Microservice supermarket management method and system based on knowledge graph

Abstract

The invention relates to the technical field of service orchestration, in particular to a micro-service supermarket management method and system based on a knowledge graph, and the method comprises the following steps: collecting the catalog and information of micro-services, converting data items into nodes in the knowledge graph, connecting the service conditions with interface information, and carrying out the assignment of node attributes, and generating knowledge graph node data. According to the method, the micro-service nodes are traversed and defined, the automation level of micro-service management is enhanced, through node data, a micro-service interaction graph is automatically drawn, micro-service discovery and load balancing are optimized, and user behavior data and demand matching are analyzed, so that the individuation and accuracy of micro-service recommendation are improved; by adjusting the connection of low-score micro-services, the influence of low-efficiency or fault services is effectively avoided, the stability and the reliability are improved, the dynamic management capability of the micro-services is remarkably improved, and cross-service effective integration and optimization are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of service orchestration, and in particular to a microservice supermarket management method and system based on knowledge graph. Background Art

[0002] The field of service orchestration technology focuses on managing and coordinating the interactions between multiple services to form a complete business process. In the microservice architecture, service orchestration is particularly critical because it allows developers to combine small, independent services into complex applications. Service orchestration mainly defines the interaction sequence between services through a central coordinator, and automates the calling sequence and logic of services through the orchestration engine, which not only improves the flexibility and maintainability of the system, but also helps to implement transactions and complex business logic across multiple services and applications.

[0003] Among them, the knowledge graph microservice supermarket management method refers to the use of knowledge graphs to enhance the management efficiency and intelligence level of microservices. By integrating the knowledge graph into the microservice architecture, the functions of each service can be automatically identified and coordinated to achieve efficient matching and calling between services. The main purpose is to optimize service discovery, load balancing and automatic repair of services, and improve the overall performance and reliability of the system. Under the "supermarket" management mode, microservices can be dynamically displayed and selected like goods on supermarket shelves, improving the flexibility of service use and development efficiency.

[0004] In the existing service orchestration technology, although the central coordinator can define the interaction order between services, the service management and calling logic rely on fixed predefined paths, which is not flexible enough in the face of dynamically changing service needs. Especially in the microservice architecture, the dependencies and interactions between services are highly complex, and a single orchestration engine is difficult to respond to changes in the market and user behavior in real time, limiting the rapid adaptability and innovation capabilities of services. Traditional methods still have many limitations in automated service discovery and load balancing, such as the inability to effectively identify and predict dependencies between services, resulting in service bottlenecks and performance degradation in actual operations. The lack of effective service quality monitoring and automatic repair mechanisms makes it impossible to ensure service continuity and stability, further affecting the operating efficiency and user satisfaction of the entire system. The shortcomings make the existing technology face significant challenges when dealing with highly dynamic and scalable microservice environments. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a microservice supermarket management method and system based on knowledge graph.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solution, a microservice supermarket management method based on knowledge graph, comprising the following steps:

[0007] S1: Collect the directory and information of microservices, convert data entries into nodes in the knowledge graph, connect the service status with the interface information, assign values ​​to the node attributes, and generate knowledge graph node data;

[0008] S2: Through the knowledge graph node data, traverse each node, define the functions and interfaces of microservice supermarket management, perform node connection reinforcement, draw a graph of service interactions, and generate a microservice semantic connection graph;

[0009] S3: sorting data of the microservice semantic connection graph, analyzing user behavior data, matching user preferences according to user needs, and generating a microservice recommendation list;

[0010] S4: monitoring the microservices in the microservice recommendation list, recording the number of microservice calls and user feedback, performing microservice supermarket management status evaluation, and obtaining microservice quality scores;

[0011] S5: According to the microservice quality score, perform microservice relationship adjustment in the knowledge graph, strengthen the connection of service score priority, avoid the connection of low-scoring services, and generate an adjusted knowledge graph;

[0012] S6: Based on the adjusted knowledge graph, identify the key nodes and potential risk connections that the microservice supermarket management depends on, perform service architecture optimization, adjust service deployment to match architecture changes, and generate a microservice deployment guide.

[0013] As a further solution of the present invention, the knowledge graph node data includes functional descriptions, interface types, performance indicators and user access frequencies of service components; the microservice semantic connection graph includes dependencies, data flows and collaborative behaviors of service node connections; the microservice recommendation list includes functions, expected efficiency and matching scenarios of recommended services; the microservice quality score includes response time, success rate, user satisfaction and failure rate of the service; the adjusted knowledge graph includes optimizing connection weights of service nodes, priorities of scored services and avoiding low-scoring services; the microservice deployment guide includes best practices for service deployment, resource allocation plans, potential risk assessments and optimization of service architecture.

[0014] As a further solution of the present invention, the steps of collecting the directory and information of microservices, converting data entries into nodes in the knowledge graph, connecting the service status with the interface information, and assigning values ​​to the node attributes to generate the knowledge graph node data are as follows:

[0015] S101: Collect microservice catalogs and information, record and organize basic information of each service, including function description, performance parameters and interface type, organize information into structured items, classify and integrate the items, and generate a service data item list;

[0016] S102: using the service data entry list, converting each service entry into a node of the knowledge graph, assigning an identifier to each node, giving service characteristic attributes, recording the call frequency and dependency, and obtaining a knowledge graph initialization node set;

[0017] S103: Initialize the node set through the knowledge graph, calibrate the connection between the service status and interface information for each node, analyze the data flow and calling relationship between the nodes, and generate knowledge graph node data.

[0018] As a further solution of the present invention, the steps of traversing each node through the knowledge graph node data, defining the functions and interfaces of microservice supermarket management, performing node connection enhancement, drawing a graph of service interactions, and generating a microservice semantic connection graph are as follows:

[0019] S201: Based on the knowledge graph node data, traverse each node, identify and record the function description and interface type of microservice supermarket management, set function parameters and interface call information for each node, and generate a function definition node list;

[0020] S202: using the function to define a node list, strengthening the connection between nodes, evaluating the connection of each node, identifying the connection density of key service nodes, verifying the access speed and response efficiency of key services, and obtaining a node connection strengthening graph;

[0021] S203: Draw an interactive relationship map between services through the node connection reinforcement diagram, identify the data flow and collaboration mode between services, display the interactive dynamics of the entire service architecture in a graphical manner, and generate a microservice semantic connection diagram.

[0022] As a further solution of the present invention, the steps of sorting the microservice semantic connection graph, analyzing the user's behavior data, matching the user's preferences according to the user's needs, and generating a microservice recommendation list are specifically as follows:

[0023] S301: Based on the microservice semantic connection graph, collect and integrate microservice supermarket management data, including service type, frequency of use and dependency, check data integrity and consistency, and generate an integrated service data set;

[0024] S302: using the integrated service data set, analyzing the user's interaction records and usage patterns, identifying the user's frequency of using the service and the user's preference settings for the service, and obtaining a user behavior analysis result;

[0025] S303: Intelligently match microservices based on the user behavior analysis results, sort the microservices according to the user's usage habits and preferences, and generate a microservice recommendation list based on the needs of differentiated user groups.

[0026] As a further solution of the present invention, the steps of monitoring the microservices in the microservice recommendation list, recording the number of calls and user feedback of the microservices, performing microservice supermarket management status evaluation, and obtaining microservice quality scores are as follows:

[0027] S401: Based on the microservice recommendation list, monitor the service quality of the microservices, regularly collect the number of calls and performance indicators of each service, check the real-time update of the data, and generate service call data records;

[0028] S402: using the service call data record, collecting and analyzing user feedback on each microservice, recording satisfaction scores, usage frequency and fault problems, and analyzing the user's real-time experience by integrating the information to obtain user feedback analysis results;

[0029] S403: Perform a quality assessment of the microservice based on the user feedback analysis result, quantify the service quality based on the number of calls and user satisfaction, and perform a service quality assessment to generate a microservice quality score.

[0030] As a further solution of the present invention, according to the microservice quality score, the microservice relationship in the knowledge graph is adjusted to strengthen the connection with service score priority and avoid the connection with low-scoring services. The steps of generating the adjusted knowledge graph are specifically as follows:

[0031] S501: Based on the microservice quality score, a decision tree algorithm is used to evaluate and classify the score of each microservice node, identify services with scores exceeding a set threshold, prioritize them, and generate a service quality classification list;

[0032] S502: Using the service quality classification list, adjust the connections in the knowledge graph, optimize the connections between service nodes, avoid connections of low-quality services, verify the priority and access efficiency of high-quality services in the knowledge graph, and obtain an optimized intermediate state graph;

[0033] S503: Through the optimized intermediate state graph, the interaction relationship graph of the entire microservice is reconstructed and drawn, the position and connection of the service nodes are adjusted, the implemented service scoring priority strategy is mapped, and the adjusted knowledge graph is generated.

[0034] As a further solution of the present invention, the formula of the decision tree algorithm is as follows:

[0035]

[0036] Among them, G(T) is the purity of the node after evaluation, T is the node in the decision tree, and p k is the probability of category k in node T, W is the weight coefficient, N is the number of samples in node T, C is the regularization coefficient, n is the number of samples in category k, and e is the base of the natural logarithm.

[0037] As a further solution of the present invention, based on the adjusted knowledge graph, the key nodes and potential risk connections that microservice supermarket management relies on are identified, service architecture optimization is performed, service deployment is adjusted to match architecture changes, and the steps of generating a microservice deployment guide are specifically as follows:

[0038] S601: extract key node information from the adjusted knowledge graph, analyze each component of the microservice architecture, identify key dependencies, and use dependency analysis and risk node identification to record risk connections and generate a dependency risk map;

[0039] S602: Based on the dependency risk map, adjust the service deployment to match the architecture change, use the optimized architecture parameters to make adjustments, configure the service deployment parameters according to service requirements and resource availability, and obtain an optimized service architecture;

[0040] S603: Deploy each service component according to the optimized service architecture, identify the deployment process and configuration guidance using configuration requirements and resource allocation, and generate a microservice deployment guide.

[0041] A microservice supermarket management system based on a knowledge graph, the microservice supermarket management system based on a knowledge graph is used to execute the above-mentioned microservice supermarket management method based on a knowledge graph, and the system includes:

[0042] The data collection module collects microservice catalogs and information, converts data entries into nodes in the knowledge graph, connects service conditions with interface information, assigns node attributes, and generates knowledge graph node data;

[0043] The service definition module traverses each node according to the knowledge graph node data, defines the functions and interfaces of microservice supermarket management, performs node connection reinforcement, and generates a microservice semantic connection graph;

[0044] The microservice sorting module sorts the data based on the microservice semantic connection graph, analyzes the user behavior data, matches the user preferences according to the user needs, and generates a microservice recommendation list;

[0045] The service monitoring module uses the microservice recommendation list to identify the number of calls and user feedback, evaluate the status of microservice supermarket management, and obtain microservice quality scores;

[0046] The relationship adjustment module adjusts the microservice relationships in the knowledge graph according to the microservice quality scores, identifies key nodes and risk connections, adjusts service deployment, and generates a microservice deployment guide.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by structuring the directory and information of microservices into the knowledge graph, efficient definition and matching of interactions between services are achieved, and data entries are converted into nodes of the knowledge graph, which not only simplifies the connection and coordination between microservices, but also enhances the automation level of microservice management by traversing and defining microservice nodes. Through node data, microservice interaction graphs are automatically drawn to optimize microservice discovery and load balancing. By analyzing user behavior data and matching them with needs, the personalization and accuracy of microservice recommendations are improved. The microservices in the recommendation list are monitored and quality scored to provide the system with continuous performance optimization indicators. By adjusting the connection of low-scoring microservices, the impact of inefficient or faulty services can be effectively avoided, stability and reliability can be improved, the dynamic management capabilities of microservices can be significantly improved, and effective integration and optimization across services can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a microservice supermarket management method based on knowledge graph, comprising the following steps:

[0060] S1: Collect the directory and information of microservices, convert data entries into nodes in the knowledge graph, connect service conditions with interface information, assign values ​​to node attributes, record call frequency and dependencies, and generate knowledge graph node data;

[0061] S2: Through the knowledge graph node data, traverse each node, define the functions and interfaces of microservice supermarket management, perform node connection enhancement, draw a graph of service interactions, and generate a microservice semantic connection graph;

[0062] S3: Organize the data of the microservice semantic connection graph, analyze the user's behavior data, match the user's needs and preferences, sort the microservices, and generate a microservice recommendation list;

[0063] S4: Monitor the microservices in the microservice recommendation list, record the number of microservice calls and user feedback, perform microservice supermarket management status evaluation, and obtain microservice quality scores;

[0064] S5: According to the microservice quality score, the microservice relationship in the knowledge graph is adjusted to strengthen the connection of service scores first, avoid the connection of low-scoring services, and generate an adjusted knowledge graph;

[0065] S6: Based on the adjusted knowledge graph, identify the key nodes and potential risk connections that microservice supermarket management depends on, perform service architecture optimization, adjust service deployment to match architecture changes, and generate a microservice deployment guide.

[0066] The knowledge graph node data includes the functional description, interface type, performance indicators and user access frequency of the service components. The microservice semantic connection diagram includes the dependencies, data flow and collaborative behavior of the service node connections. The microservice recommendation list includes the functions, expected efficiency and matching scenarios of the recommended services. The microservice quality score includes the response time, success rate, user satisfaction and failure rate of the service. The adjusted knowledge graph includes optimizing the connection weights of service nodes, the priority of scored services and avoiding low-scoring services. The microservice deployment guide includes the best practices for service deployment, resource allocation plans, potential risk assessments and optimization of service architecture.

[0067] See also Figure 2 , collect the directory and information of microservices, convert data entries into nodes in the knowledge graph, connect the service status with the interface information, and assign values ​​to the node attributes. The specific steps for generating knowledge graph node data are as follows:

[0068] S101: Collect microservice catalogs and information, record and organize basic information of each service, including function description, performance parameters and interface type, organize information into structured items, and classify and integrate the items to generate a service data item list. The execution process is as follows;

[0069] Collect microservice catalogs and information, including functional descriptions, performance parameters, interface types, etc. of each service. By using automated tools to scan and record data, you can effectively organize the structured entries of services. Categorize and integrate the entries, such as sorting and grouping them according to service functions, performance levels, or service types. The key is to ensure the integrity and accuracy of the data and generate a list of service data entries using the formula:

[0070]

[0071] Among them, D s represents the integration degree of service data, c i represents the classification index of the ith service, and Z represents the total number of services.

[0072] S102: Using the service data entry list, convert each service entry into a node of the knowledge graph, assign an identifier to each node, give service characteristic attributes, record the call frequency and dependency, and obtain the execution process of the knowledge graph initialization node set as follows;

[0073] Using the service data entry list, each service entry is converted into a node in the knowledge graph, a unique identifier is assigned to each service, and each node is given corresponding service characteristic attributes, such as call frequency, dependencies, and performance indicators. The attributes not only describe the functions and performance of the service, but also reflect the dependencies and collaboration patterns between services. To ensure the consistency and queryability of the data, lay the foundation for building a service dependency network, and obtain the knowledge graph initialization node set, the formula used is:

[0074]

[0075] Among them, K n Represents the total feature value of the node in the knowledge graph, a j represents the attribute value of node j, f j represents the frequency weight of the attribute, and M represents the total number of attributes.

[0076] S103: Initialize the node set through the knowledge graph, calibrate the connection between the service status and interface information for each node, analyze the data flow and call relationship between the nodes, and generate the execution process of the knowledge graph node data as follows;

[0077] Initialize the node set through the knowledge graph and calibrate the connection between the service status and interface information, including analyzing the data flow and call relationship between nodes. By drawing the interaction and data exchange paths between services, the role and relevance of each service can be clarified. Help identify potential conflict points and optimization areas between services, provide decision support for further service improvement and maintenance, and generate knowledge graph node data. The formula used is:

[0078]

[0079] Among them, G c represents the complexity of the connection in the graph, r k represents the relationship strength of connection k, l k Indicates the connection length, and L indicates the total number of connections.

[0080] See also Figure 3 ,Through the knowledge graph node data, traverse each node, define the functions and interfaces of microservice supermarket management, perform node connection enhancement, draw a graph of service interaction, and generate the microservice semantic connection graph in the following steps:

[0081] S201: Based on the knowledge graph node data, traverse each node, identify and record the function description and interface type of microservice supermarket management, set function parameters and interface call information for each node, and generate the execution process of the function definition node list as follows;

[0082] Based on the knowledge graph node data, we deeply analyze each microservice node, traverse each node, record the function description and interface type of each service, and use this information to help understand the role and operation of each service. We set corresponding function parameters and interface call information for each node, and the parameters define the function scope and interface dependency of the service in detail. Through detailed settings, we can clarify the specific functions and interface usage of each service, provide basic data for subsequent service optimization and interface adjustment, and create a function definition node list. The formula used is:

[0083]

[0084] Among them, F d represents the total amount of function definitions, f i represents the function parameter value of the i-th node, w i represents the weight factor of the function, and P represents the total number of nodes.

[0085] S202: Use the function to define the node list, strengthen the connection between nodes, evaluate the connection of each node, identify the connection density of key service nodes, verify the access speed and response efficiency of key services, and obtain the execution process of the node connection strengthening graph as follows;

[0086] The function is used to define the node list, strengthen the connection between nodes, evaluate the connection of each node, and identify the connection density of each service node. It not only involves the evaluation of physical connections, but also includes the analysis of the frequency and efficiency of calls between services. The access speed and response efficiency of key service nodes are verified through actual call tests and data analysis, and a node connection strengthening diagram is generated. The formula used is:

[0087]

[0088] Among them, L s represents the total score of connection strengthening, d j represents the connection density of the jth node, v j represents the access speed of the node, and P represents the total number of nodes.

[0089] S203: Draw the interaction relationship map between services through the node connection reinforcement diagram, identify the data flow and collaboration mode between services, and use graphics to display the interaction dynamics of the entire service architecture. The execution process of generating a microservice semantic connection diagram is as follows;

[0090] By drawing the interactive relationship map between services through the node connection reinforcement diagram, identifying the data flow and collaboration mode between services, it helps to understand how each service interacts and supports each other. By using a graphical method to display the interactive dynamics of the service architecture, you can not only intuitively see the connection between services, but also identify potential conflicts or optimization points, and generate a microservice semantic connection diagram. The formula used is:

[0091]

[0092] Among them, I r represents the sum of the strengths of the interactions, s k represents the data flow intensity between the kth services, c k represents the complexity of the collaboration pattern, and R represents the total number of service relationships.

[0093] See also Figure 4 , the steps of sorting data on the microservice semantic connection graph, analyzing user behavior data, matching user preferences according to user needs, and generating a microservice recommendation list are as follows:

[0094] S301: Based on the microservice semantic connection graph, collect and integrate microservice supermarket management data, including service type, frequency of use and dependency, check data integrity and consistency, and generate the integrated service data set. The execution process is as follows;

[0095] Based on the microservice semantic connection graph, the team collects and integrates the data of microservice supermarket management, focusing on collecting key data such as service type, usage frequency, and dependencies. Each data point is carefully reviewed to ensure the integrity and consistency of the information. The aggregation of data not only helps to better understand the actual application of the service, but also enables the detection of volatility in the data and ensures the accuracy of the data set. Through refined data processing, the integrated service data set is generated, using the formula:

[0096]

[0097] Among them, D I represents the completeness index of the integrated service data, t m Indicates the type of the mth service, u m Indicates the frequency of use, d m represents the dependency strength, and Q represents the total number of service items.

[0098] S302: using the integrated service data set to analyze the user's interaction records and usage patterns, identifying the frequency of user service usage and the user's preference settings for the service, and obtaining the user behavior analysis results. The execution process is as follows;

[0099] By integrating service data sets, we analyze user interaction records and usage patterns. By deeply analyzing user interaction data, the team can identify the frequency of use and preference settings of users for various services. The process includes not only statistical analysis, but also the identification of behavioral patterns, which can provide a detailed understanding of how users interact with services and their particular preferences for services. Through insights, we generate user behavior analysis results using the formula:

[0100]

[0101] Among them, B a represents the user behavior analysis score, f n represents the frequency of the nth user using the service, p n represents the user's preference strength for the service, and W represents the total number of users.

[0102] S303: Intelligently match microservices based on the user behavior analysis results, sort the microservices based on the user's usage habits and preferences, and generate a microservice recommendation list based on the needs of differentiated user groups. The execution process is as follows;

[0103] Through the results of user behavior analysis, microservices are intelligently matched, and services are sorted according to user usage habits and preferences. At the same time, the specific needs of differentiated user groups are considered, and advanced algorithms are used to optimize service recommendations to ensure that each user can obtain the service list that best meets their needs. A microservice recommendation list is generated using the formula:

[0104]

[0105] Among them, R l Indicates the matching degree of the recommendation list, h o represents the user preference matching degree, v o represents the service ranking value, and O represents the total number of services considered.

[0106] See also Figure 5 , monitor the microservices in the microservice recommendation list, record the number of microservice calls and user feedback, perform microservice supermarket management status evaluation, and obtain the microservice quality score in the following steps:

[0107] S401: Based on the microservice recommendation list, the service quality of the microservice is monitored, the number of calls and performance indicators of each service are regularly collected, the real-time update of the data is checked, and the execution process of generating the service call data record is as follows;

[0108] Based on the microservice recommendation list, monitor the service quality of each microservice. The process involves regularly collecting the number of calls for each service and related performance indicators, such as response time and processing efficiency. The collected data is updated and verified by real-time monitoring equipment to ensure that all records reflect the latest service status. The data not only helps monitor the operation status of the service, but also provides the necessary information to evaluate the reliability and efficiency of the service, and generates service call data records. The formula used is:

[0109]

[0110] Among them, D rrepresents the reliability index of service call data, c i represents the number of calls to the i-th service, p i represents the performance index of the corresponding service, and I represents the total number of services.

[0111] S402: Use service call data records to collect and analyze user feedback on each microservice, record satisfaction scores, usage frequency, and fault issues, and analyze the user's real-time experience by integrating information to obtain the execution process of the user feedback analysis results as follows;

[0112] Use service call data records to deeply analyze user feedback on each microservice, collect user satisfaction scores, usage frequency, and fault issues. Help the team understand the user's real-time service experience and satisfaction, and make necessary adjustments and optimizations to the service. Integrate user feedback information to provide a comprehensive perspective to evaluate the user acceptance and market performance of the service, and obtain the user feedback analysis results. The formula used is:

[0113]

[0114] Among them, F a represents the user satisfaction analysis score, s j represents the satisfaction score of the jth user, f j represents the frequency of use, and J represents the total number of users who rated it.

[0115] S403: Perform quality assessment of microservices based on user feedback analysis results, quantify service quality based on call times and user satisfaction, and perform service quality assessment to generate microservice quality scores. The execution process is as follows;

[0116] Through the analysis of user feedback, the quality of microservices is evaluated. By combining the number of service calls and user satisfaction data, the quality of each service is quantified. The scoring process takes into account the reliability, efficiency and user satisfaction of the service, providing a basis for service providers to improve services and generate microservice quality scores. The formula used is:

[0117]

[0118] Among them, Q s represents the quality score of the microservice, n k Indicates the number of calls to the kth service, s k represents the corresponding user satisfaction, and K represents the total number of services.

[0119] See also Figure 6 ,According to the microservice quality score, the microservice relationship in the knowledge graph is adjusted to strengthen the connection of service score priority and avoid the connection of low-scoring services. The specific steps to generate the adjusted knowledge graph are:

[0120] S501: Based on the microservice quality score, a decision tree algorithm is used to evaluate and classify the score of each microservice node, identify services with scores exceeding the set threshold, prioritize them, and generate a service quality classification list. The execution process is as follows;

[0121] Based on the microservice quality score, each microservice node is evaluated and classified through a decision tree algorithm. Each service node is classified according to the quality score, and services with scores exceeding the preset threshold are prioritized. Classification and sorting help identify the best performing services, guide the optimal allocation of resources and further development of services, provide a clear view, show the order of each service by quality, provide a basis for subsequent resource allocation and service improvement, and generate a service quality classification list.

[0122] The formula for the decision tree algorithm is as follows:

[0123]

[0124] Among them, G(T) is the purity of the node after evaluation, T is the node in the decision tree, and p k is the probability of category k in node T, W is the weight coefficient, N is the number of samples in node T, C is the regularization coefficient, n is the number of samples in category k, and e is the base of the natural logarithm.

[0125] The execution flow is as follows:

[0126] Calculate p k ·(1-p k ) to get the basic purity gain, multiply it by the weight coefficient W, W can be determined by analyzing the feature importance score of the node sample, divided by the number of node samples N, to ensure that the gain is not too affected by the number of samples, plus the regularization term C log(n+e), C can be determined by the cross-validation method, e is a constant to avoid the problem of undefined logarithm when n is zero, all steps are combined to form a weighted purity gain G, which is used to more accurately evaluate and classify the quality of microservice nodes.

[0127] S502: Use the service quality classification list to adjust the connections in the knowledge graph, optimize the connections between service nodes, avoid connections of low-quality services, verify the priority and access efficiency of high-quality services in the knowledge graph, and obtain the execution process of the optimized intermediate state graph as follows;

[0128] Using the service quality classification list, the service node connections in the knowledge graph are optimized and adjusted, including pruning the connections with low-quality services and enhancing the connections between high-quality services. This not only improves the overall quality of the knowledge graph, but also optimizes the access efficiency and reliability of the service. The high-quality services in the knowledge graph can obtain higher priority and better response efficiency, forming an optimized intermediate state graph. The formula used is:

[0129]

[0130] Among them, G opt Represents the optimized graph score, y j is the quality score of the jth connection, δ j is the indicator function and M is the total number of connections.

[0131] S503: Through the optimized intermediate state graph, the interaction relationship graph of the entire microservice is reconstructed and drawn, the location and connection of the service nodes are adjusted, and the implemented service scoring priority strategy is mapped to generate the execution process of the adjusted knowledge graph as follows;

[0132] Through the optimized intermediate state graph, the interaction relationship graph of the entire microservice is rebuilt and drawn, which involves adjusting the location and connection of the service nodes to better reflect the implemented service scoring priority strategy, which not only reflects the optimized interaction relationship between services, but also provides a more efficient service network structure to support the continuous improvement and development of the microservice architecture, and generates the adjusted knowledge graph using the formula:

[0133]

[0134] Among them, G final is the adjusted knowledge graph score, z k is the location optimization score of the kth node, μ k is the weight of the node in the adjusted graph, and L is the total number of nodes.

[0135] See also Figure 7 ,Based on the adjusted knowledge graph, identify the key nodes and potential risk connections that microservice supermarket management depends on, perform service architecture optimization, adjust service deployment to match architecture changes, and generate microservice deployment guides in the following steps:

[0136] S601: Extract key node information from the adjusted knowledge graph, analyze each component of the microservice architecture, identify key dependencies, and use dependencies to analyze and identify risk nodes, record risk connections, and generate a dependency risk map. The execution process is as follows;

[0137] Extract key node information from the adjusted knowledge graph, perform dependency analysis on each component of the microservice architecture, identify and record key dependencies, mark the risk nodes of each component to identify risk connections that affect service quality and stability, and display the dependencies and potential risks between service components in detail, providing a basis for further risk management and mitigation strategies, and generate a dependency risk map using the formula:

[0138]

[0139] Among them, R dep represents the total risk value of the dependent risk map, r i is the risk score of the ith dependent node, λ i is the probability of risk occurrence, and D is the total number of dependent nodes.

[0140] S602: Based on the dependency risk map, adjust the service deployment to match the architecture changes, use the optimized architecture parameters for adjustment, configure the service deployment parameters according to service requirements and resource availability, and obtain the execution process of the optimized service architecture as follows;

[0141] Based on the dependency risk map, adjust the service deployment to match the changes in the architecture, including using the optimized architecture parameters to configure the service deployment parameters according to service requirements and resource availability. Ensure the efficiency and adaptability of service deployment so that the service can better respond to system and user needs while maintaining the optimal use of resources to obtain the optimized service architecture. The formula used is:

[0142]

[0143] Among them, S opt is the score of the optimized service architecture, u| j is the optimization coefficient of the jth service deployment parameter, v j is the adjusted value of the parameter and P is the total number of parameters.

[0144] S603: Deploy each service component according to the optimized service architecture, identify the deployment process and configuration guidance using configuration requirements and resource allocation, and generate the execution process of the microservice deployment guide as follows;

[0145] According to the optimized service architecture, specific deployment is carried out for each service component. Configuration requirements and resource allocation information are used to refine the deployment process and configuration guidance to ensure that each service component can run in the most suitable environment, maximize performance and efficiency, and help the technical team to correctly deploy and maintain each microservice component. Generate a microservice deployment guide using the formula:

[0146]

[0147] Among them, C dep represents the deployment efficiency, w k is the resource weight of the kth component, q k is the configuration difficulty of the component, and C is the total number of components.

[0148] See also Figure 8 A microservice supermarket management system based on knowledge graph, the microservice supermarket management system based on knowledge graph is used to execute the above-mentioned microservice supermarket management method based on knowledge graph, the system includes:

[0149] The data collection module collects microservice catalogs and information, converts data entries into nodes in the knowledge graph, connects service conditions with interface information, assigns node attributes, and generates knowledge graph node data;

[0150] The service definition module traverses each node according to the knowledge graph node data, defines the functions and interfaces of microservice supermarket management, performs node connection enhancement, and generates a microservice semantic connection graph;

[0151] The microservice sorting module sorts data based on the microservice semantic connection graph, analyzes user behavior data, matches user preferences according to user needs, and generates a microservice recommendation list;

[0152] The service monitoring module uses a microservice recommendation list to identify call times and user feedback, evaluate the status of microservice supermarket management, and obtain microservice quality scores;

[0153] The relationship adjustment module adjusts the microservice relationships in the knowledge graph according to the microservice quality scores, identifies key nodes and risky connections, adjusts service deployment, and generates a microservice deployment guide.

[0154] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A microservice supermarket management method based on knowledge graph, characterized in that: The following steps are involved: Collect the directory and information of microservices, convert data entries into nodes in the knowledge graph, connect service conditions with interface information, assign values ​​to node attributes, and generate knowledge graph node data; Through the knowledge graph node data, traverse each node, define the functions and interfaces of microservice supermarket management, perform node connection reinforcement, draw a graph of service interactions, and generate a microservice semantic connection graph; Organize the data of the microservice semantic connection graph, analyze the user's behavior data, match the user's preferences according to the user's needs, and generate a microservice recommendation list; Monitor the microservices in the microservice recommendation list, record the number of microservice calls and user feedback, perform microservice supermarket management status evaluation, and obtain microservice quality scores; According to the microservice quality score, perform microservice relationship adjustment in the knowledge graph, strengthen the connection of service score priority, avoid the connection of low-scoring services, and generate an adjusted knowledge graph; Based on the adjusted knowledge graph, key nodes and potential risk connections that microservice supermarket management depends on are identified, service architecture optimization is performed, service deployment is adjusted to match architecture changes, and a microservice deployment guide is generated.

2. The microservice supermarket management method based on knowledge graph according to claim 1 is characterized in that: The knowledge graph node data includes the functional description, interface type, performance indicators and user access frequency of the service component, the microservice semantic connection diagram includes the dependency, data flow and collaborative behavior of the service node connection, the microservice recommendation list includes the function, expected efficiency and matching scenario of the recommended service, the microservice quality score includes the response time, success rate, user satisfaction and failure rate of the service, the adjusted knowledge graph includes optimizing the connection weight of the service node, the priority of the scoring service and avoiding low-scoring services, and the microservice deployment guide includes the best practices for service deployment, resource allocation plans, potential risk assessment and optimization of the service architecture.

3. The microservice supermarket management method based on knowledge graph according to claim 1 is characterized in that: Collect the directory and information of microservices, convert data entries into nodes in the knowledge graph, connect the service status with the interface information, and assign values ​​to the node attributes. The specific steps to generate knowledge graph node data are as follows: Collect microservice catalogs and information, record and organize basic information of each service, including functional description, performance parameters and interface types, organize information into structured items, classify and integrate items, and generate a list of service data items; Using the service data entry list, convert each service entry into a node of the knowledge graph, assign an identifier to each node, give service characteristic attributes, record the call frequency and dependency, and obtain a knowledge graph initialization node set; The node set is initialized through the knowledge graph, the connection between the service status and interface information is calibrated for each node, the data flow and calling relationship between the nodes are analyzed, and the knowledge graph node data is generated.

4. The microservice supermarket management method based on knowledge graph according to claim 1 is characterized in that: Through the knowledge graph node data, traverse each node, define the functions and interfaces of microservice supermarket management, perform node connection enhancement, draw a graph of service interaction, and generate a microservice semantic connection graph in the following steps: Based on the knowledge graph node data, traverse each node, identify and record the function description and interface type of microservice supermarket management, set function parameters and interface call information for each node, and generate a function definition node list; The function is used to define a node list, strengthen the connection between nodes, evaluate the connection of each node, identify the connection density of key service nodes, verify the access speed and response efficiency of key services, and obtain a node connection strengthening graph; Through the node connection reinforcement diagram, the interaction relationship map between services is drawn, the data flow and collaboration mode between services are identified, the interaction dynamics of the entire service architecture are displayed graphically, and a microservice semantic connection diagram is generated.

5. The microservice supermarket management method based on knowledge graph according to claim 1 is characterized in that: The steps of sorting data on the microservice semantic connection graph, analyzing user behavior data, matching user preferences according to user needs, and generating a microservice recommendation list are as follows: Based on the microservice semantic connection graph, collect and integrate microservice supermarket management data, including service type, frequency of use and dependency, check data integrity and consistency, and generate an integrated service data set; Using the integrated service data set, analyzing the user's interaction records and usage patterns, identifying the frequency of user service use and the user's preference settings for the service, and obtaining user behavior analysis results; Through the user behavior analysis results, microservices are intelligently matched, microservices are sorted according to user usage habits and preferences, and a microservice recommendation list is generated according to the needs of differentiated user groups.

6. The microservice supermarket management method based on knowledge graph according to claim 1 is characterized in that: The steps of monitoring the microservices in the microservice recommendation list, recording the number of microservice calls and user feedback, and performing microservice supermarket management status evaluation to obtain microservice quality scores are as follows: Based on the microservice recommendation list, monitor the service quality of the microservices, regularly collect the number of calls and performance indicators of each service, check the real-time update of the data, and generate service call data records; The service call data records are used to collect and analyze user feedback on each microservice, record satisfaction scores, usage frequency, and fault issues, and analyze the user's real-time experience by integrating information to obtain user feedback analysis results; The microservice quality evaluation is performed based on the user feedback analysis results, the service quality is quantified based on the number of calls and user satisfaction, and the service quality evaluation is performed to generate a microservice quality score.

7. The microservice supermarket management method based on knowledge graph according to claim 1 is characterized in that: According to the microservice quality score, the microservice relationship in the knowledge graph is adjusted to strengthen the connection of service score priority and avoid the connection of low-scoring services. The steps of generating the adjusted knowledge graph are as follows: Based on the microservice quality score, a decision tree algorithm is used to evaluate and classify the score of each microservice node, identify services with scores exceeding a set threshold, prioritize them, and generate a service quality classification list; Adopting the service quality classification list, adjusting the connections in the knowledge graph, optimizing the connections between service nodes, avoiding the connections of low-quality services, verifying the priority and access efficiency of high-quality services in the knowledge graph, and obtaining an optimized intermediate state graph; Through the optimized intermediate state graph, the interaction relationship graph of the entire microservice is reconstructed and drawn, the position and connection of the service nodes are adjusted, the implemented service scoring priority strategy is mapped, and the adjusted knowledge graph is generated.

8. The microservice supermarket management method based on knowledge graph according to claim 7 is characterized in that: The formula of the decision tree algorithm is as follows: Among them, G(T) is the purity of the node after evaluation, T is the node in the decision tree, and p k is the probability of category k in node T, W is the weight coefficient, N is the number of samples in node T, C is the regularization coefficient, n is the number of samples in category k, and e is the base of the natural logarithm.

9. The microservice supermarket management method based on knowledge graph according to claim 1 is characterized in that: Based on the adjusted knowledge graph, identify the key nodes and potential risk connections that microservice supermarket management relies on, perform service architecture optimization, adjust service deployment to match architecture changes, and generate a microservice deployment guide in the following steps: Extract key node information from the adjusted knowledge graph, analyze each component of the microservice architecture, identify key dependencies, and use dependencies to analyze and identify risk nodes, record risk connections, and generate a dependency risk map; Based on the dependency risk map, adjust the service deployment to match the architecture changes, use the optimized architecture parameters to make adjustments, configure the service deployment parameters according to service requirements and resource availability, and obtain the optimized service architecture; According to the optimized service architecture, each service component is deployed, and the deployment process and configuration guidance are identified using configuration requirements and resource allocation to generate a microservice deployment guide.

10. A microservice supermarket management system based on knowledge graph, characterized in that: According to the knowledge graph-based microservice supermarket management method according to any one of claims 1 to 9, the system comprises: The data collection module collects microservice catalogs and information, converts data entries into nodes in the knowledge graph, connects service conditions with interface information, assigns node attributes, and generates knowledge graph node data; The service definition module traverses each node according to the knowledge graph node data, defines the functions and interfaces of microservice supermarket management, performs node connection reinforcement, and generates a microservice semantic connection graph; The microservice sorting module sorts the data based on the microservice semantic connection graph, analyzes the user behavior data, matches the user preferences according to the user needs, and generates a microservice recommendation list; The service monitoring module uses the microservice recommendation list to identify the number of calls and user feedback, evaluate the status of microservice supermarket management, and obtain microservice quality scores; The relationship adjustment module adjusts the microservice relationships in the knowledge graph according to the microservice quality scores, identifies key nodes and risk connections, adjusts service deployment, and generates a microservice deployment guide.

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