship analysis has a multi-level and multi-dimensional perspective.

[0088] The consumer relationship analysis and annotation device 104 can construct a directed graph of microservice calls in real time based on collected and integrated data. Nodes in the graph represent services, and edges represent service call relationships. This directed graph includes both cross-system call dependencies and calls between modules or services within the system, clearly presenting a complex dependency topology.

[0089] The consumer relationship analysis and annotation device 104 can calculate the weight of each service call relationship through dynamic analysis of call performance indicators and abnormal situations, reflecting its call frequency, stability, and business importance. Simultaneously, it also assesses the coupling degree between services, quantifies dependency strength, and helps identify highly coupled or potentially risky call chains.

[0090] The consumer relationship analysis and annotation device 104 can annotate the call relationship in multiple dimensions based on weight and coupling degree, including call importance, stability, risk level and business priority, so as to provide a basis for operation and maintenance and architecture optimization.

[0091] The consumer relationship analysis and annotation device 104 provided in this embodiment of the invention can help enterprises achieve visualization and refined management of microservice call relationships, facilitate quick location of call bottlenecks, abnormal propagation paths and high-risk dependencies, support intelligent fault diagnosis and business impact assessment, and improve system stability and maintainability.

[0092] The intelligent route identification, labeling, and recommendation device 105 is used to detect abnormal call patterns based on graph algorithms and machine learning techniques, and to provide optimization suggestions and route recommendations.

[0093] The intelligent route identification, labeling, and recommendation device 105 uses the performance weights (calculated from indicators such as response time and error rate) of each service route and the weights of inter-service consumption relationships (indicators such as call frequency and coupling degree) provided by the front-end route performance analysis and labeling device 103 and consumption relationship analysis and labeling device 104 as inputs to construct a comprehensive service call and route performance profile. By comprehensively analyzing route weights and consumption relationship weights, it can dynamically and intelligently allocate routes and determine priorities. For example, for service call relationships with high weights and strong criticality, it automatically recommends or allocates high-reliability, low-latency route paths to ensure the stability and performance of critical business calls.

[0094] The intelligent route identification, labeling, and recommendation device 105 can use graph algorithms to accurately analyze the directed graph of microservice calls, detect circular dependencies and excessive coupling, and automatically identify potential risky call patterns through graph topology analysis.

[0095] The intelligent route identification, labeling, and recommendation device 105 can be combined with machine learning models to further enhance anomaly detection and prediction capabilities. For example: Anomaly pattern recognition: automatically learns normal call behavior and promptly detects abnormal calls that deviate from normal patterns. Business change prediction: combines historical call data and business evolution trends to predict potential new dependencies or call chain changes. Consumer relationship optimization: identifies invalid or redundant call paths and proposes optimization suggestions. Route link tuning: recommends more reasonable route configurations based on performance and load data.

[0096] The intelligent route identification, labeling, and recommendation device 105 can build a view of call dependencies between microservices, modules, and systems, helping operations and architecture personnel to grasp the complex relationships of service calls globally, supporting decision-making and risk management. When anomalies or potential problems are detected, it provides real-time alarms, along with detailed diagnostic information and optimization suggestions, to help quickly locate and resolve system bottlenecks or faults, ensuring stable service operation.

[0097] The intelligent route identification, labeling, and recommendation device 105 provided in this embodiment of the invention utilizes advanced technologies such as graph algorithms and machine learning to dynamically detect abnormal patterns in microservice calls based on routing performance and consumer relationship weight data, and intelligently recommends optimized routing strategies and adjustment schemes, effectively improving the stability, elasticity, and operational efficiency of the microservice architecture.

[0098] The routing information registration device 201 is used to identify the operating environment and automatically add environment tags to the service metadata to complete the service registration.

[0099] The routing information registration device 201 can automatically identify the operating environment of the current microservice instance, such as whether it is a development environment, a testing environment, or a production environment. In addition, it will also perceive information such as the geographical region, data center, availability zone, and resource-related tags (such as machine type, hardware specifications, etc.) to ensure a comprehensive and accurate understanding of the service's environmental attributes.

[0100] The routing information registration device 201 can automatically detect various environment configurations on the application side, such as service version number, canary release attributes (whether to participate in canary release), configuration groups, etc., and dynamically obtain the context information of service instances. Based on the identified environment configuration, it automatically appends the corresponding environment tags (such as "environment=production", "version=1.2.3", "canary=enabled", etc.) to the service's metadata, ensuring the completeness and accuracy of the registration information. Registering the service metadata with environment tags and routing group information to the registry center enables subsequent service discovery, load balancing, and routing policies to be invoked and managed more accurately based on these rich environment tags.

[0101] The routing information registration device 201 provided in this embodiment of the invention automatically senses and labels environmental information. The routing information registration device 201 supports complex microservice deployment scenarios such as multiple environments, multiple versions, and multiple regions, and facilitates the implementation of advanced routing strategies such as canary release, version routing, and regional isolation, thereby improving the flexibility and controllability of the system.

[0102] The routing performance metrics collection device 202 is used to collect performance metrics from service providers and synchronize them to the registry center.

[0103] The routing performance metric collection device 202 can continuously monitor and collect multiple performance metric data from service providers, such as response time distribution, throughput, and resource consumption. Based on the collected performance metrics, a service performance profile is generated, comprehensively reflecting the service's operational status and performance, providing foundational data for subsequent analysis and optimization. The collected performance profile data is synchronized to the registry center in real time or periodically to ensure that the registry center has the latest service performance information, supporting global service governance. The registry center, in conjunction with the performance metric data and other analysis devices (such as the consumer relationship analysis and annotation device 104), performs intelligent routing weight calculation and priority decision-making to achieve dynamic optimization of routing paths and load balancing.

[0104] The routing performance index collection device 202 provided in this embodiment of the invention reflects service performance in a timely and accurate manner, identifies performance bottlenecks, abnormal fluctuations and potential risks, and promotes the stability and high availability of microservice systems.

[0105] The routing data service discovery device 203 is used to perform degraded service discovery when the registry center is unavailable, employing a multi-level caching mechanism.

[0106] The routing data service discovery device 203 not only obtains service instance information through the traditional service registry, but also combines the consumption relationships between microservices to achieve more intelligent and accurate service discovery, and optimize call paths and resource utilization.

[0107] To improve the stability and response speed of service discovery, the routing data service discovery device 203 employs a multi-level caching strategy, which may include: Level 1 cache: local memory cache for fast request response and reduced reliance on the network; Level 2 cache: potentially local disk cache or neighbor node cache to ensure the persistence and consistency of cached data. This multi-level caching layering ensures the reliability of service discovery data.

[0108] When the registry center malfunctions, experiences network anomalies, or becomes unavailable, the routing data service discovery device 203 can automatically switch to locally cached data for service discovery, preventing call requests from failing due to the inability to obtain service addresses and ensuring the continuity and stability of business calls. When the registry center is normally available, the routing data service discovery device 203 will periodically or in real-time synchronize service registration data and update the local cache, ensuring the timeliness and accuracy of cached data and providing a reliable data foundation for degradation scenarios.

[0109] The routing data service discovery device 203 provided in this embodiment of the invention significantly improves the fault tolerance and stability of the microservice system through multi-level caching and degradation mechanisms, avoids business interruption caused by single point of failure of the registry center, and enhances the elasticity and robustness of the overall system.

[0110] The performance metrics collection device 204 is used to collect call performance data and synchronize it to the registry center.

[0111] The performance metric collection device 204, through integrated distributed tracing technology, can capture detailed information about every request in the service call chain, obtaining the call path, call level, and call relationships. Simultaneously, by combining network topology information, it provides a comprehensive understanding of the service dependency structure and network connectivity.

[0112] The call performance data can include response time, throughput, error codes and error rates, call exceptions, and the impact on the business chain.

[0113] The call performance metric collection device 204 can synchronize call performance data to the registry center in real time or periodically, forming a performance profile of service calls, which serves as an important basis for service governance and routing strategy adjustments. The registry center uses these call performance metrics, combined with other data (such as server-side performance metrics, consumption relationships, etc.), to perform intelligent tagging and service routing recommendations, enabling advanced functions such as dynamic load balancing, fault isolation, and canary releases.

[0114] The call performance indicator collection device 204 provided in this embodiment of the invention continuously monitors and analyzes call performance, helping the operation and maintenance team to discover performance bottlenecks and anomalies in a timely manner, thereby improving the overall quality of microservice calls and the stability of the system.

[0115] like Figure 3 As shown, this embodiment of the invention also provides a service relationship management method, applied in the above-mentioned service relationship management system, which may include:

[0116] S300 collects the runtime environment information of microservice instances through the routing information registration device in the client module, and obtains service metadata containing environment tags.

[0117] Specifically, embodiments of the present invention can automatically collect the runtime environment information of the current microservice instance, including context data such as environment tags, region, version, grayscale tags, and resource tags, and generate structured service metadata by combining static configuration and runtime parameters.

[0118] S310. The service metadata is processed by the cloud platform registration and discovery device in the server module to obtain service instance information that has been registered in the cloud platform registration center.

[0119] Specifically, in this embodiment of the invention, service metadata and service basic information can be merged to obtain registration request data; the registration request data can be submitted to the cloud platform registration center to obtain information on successfully registered service instances; the liveness status of registered instances can be checked periodically, and service instance information can be re-registered and updated when changes are detected.

[0120] Furthermore, this embodiment of the invention implements automatic service registration under a cloud-native architecture: the server-side module automatically collects registration information such as instance, basic attributes, and network address corresponding to the deployment group of the current microservice by connecting to the cloud platform's native API, and automatically registers the service by combining metadata such as service consumption relationships. Specifically, this includes: obtaining the running information of the microservice instance through the API provided by the cloud-native platform; merging the service metadata (including the consumption relationship list) and the service basic information into a registration request and submitting the registration information to the cloud platform registration center; storing the information after the registration center completes the verification; and periodically checking the liveness of the registered instance and the consistency of the metadata to achieve automatic re-registration and synchronous updates.

[0121] Optionally, embodiments of the present invention can also interface with cloud platform APIs to dynamically monitor the registration information of microservice applications in the cluster; classify and group service instances according to multi-dimensional attributes; and automatically update group tags and synchronize them to client devices that depend on the consumption relationship after changes in service registration information and metadata.

[0122] S320. By using the multi-level consumption relationship management device in the server module to analyze the consumption relationship of service instance information, a multi-level consumption relationship graph at the system level, module level, and microservice level is obtained.

[0123] Specifically, embodiments of the present invention can perform hierarchical parsing of service instance information to obtain three-layer structure data including system, module, and microservice; and construct directed dependency relationships based on the three-layer structure data to obtain a multi-level consumption relationship graph.

[0124] Furthermore, this embodiment of the invention implements multi-level microservice application consumption relationship management, establishing a consumption relationship management mechanism that supports three granularities: system-level, module-level, and microservice-level. It supports maintaining or obtaining multi-level consumption relationships through interface configuration, file import, and API calls. In system-level consumption relationships, it maintains the API-level call relationships between consumer applications and provider applications. In module-level consumption relationships, it records the dependency relationships between microservices within each module. It constructs a unified dependency graph view and automatically generates a consumption dependency list for each microservice.

[0125] Meanwhile, embodiments of the present invention can provide multi-level consumption relationship entry and unified view modeling: a multi-level consumption dependency model is constructed based on the entered data, forming a directed dependency relationship at the system, module, and service levels; the server-side device periodically or in real time compares the actual call chain with the defined consumption relationship to perform consistency verification and difference analysis; all modeled consumption relationships enter the consumption relationship management module, supporting client queries of the dependency range.

[0126] S330: Collect service operation performance data through the routing performance indicator collection device and the call performance indicator collection device in the client module to obtain the service routing performance dataset.

[0127] S340. The performance dataset is dynamically analyzed and calculated using the routing performance analysis and annotation device in the server module to obtain the weight score of each service route.

[0128] Specifically, embodiments of the present invention can perform response time and error rate statistical analysis on the performance dataset to obtain basic performance indicators of service routing; and combine the service resource status and health status of service routing to perform weighted calculation on the basic performance indicators to obtain the weight score of service routing.

[0129] S350. By combining the consumption relationship analysis and annotation device in the server module with the multi-level consumption relationship graph and weight score, a service dependency graph with dependency strength is obtained.

[0130] Specifically, in this embodiment of the invention, weighted scores are mapped to corresponding nodes in a multi-level consumption relationship graph to obtain weighted dependency data; a coupling degree algorithm is used to perform in-depth analysis on the dependency data to obtain a dependency strength score; and the corresponding service relationships are labeled based on the dependency strength score to generate a service dependency graph.

[0131] Furthermore, this embodiment of the invention implements service consumption relationship analysis and multi-level dependency labeling: the client module collects and uploads service call performance data and topology information; the server module continuously collects service registration and discovery information, and constructs a call directed graph by combining the call data; the weight value of each consumption relationship is calculated by an algorithm to form a quantitative dependency score; the score results are synchronized to relevant modules in the form of labels for service governance.

[0132] S360 uses the intelligent route identification, labeling, and recommendation device in the server module to intelligently analyze the service dependency graph and obtain optimization strategies that include route recommendations and anomaly detection results.

[0133] Specifically, embodiments of the present invention can utilize a routing weight model to perform anomaly detection on the service dependency graph and obtain anomaly detection results; perform predictive analysis on the service call patterns in the service dependency graph based on a machine learning model to obtain potential problem prediction results; and use the anomaly detection results and potential problem prediction results to generate an optimization strategy that includes route recommendations and anomaly detection results.

[0134] S370. The routing data service discovery device in the client module performs routing configuration based on the optimization strategy to obtain a customized service route list.

[0135] Specifically, in this embodiment of the invention, available service routes can be prioritized according to the weight allocation scheme in the optimization strategy to obtain the route ranking result; the route ranking result can be filtered based on the consumer environment constraints to obtain a service route list.

[0136] Furthermore, this embodiment of the invention implements service discovery configuration generation and distribution driven by consumer relationships: the server device obtains a list of all services currently depended upon by the consumer in the consumer relationship graph; based on the consumer's current environment and system, it filters out the routing information of accessible service providers and generates service discovery data; the service discovery configuration items are synchronized to the corresponding client device of the consumer through a data query or push channel; after receiving the data, the client device automatically updates its local set of discoverable services. Optionally, the service discovery data includes the name, instance address, version tag, routing attributes, and importance score of the target service.

[0137] Meanwhile, embodiments of the present invention can distribute services based on weight and priority: the server constructs a weight mapping relationship between the service provider and the caller based on the collected multi-dimensional data, and generates a dynamic weight score; based on the weight model results, the service routing weight is dynamically adjusted, and the service routes are distributed according to the differences in the importance of the consumption relationship; after receiving the customized routing weight information, the client constructs a local weight load balancer.

[0138] S380 enables intelligent service governance by weighted distribution through service discovery and invocation based on the service routing list.

[0139] Specifically, in this embodiment of the invention, a service call can be initiated based on a service routing list; during the service call process, the performance data of this call is collected in real time by calling a performance metric collection device to obtain a real-time performance snapshot, and the real-time performance snapshot is synchronized to the registry center to form performance data for feedback and optimization models.

[0140] Optionally, in embodiments of the present invention, when service calls encounter anomalies or performance degradation, the routing data service discovery device can, according to a predefined degradation strategy, block abnormal instances from the service routing list to obtain a degraded list of available routes. The device can then synchronize the abnormal event data to the intelligent route identification, labeling, and recommendation device by calling the performance metric collection device. Based on the received abnormal event data, the intelligent route identification, labeling, and recommendation device can trigger a rapid correction of the routing weight model and generate an updated optimization strategy to complete the self-healing closed loop.

[0141] Furthermore, this embodiment of the invention also implements automatic service discovery under a cloud-native architecture: when a consumer application initiates a service discovery request, the client device sends metadata; the server device parses the consumer instance identifier and queries its registered consumer relationship list; based on the service dependency list declared by the consumer, it queries the corresponding service provider list and its routing information; according to the consumer's environment, region, and other constraints, it filters and sorts the service provider routes; it generates a route list containing the services to be consumed and returns it to the client device, supporting both query and proactive push methods, and also supporting offline cache degradation.

[0142] The embodiments of this invention provide a closed-loop service routing distribution based on weight and priority: when an anomaly occurs during the client call process, a backup route weight mechanism can be activated locally and the server-side model weight can be quickly corrected to reassess the status of the abnormal service and push the latest routing strategy, thereby achieving intelligent self-healing and rapid recovery of the closed loop.

[0143] Although the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous.

[0144] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0145] This invention is described with reference to flowchart illustrations and / or block diagrams of methods and systems according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0147] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0148] In the description of this invention, it should be understood that if the terms "upper", "lower", "front", "rear", "left" and "right" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the position or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0149] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0150] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the present invention.

Claims

1. A service relationship management system, characterized in that, include: The system includes a server module and a client module. The server module includes a multi-level consumer relationship management device, a cloud platform registration and discovery device, a route performance analysis and labeling device, a consumer relationship analysis and labeling device, and an intelligent route identification, labeling, and recommendation device. The client module includes a route information registration device, a route performance indicator collection device, a route data service discovery device, and a call performance indicator collection device. The multi-level consumption relationship management device is used to centrally manage consumption relationships at the system level, module level, and microservice level, and to build a unified dependency graph view. The cloud platform registration and discovery device is used to connect to multi-cloud platform APIs, integrate cross-cloud platform service routing collection and registration centers, and support multi-tenant and multi-data center deployment. The routing performance analysis and labeling device is used to dynamically calculate service weights based on service response time and error rate, and to label the importance, availability, and priority of service routes. The consumption relationship analysis and annotation device is used to construct a directed graph of microservice calls, dynamically analyze the weight and coupling degree of service relationships, and annotate them. The intelligent route identification, labeling, and recommendation device is used to detect abnormal call patterns based on graph algorithms and machine learning techniques, and to provide optimization suggestions and route recommendations. The routing information registration device is used to identify the operating environment and automatically add environment tags to the service metadata to complete the service registration; The routing performance metric collection device is used to collect the performance metrics of the service provider and synchronize them to the registration center; The routing data service discovery device is used to perform degraded service discovery when the registration center is unavailable by employing a multi-level caching mechanism. The call performance metric collection device is used to collect call performance data and synchronize it to the registry center.

2. A service relationship management method, characterized in that, Applied to the service relationship management system of claim 1, the method includes: The routing information registration device in the client module collects the runtime environment information of the microservice instance and obtains service metadata containing environment tags. The service metadata is processed by the cloud platform registration and discovery device in the server module to obtain service instance information that has completed registration in the cloud platform registration center; The service instance information is analyzed by the multi-level consumption relationship management device in the server module to obtain a multi-level consumption relationship graph at the system level, module level, and microservice level. The service operation performance data is collected by the routing performance indicator collection device and the call performance indicator collection device in the client module to obtain the service routing performance dataset. The performance dataset is dynamically analyzed and calculated using the routing performance analysis and annotation device in the server module to obtain the weight score of each service route. By combining the consumption relationship analysis and annotation device in the server module with the multi-level consumption relationship graph and the weight score, a service dependency relationship graph with dependency strength is obtained. The intelligent route identification, labeling, and recommendation device in the server module performs intelligent analysis on the service dependency graph to obtain an optimization strategy that includes route recommendation and anomaly detection results. The routing data service discovery device in the client module performs routing configuration based on the optimization strategy to obtain a customized service route list; Service discovery and invocation are performed based on the service routing list, enabling intelligent service governance with weighted distribution.

3. The method according to claim 2, characterized in that, The process of processing the service metadata through the cloud platform registration and discovery device in the server module to obtain service instance information that has completed registration in the cloud platform registration center includes: The service metadata and service basic information are merged to obtain registration request data; Submit the registration request data to the cloud platform registration center to obtain information on successfully registered service instances; Periodically check the liveness status of registered instances, and re-register and update the service instance information when changes are detected.

4. The method according to claim 2, characterized in that, The step of analyzing the service instance information through the multi-level consumption relationship management device in the server module to obtain a multi-level consumption relationship graph at the system level, module level, and microservice level includes: The service instance information is parsed hierarchically to obtain three-layer structure data including system, module, and microservice; Based on the three-layer structure data, a directed dependency relationship is constructed to obtain a multi-level consumption relationship graph.

5. The method according to claim 2, characterized in that, The step of dynamically analyzing and calculating the performance dataset using the routing performance analysis and annotation device in the server module to obtain the weight score of each service includes: Perform response time and error rate statistical analysis on the performance dataset to obtain the basic performance indicators of the service routing; By combining the service resource status and health status of the service route, the basic performance indicators are weighted and calculated to obtain the weight score of the service route.

6. The method according to claim 2, characterized in that, The step of analyzing the service dependency graph with dependency strength by combining the multi-level consumption relationship graph with the weighted score through the consumption relationship analysis and annotation device in the server module includes: The weighted scores are mapped to the corresponding nodes of the multi-level consumption relationship graph to obtain weighted dependency data. The dependency data is analyzed in depth using a coupling degree algorithm to obtain a dependency strength score; Based on the dependency strength score, the corresponding service relationships are labeled to generate a service dependency graph.

7. The method according to claim 2, characterized in that, The intelligent analysis of the service dependency graph using the intelligent route identification, labeling, and recommendation device in the server module to obtain an optimization strategy that includes route recommendations and anomaly detection results includes: Anomaly detection results are obtained by using a routing weight model to perform anomaly detection on the service dependency graph. Based on machine learning models, predictive analysis is performed on service call patterns in the service dependency graph to obtain prediction results of potential problems. Using the anomaly detection results and the potential problem prediction results, an optimization strategy that includes route recommendations and anomaly detection results is generated.

8. The method according to claim 2, characterized in that, The routing data service discovery device in the client module performs routing configuration based on the optimization strategy to obtain a customized service route list, including: Based on the weight allocation scheme in the optimization strategy, the available service routes are prioritized to obtain the route ranking result. The routing ranking results are filtered based on consumer-side environmental constraints to obtain a service route list.

9. The method according to claim 2, characterized in that, The process of service discovery and invocation based on the service routing list, enabling intelligent service governance through weighted distribution, includes: Initiate a service call based on the service route list; During the service call process, the performance data of the call is collected in real time by the call performance indicator collection device to obtain a real-time performance snapshot, and the real-time performance snapshot is synchronized to the registry center to form performance data for feedback and optimization model.

10. The method according to claim 9, characterized in that, Also includes: When service calls encounter anomalies or performance degradation, the routing data service discovery device, based on a predefined degradation strategy, filters out abnormal instances from the service routing list to obtain a degraded list of available routes. The call performance metric collection device then synchronizes the abnormal event data to the intelligent route identification, labeling, and recommendation device. Based on the received abnormal event data, the intelligent route identification, labeling, and recommendation device triggers a rapid correction of the routing weight model and generates an updated optimization strategy to complete the self-healing closed loop.

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