Method and device for supporting unified dimension display of multi-model data, equipment and medium

Abstract

The embodiment of the invention discloses a method and device for supporting unified dimension display of multi-model data, equipment and a medium, and relates to the technical field of data management.The method comprises the steps that a plurality of data models are defined, and at least one attribute is defined for each data model; configuring a model association rule, wherein the model association rule comprises a constraint relationship between the data models and an attribute association rule based on the attributes; based on the model association rule, performing matching calculation on the configuration items in the plurality of data models, and automatically generating and storing a parent-child dependency relationship among the configuration items; and in response to a view query request, generating and outputting a unified dimension data view based on the stored father-child dependency relationship. According to the method, a data island in a traditional configuration management database is effectively broken, automatic association and unified display of cross-model data are realized, the data maintenance cost is remarkably reduced, and the data integration utilization efficiency and the operation and maintenance management efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a method, apparatus, device, and medium that supports unified dimensional display of multi-model data. Background Technology

[0002] In the asset data management of Internet companies, the Configuration Management Database (CMDB) serves as a core tool, undertaking the important responsibility of storing and managing various types of asset data. Its stored objects typically cover multiple model data such as applications, databases, and middleware, and it provides users with basic data query functions.

[0003] However, in practical use, traditional CMDBs have gradually revealed their limitations in data integration and relationship presentation. On the one hand, because the metadata of each model is independent and lacks a systematic correlation mechanism, it is difficult for data from different models to form an effective linkage. This data fragmentation makes it difficult for enterprises to obtain a unified and coherent data view when conducting cross-model data analysis and global decision-making, and data resources cannot be fully integrated and utilized.

[0004] On the other hand, traditional CMDBs have significant shortcomings in presenting the upstream and downstream dependencies of specific model data. For example, when an application needs to depend on a specific Redis instance or runtime environment, such dependency chains cannot be clearly displayed. This makes it difficult for operations and maintenance personnel to quickly locate the complete relationship path when conducting troubleshooting, impact analysis, or resource assessment, increasing the operational complexity and reducing operational response efficiency. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium that supports unified dimensional display of multi-model data. The technical problem it aims to solve is that traditional configuration management databases lack an effective mechanism for displaying multi-model data associations and dependencies, leading to difficulties in data integration and utilization, and low efficiency in operation and maintenance management.

[0006] In a first aspect, embodiments of the present invention provide a method for supporting unified dimensional display of multi-model data, comprising: Define multiple data models, and define at least one attribute for each data model; Configure model association rules, which include constraint relationships between data models and attribute association rules based on the attributes; Based on the model association rules, matching calculations are performed on configuration items in multiple data models to automatically generate and store parent-child dependency relationships between the configuration items. In response to a view query request, a unified dimension data view is generated and output based on the stored parent-child dependency relationship.

[0007] A further technical solution is that the configuration model association rules include: Configure model constraints, which are used to define the association type and data correspondence rules between the source model and the target model; Configure attribute association rules, which are used to define the matching conditions between the attributes of the source model and the attributes of the target model.

[0008] A further technical solution is that the attribute association rule is defined through a structured logical expression; the logical expression includes logical operators and at least one condition judgment unit, the condition judgment unit being used to define the matching relationship between an attribute of the source model and an attribute of the target model.

[0009] A further technical solution is that, based on the model association rules, matching calculations are performed on configuration items in multiple data models to automatically generate and store parent-child dependency relationships between the configuration items, including: The source and target configuration items to be matched are determined based on the model constraint relationships. Based on the matching conditions defined in the attribute association rules, determine whether the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association conditions; If the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association condition, then a parent-child dependency relationship is established between the source configuration item and the target configuration item.

[0010] A further technical solution is that the parent-child dependency relationship is stored in a relational data table, which at least includes a parent configuration item identifier field and a child configuration item identifier field.

[0011] A further technical solution is that, in response to a view query request, a unified dimensional data view is generated and output based on the stored parent-child dependency relationship, including: Parse the view query request to obtain the target dimension identifier and the target configuration item identifier; Starting with the target configuration item identifier as the starting node, a recursive traversal is performed based on the parent-child dependency relationship to obtain all associated configuration items with direct or indirect relationships. Based on the target dimension identifier, filter out the configuration items belonging to the specified data model from the associated configuration items; The selected configuration items are combined according to their model type and dependencies to generate and output the unified dimension data view.

[0012] A further technical solution is that the method further includes: When any configuration item of a data model changes, the matching calculation of the relevant configuration item is automatically triggered, and the parent-child dependency relationship is updated.

[0013] Secondly, embodiments of the present invention also provide an apparatus for supporting unified dimensional display of multi-model data, which includes a unit for performing the above-described method.

[0014] Thirdly, embodiments of the present invention also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0016] This invention provides a method, apparatus, device, and medium for supporting unified dimensional display of multi-model data. The method includes: defining multiple data models and defining at least one attribute for each data model; configuring model association rules, including constraints between data models and attribute association rules based on the attributes; performing matching calculations on configuration items in the multiple data models based on the model association rules, automatically generating and storing parent-child dependencies between the configuration items; and generating and outputting a unified dimensional data view based on the stored parent-child dependencies in response to a view query request. By defining data model attributes, configuring model association rules, automatically generating parent-child dependencies, and displaying a unified view, a mechanism for integrating and visualizing multi-model data is constructed. This invention effectively breaks down data silos in traditional configuration management databases, achieves automatic association and unified display of cross-model data, significantly reduces data maintenance costs, and improves data integration and utilization efficiency and operational management effectiveness. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for supporting unified dimensional display of multi-model data provided in an embodiment of the present invention; Figure 2 A unified view for displaying data from multiple models; Figure 3A diagram illustrating the upstream and downstream relationships of the data; Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0023] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0024] Please see Figure 1 This invention provides a method for supporting unified dimensional display of multi-model data. By systematically constructing a data association and visualization mechanism, it can effectively solve the inherent defects of traditional configuration management databases in multi-model data integration and dependency display, thereby significantly improving data utilization efficiency and operation and maintenance management level. Specifically, the method includes the following steps: S1 defines multiple data models and defines at least one attribute for each data model.

[0025] In practice, defining multiple data models and core attributes for each model laid the foundation for data standardization for the entire technical solution. This gave heterogeneous data, originally scattered across different models, a unified description standard and interaction interface. For example, attributes such as application name, responsible person, and business domain can be defined for application models, while attributes such as instance name, environment, and associated application can be defined for database models. This standardized attribute definition enables data from different data models to interact within the same semantic space, fundamentally eliminating ambiguities in data understanding caused by inconsistent metadata standards, and creating prerequisites for subsequent automated association.

[0026] For example, in one embodiment, the data model includes: (1) Second-order domain model

[0027] Table 1. Definition Table of Second-Level Domain Model (2) Application Model

[0028] Table 2. Application Model Definition Table (3) RocketMQ model

[0029] Table 3. RocketMQ Model Definition Table (4) Redis model

[0030] Table 4. Redis Model Definition Table (5) Environmental Model

[0031] Table 5. Environmental Model Definition Table S2, Configure model association rules, which include constraint relationships between data models and attribute association rules based on the attributes.

[0032] In practice, logical connections between data are further constructed by configuring model association rules. This rule system includes constraint relationships between models and attribute-based association rules. The model constraint relationships define, at a macro level, the allowed types and quantities of associations between different models, such as specifying that an application can contain multiple database instances, or that a business domain can be associated with multiple applications. This constraint relationship provides a structured framework for data association, preventing invalid or illogical associations from occurring.

[0033] Furthermore, attribute association rules specify the judgment logic for specific association conditions at the micro level. For example, they require that the application's business code must be consistent with the database instance's business code to establish an association. This two-layer rule design ensures both the overall rationality of the association relationship and the accuracy of the specific association conditions, so that data association will neither miss necessary connections nor generate incorrect links, effectively improving accuracy.

[0034] In some preferred embodiments, the above step "configure model association rules" specifically includes the following steps: configuring model constraint relationships, which are used to define the association type and data correspondence rules between the source model and the target model; configuring attribute association rules, which are used to define the matching conditions between the attributes of the source model and the attributes of the target model.

[0035] In practice, the model association rules are refined into model constraint relationships and attribute association rules, further clarifying the logical hierarchy and precision of data association. Model constraint relationships define the association type and data correspondence rules between the source model and the target model. For example, it clarifies the one-to-many relationship that "an application can contain multiple database instances," standardizing the association framework between models at a macro level and avoiding confusion in association relationships.

[0036] Furthermore, attribute association rules ensure the accuracy of associations by defining matching conditions between specific attributes. For example, in the association between an application model and a database model, a matching condition can be set as "the business code of the application must be consistent with the business code of the database," thereby avoiding incorrect associations. This hierarchical configuration approach makes association rules both generally standardized and flexible in detail. Users can adjust constraints or attribute conditions according to actual needs, such as changing "containment" relationships to "association" relationships, or adding environment type judgments to attribute conditions, thus adapting to different business scenarios.

[0037] Furthermore, this clear rule definition provides a clear logical basis for system implementation, making the automatic matching calculation process predictable and verifiable, and improving the system's reliability and maintainability.

[0038] In one embodiment, the specific implementation is as follows: The model constraint relationships are configured through a model relationship configuration interface that receives user input and defines the source model identifier, target model identifier, association type, and data correspondence rules. The association type includes inclusion, reference, or dependency relationships. The data correspondence rules are used to define the quantitative constraint relationships between the source model and the target model, including one-to-one, one-to-many, or many-to-many relationships, which are not specifically limited in this invention. The model constraint relationships are persistently stored in a model relationship table for use during matching calculations.

[0039] The configuration attribute association rules are specifically implemented by receiving user-defined logical expressions through a rule configuration interface. These logical expressions are composed of at least one condition judgment unit combined with logical operators. Each condition judgment unit defines a matching condition between a specific attribute of the source model and a specific attribute of the target model. This matching condition is defined using a relational operator, which includes equal to, not equal to, contain, or belong to, and is not specifically limited in this invention. The logical expression is stored as a structured data object. During matching calculations, a rule parsing engine loads and executes the logical expression to determine whether the source configuration item and the target configuration item satisfy the association condition.

[0040] For example, in one embodiment, the model constraint relationships are as follows:

[0041] Table 6. Model Constraint Relationship Table In some preferred embodiments, the attribute association rules are defined by structured logical expressions; the logical expressions include logical operators and at least one conditional judgment unit, which is used to define the matching relationship between an attribute of the source model and an attribute of the target model.

[0042] In practice, by using structured logical expressions to define attribute association rules, the flexibility and complexity handling capabilities of rule expression are significantly improved. Logical expressions, through combinations of logical operators and conditional judgment units, can describe multi-level, multi-condition association logic.

[0043] For example, in a scenario where an application is associated with a message queue, complex association rules can be set (such as requiring that "the application's environment type is a production environment and its encoding is consistent with the application encoding of the message queue, or that the person responsible for the application belongs to the operations and maintenance team"). Such complex conditions are difficult to achieve using traditional fixed configuration methods. This solution, however, uses structured logical expressions to accurately map complex business logic into machine-executable rules.

[0044] Furthermore, this expression method enhances the system's scalability. When business rules change, only the content of the logical expression needs to be adjusted, without modifying the program code. For example, if a condition for judging the application version needs to be added later, only a new condition judgment unit needs to be added to the expression.

[0045] Furthermore, structured logical expressions facilitate visual configuration and maintenance. Users can generate expressions by dragging and dropping on the interface or filling in forms, which lowers the technical threshold and allows business personnel to participate in rule management.

[0046] In some preferred embodiments, the logical expression is in JSON format.

[0047] In practice, by limiting logical expressions to JSON format, a balance between expressiveness and ease of processing is achieved in attribute association rules. JSON, as a lightweight, hierarchical data exchange format, naturally suits the key-value pair structure for expressing the various components of logical rules. For example, the "func" key clearly represents logical operators, while the "value" key can hold attribute identifiers or nested expression objects. This structure is easy for humans to read and write, and also easy for machines to parse and execute.

[0048] Furthermore, at the system implementation level, JSON-formatted rules can be directly and natively supported by modern programming languages, eliminating the need to develop complex parsers and reducing implementation complexity. For example, the system can directly use JavaScript or Python's JSON library to convert rule strings into operable data structures.

[0049] Furthermore, the universality of the JSON format facilitates the storage and transmission of rule data. Rules can be stored entirely in text fields in a database or passed between system modules via API without format conversion. In addition, the JSON format enables visual configuration of rules; the front-end interface can generate JSON rules, which can be used directly by the back-end, achieving a unified data format between the front-end and back-end, improving development efficiency and system maintainability.

[0050] S3. Based on the model association rules, perform matching calculations on the configuration items in multiple data models, and automatically generate and store the parent-child dependency relationships between the configuration items.

[0051] In practical implementation, based on the aforementioned model association rules, this invention performs association analysis on configuration items in multiple data models through an automated matching calculation process. It automatically generates and stores parent-child dependencies between configuration items, realizing the transformation from rule definition to actual relationship establishment. By automatically executing the matching algorithm, the system replaces the tedious process of manually identifying and maintaining data associations as in traditional methods. This automated processing not only significantly reduces the labor costs of data maintenance but, more importantly, ensures the timeliness and accuracy of data associations, avoiding data inconsistencies caused by delays or negligence in manual operations.

[0052] In some preferred embodiments, the above step "based on the model association rules, performing matching calculations on configuration items in multiple data models, automatically generating and storing parent-child dependency relationships between the configuration items" specifically includes the following steps: determining the source configuration item and target configuration item to be matched according to the model constraint relationship; determining whether the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association condition according to the matching conditions defined in the attribute association rules; if the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association condition, then establishing a parent-child dependency relationship between the source configuration item and the target configuration item.

[0053] In practice, by specifically defining the steps of the matching calculation, the establishment of association relationships is automated and precise. By determining the source and target configuration items to be matched based on model constraints, the matching scope is narrowed down from a macro level, avoiding the performance overhead of matching all data. For example, after clarifying that "there is an association constraint between the application model and the database model," the system only needs to perform matching calculations on the data of these two types of models, without having to traverse all models.

[0054] Furthermore, the system determines whether attribute values ​​meet the conditions based on attribute association rules, ensuring the accuracy of the association. For example, when determining whether an application is associated with a specific database, the system checks whether the "application's business unit" attribute matches the "database's business unit" attribute, thereby avoiding incorrect associations.

[0055] Furthermore, parent-child dependencies are automatically established when conditions are met, enabling real-time and dynamic data association. This process completely replaces the traditional method of manually maintaining relationships in CMDBs, not only improving efficiency but also eliminating data inconsistencies or update delays caused by manual operations. For example, when the business unit attribute to which the database belongs changes, the system can automatically recalculate and update its association with the application, ensuring the real-time accuracy of the data link.

[0056] In some preferred embodiments, the parent-child dependency relationship is stored in a relational data table, which includes at least a parent configuration item identifier field and a child configuration item identifier field.

[0057] In practice, efficient storage and fast querying of dependency relationships are achieved by storing parent-child dependencies in a relational data table containing parent and child configuration item identifiers. This storage structure records the complete dependency chain with a minimal data model; for example, a complex multi-level dependency tree can be expressed through a two-dimensional relationship of "parent ID - child ID". This design makes data querying and traversal extremely efficient. For example, when querying all downstream dependencies of an application, the system only needs to retrieve the record with the application ID as the parent ID in the relational table to quickly obtain its direct child nodes, and then obtain the complete chain through recursive querying.

[0058] Furthermore, this standardized storage method facilitates integration with other systems. For example, operation and maintenance monitoring systems can directly read this relational table to construct resource topology diagrams; change management systems can analyze the scope of impact based on dependencies. The structure of the relational data table also ensures data consistency and maintainability. Through the database's transaction mechanism, the atomicity of add, delete, and modify operations on dependencies can be guaranteed, avoiding data inconsistency.

[0059] Furthermore, this storage method provides a foundation for data traceability. For example, by querying historical snapshots of relational tables, the change trajectory of dependencies can be analyzed, assisting in troubleshooting and auditing.

[0060] S4, in response to the view query request, generate and output a unified dimension data view based on the stored parent-child dependency relationship.

[0061] In practical implementation, this invention generates and outputs a unified-dimensional data view based on stored parent-child dependencies by responding to view query requests, providing users with an intuitive and comprehensive display of data relationships. When a user needs to understand the complete relationship of a specific resource configuration, the system can quickly extract relevant information from the stored dependencies and present it in a unified view format.

[0062] For example, when a user queries a core application, the system can simultaneously display all the databases, middleware, runtime environments, and other resources that the application depends on, forming a complete resource configuration topology diagram. This unified display method eliminates the need for users to repeatedly switch between different models or systems, greatly improving the efficiency of data retrieval and analysis.

[0063] In some preferred embodiments, the above step "in response to a view query request, generating and outputting a unified dimension data view based on the stored parent-child dependency relationship" specifically includes the following steps: parsing the view query request to obtain the target dimension identifier and the target configuration item identifier; using the target configuration item identifier as the starting node, recursively traversing based on the parent-child dependency relationship to obtain all associated configuration items with direct or indirect relationships; filtering configuration items belonging to a specified data model from the associated configuration items according to the target dimension identifier; combining the filtered configuration items according to their model type and dependency relationship to generate and output the unified dimension data view.

[0064] In practice, by refining the view generation steps, the system achieves accurate generation and efficient response of unified-dimensional data views. First, by parsing the view query request, the system obtains the target dimension identifier and configuration item identifier, enabling it to accurately understand the user's intent. For example, if a user requests "show application A and all its dependent resources," the system can locate application A and determine that the query uses "application" as the dimension.

[0065] Furthermore, recursive traversal starting from the target configuration item ensures the integrity of the associated data. For example, starting from application A, its directly dependent databases, indirectly dependent network devices, and underlying servers can be retrieved level by level, forming a complete link view.

[0066] Furthermore, by filtering configuration items for a specified data model based on the target dimension identifier, customized view output is achieved. For example, when a user only focuses on infrastructure, the system can filter out application model data and retain only server, network, and other model data.

[0067] Furthermore, data is combined and views are generated according to model type and dependencies, resulting in outputs that are both hierarchical and consistent with business logic. This step-by-step view generation mechanism not only ensures the accuracy and completeness of the data but also meets diverse user data viewing needs through flexible filtering and combination capabilities, thereby improving the system's usability and user experience.

[0068] In one embodiment, the specific implementation is as follows: The view query request is parsed, and the target dimension identifier and target configuration item identifier are extracted from the predetermined fields of the request. The target dimension identifier is used to specify the core data model displayed in the view, and the target configuration item identifier is a unique identifier of a specific configuration item in the core data model.

[0069] Furthermore, taking the target configuration item identifier as the starting node, a recursive traversal is performed based on the parent-child dependency relationships stored in the relational data table. The recursive traversal process is as follows: first, query all direct associations in the relational data table that use the target configuration item identifier as the parent or child configuration item identifier to obtain the first-level associated configuration item set; then, taking each configuration item identifier in the first-level associated configuration item set as a new starting node, repeat the above query process to iteratively obtain all deep-level associated configuration items until there are no new associated records in the relational data table, thereby obtaining a complete association set containing all direct or indirect associated configuration items.

[0070] Furthermore, based on the data model specified by the target dimension identifier, configuration items whose model types match the target dimension identifier are selected from the complete association set.

[0071] Furthermore, the selected configuration items are combined according to their model type and dependencies to generate the unified dimension data view; based on the parent-child dependency relationships recorded in the relational data table, a tree-like or graph-like data structure is constructed, where the root node is the target configuration item and the child nodes are configuration items that depend on or are contained in this configuration item; finally, the data structure is serialized into a unified dimension data view in a predetermined format and returned through an output interface.

[0072] For example, in one embodiment, see Figure 2 , Figure 2 This diagram presents a unified view of multi-model data, showcasing the configuration items of multiple data models associated with the target application "jintong-web". The view centrally displays various metadata related to the target application, including but not limited to message queue instances, container Pod resources, database instances, and domain name configuration items. Specifically, the message queue instance includes "mq-jintong-prod", the container Pod resource includes "pod-jintong-web-01", the database instance includes "db-jintong-user", and the domain name configuration item includes "jintong-web.example.com".

[0073] Furthermore, the view provides a user interaction interface. When a user triggers the "Infrastructure" option, the system responds by switching the display dimension of the current view from the application dimension to the secondary domain dimension. Under the secondary domain dimension, the system displays all infrastructure configuration items associated with the secondary domain to which the target application belongs. These infrastructure configuration items include one or more of server devices, storage devices, and network devices, which are not specifically limited in this invention.

[0074] For example, in one embodiment, see Figure 3 , Figure 3 This diagram illustrates the upstream and downstream data relationships of the target application "servicetreecore". In this topology, the upstream association of the target application points to the container Pod configuration item "Container POD-servicetreecore-02" in the infrastructure model, and the downstream association points to the database configuration item "service_tree" in the database model. The topology also identifies deployment instances of the target application in multiple runtime environments, including production, testing, and pre-production environments. This topology allows operations personnel to intuitively identify the complete data flow links of the target application, effectively supporting fault tracing and resource dependency analysis, and improving operational management efficiency.

[0075] In some preferred embodiments, the method further includes: automatically triggering a matching calculation of the relevant configuration items and updating the parent-child dependency relationship when any configuration item of a data model changes.

[0076] In practice, by introducing a mechanism that automatically triggers dependency updates when data changes, the real-time and consistency maintenance of data relationships is achieved. In traditional CMDBs, when the attribute of a configuration item changes, its relationship with other configuration items often needs to be manually re-verified and updated. This is not only inefficient, but also prone to data distortion due to untimely updates.

[0077] In this embodiment, by automatically triggering matching calculations, the problem of data association distortion caused by untimely manual updates is completely solved. For example, when the "belonging business unit" attribute of a database instance changes, the system immediately retrieves all application model data that uses that database as the association target, recalculates the attribute matching conditions, and automatically removes associations that no longer meet the conditions and establishes new associations that meet the conditions. This dynamic update mechanism ensures that dependencies are always consistent with the latest data state, providing a reliable basis for real-time operation and maintenance decisions. For example, in troubleshooting scenarios, operation and maintenance personnel can be certain that the application-database dependencies they are viewing are completely accurate, avoiding erroneous judgments based on outdated data. Furthermore, this automated maintenance also significantly reduces the long-term operation and maintenance costs of the system, making the data management process more intelligent and sustainable.

[0078] This invention proposes a method for supporting unified dimensional display of multi-model data, comprising: defining multiple data models and defining at least one attribute for each data model; configuring model association rules, including constraint relationships between data models and attribute association rules based on the attribute; performing matching calculations on configuration items in the multiple data models based on the model association rules, automatically generating and storing parent-child dependency relationships between the configuration items; and generating and outputting a unified dimensional data view based on the stored parent-child dependency relationships in response to a view query request. By defining data model attributes, configuring model association rules, automatically generating parent-child dependency relationships, and displaying a unified view, a mechanism for integrating and visualizing multi-model data is constructed. This invention effectively breaks down data silos in traditional configuration management databases, realizes automatic association and unified display of cross-model data, significantly reduces data maintenance costs, and improves data integration and utilization efficiency and operation and maintenance management effectiveness.

[0079] Corresponding to the above-described method for supporting unified dimensional display of multi-model data, the present invention also provides an apparatus for supporting unified dimensional display of multi-model data. This apparatus includes a unit for executing the above-described method for supporting unified dimensional display of multi-model data, and can be configured in a terminal or server. Specifically, the apparatus for supporting unified dimensional display of multi-model data includes: A definition unit is used to define multiple data models and define at least one attribute for each data model; A configuration unit is used to configure model association rules, which include constraint relationships between data models and attribute association rules based on the attributes. The calculation unit is used to perform matching calculations on configuration items in multiple data models based on the model association rules, and automatically generate and store the parent-child dependency relationships between the configuration items; The generation unit is used to generate and output a unified dimension data view in response to a view query request, based on the stored parent-child dependency relationship.

[0080] In some preferred embodiments, the configuration model association rules include: Configure model constraints, which are used to define the association type and data correspondence rules between the source model and the target model; Configure attribute association rules, which are used to define the matching conditions between the attributes of the source model and the attributes of the target model.

[0081] In some preferred embodiments, the attribute association rules are defined by structured logical expressions; the logical expressions include logical operators and at least one conditional judgment unit, which is used to define the matching relationship between an attribute of the source model and an attribute of the target model.

[0082] In some preferred embodiments, the step of matching and calculating configuration items in multiple data models based on the model association rules, and automatically generating and storing parent-child dependency relationships between the configuration items, includes: The source and target configuration items to be matched are determined based on the model constraint relationships. Based on the matching conditions defined in the attribute association rules, determine whether the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association conditions; If the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association condition, then a parent-child dependency relationship is established between the source configuration item and the target configuration item.

[0083] In some preferred embodiments, the parent-child dependency relationship is stored in a relational data table, which includes at least a parent configuration item identifier field and a child configuration item identifier field.

[0084] In some preferred embodiments, the step of generating and outputting a unified dimension data view in response to a view query request, based on the stored parent-child dependency relationship, includes: Parse the view query request to obtain the target dimension identifier and the target configuration item identifier; Starting with the target configuration item identifier as the starting node, a recursive traversal is performed based on the parent-child dependency relationship to obtain all associated configuration items with direct or indirect relationships. Based on the target dimension identifier, filter out the configuration items belonging to the specified data model from the associated configuration items; The selected configuration items are combined according to their model type and dependencies to generate and output the unified dimension data view.

[0085] In some preferred embodiments, it further includes: The update unit is used to automatically trigger the matching calculation of the relevant configuration items and update the parent-child dependency relationship when any configuration item of the data model changes.

[0086] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned device and its units that support the unified dimensional display of multi-model data can be found in the corresponding descriptions in the foregoing method embodiments. For the sake of convenience and brevity, these details will not be repeated here.

[0087] The aforementioned device supporting unified dimensional display of multi-model data can be implemented as a computer program, which can, for example... Figure 4 It runs on the computer device shown.

[0088] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0089] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0090] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it enables the processor 502 to execute a method that supports a unified dimensional representation of multi-model data.

[0091] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0092] The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method that supports the unified dimensional display of multi-model data.

[0093] The network interface 505 is used for network communication with other devices. Those skilled in the art will understand that the above structure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. A specific computer device 500 may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0094] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps: Define multiple data models, and define at least one attribute for each data model; Configure model association rules, which include constraint relationships between data models and attribute association rules based on the attributes; Based on the model association rules, matching calculations are performed on configuration items in multiple data models to automatically generate and store parent-child dependency relationships between the configuration items. In response to a view query request, a unified dimension data view is generated and output based on the stored parent-child dependency relationship.

[0095] In some preferred embodiments, the configuration model association rules include: Configure model constraints, which are used to define the association type and data correspondence rules between the source model and the target model; Configure attribute association rules, which are used to define the matching conditions between the attributes of the source model and the attributes of the target model.

[0096] In some preferred embodiments, the attribute association rules are defined by structured logical expressions; the logical expressions include logical operators and at least one conditional judgment unit, which is used to define the matching relationship between an attribute of the source model and an attribute of the target model.

[0097] In some preferred embodiments, the step of matching and calculating configuration items in multiple data models based on the model association rules, and automatically generating and storing parent-child dependency relationships between the configuration items, includes: The source and target configuration items to be matched are determined based on the model constraint relationships. Based on the matching conditions defined in the attribute association rules, determine whether the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association conditions; If the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association condition, then a parent-child dependency relationship is established between the source configuration item and the target configuration item.

[0098] In some preferred embodiments, the parent-child dependency relationship is stored in a relational data table, which includes at least a parent configuration item identifier field and a child configuration item identifier field.

[0099] In some preferred embodiments, the step of generating and outputting a unified dimension data view in response to a view query request, based on the stored parent-child dependency relationship, includes: Parse the view query request to obtain the target dimension identifier and the target configuration item identifier; Starting with the target configuration item identifier as the starting node, a recursive traversal is performed based on the parent-child dependency relationship to obtain all associated configuration items with direct or indirect relationships. Based on the target dimension identifier, filter out the configuration items belonging to the specified data model from the associated configuration items; The selected configuration items are combined according to their model type and dependencies to generate and output the unified dimension data view.

[0100] In some preferred embodiments, the method further includes: When any configuration item of a data model changes, the matching calculation of the relevant configuration item is automatically triggered, and the parent-child dependency relationship is updated.

[0101] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0102] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0103] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program causes the processor to perform the following steps: Define multiple data models, and define at least one attribute for each data model; Configure model association rules, which include constraint relationships between data models and attribute association rules based on the attributes; Based on the model association rules, matching calculations are performed on configuration items in multiple data models to automatically generate and store parent-child dependency relationships between the configuration items. In response to a view query request, a unified dimension data view is generated and output based on the stored parent-child dependency relationship.

[0104] In some preferred embodiments, the configuration model association rules include: Configure model constraints, which are used to define the association type and data correspondence rules between the source model and the target model; Configure attribute association rules, which are used to define the matching conditions between the attributes of the source model and the attributes of the target model.

[0105] In some preferred embodiments, the attribute association rules are defined by structured logical expressions; the logical expressions include logical operators and at least one conditional judgment unit, which is used to define the matching relationship between an attribute of the source model and an attribute of the target model.

[0106] In some preferred embodiments, the step of matching and calculating configuration items in multiple data models based on the model association rules, and automatically generating and storing parent-child dependency relationships between the configuration items, includes: The source and target configuration items to be matched are determined based on the model constraint relationships. Based on the matching conditions defined in the attribute association rules, determine whether the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association conditions; If the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association condition, then a parent-child dependency relationship is established between the source configuration item and the target configuration item.

[0107] In some preferred embodiments, the parent-child dependency relationship is stored in a relational data table, which includes at least a parent configuration item identifier field and a child configuration item identifier field.

[0108] In some preferred embodiments, the step of generating and outputting a unified dimension data view in response to a view query request, based on the stored parent-child dependency relationship, includes: Parse the view query request to obtain the target dimension identifier and the target configuration item identifier; Starting with the target configuration item identifier as the starting node, a recursive traversal is performed based on the parent-child dependency relationship to obtain all associated configuration items with direct or indirect relationships. Based on the target dimension identifier, filter out the configuration items belonging to the specified data model from the associated configuration items; The selected configuration items are combined according to their model type and dependencies to generate and output the unified dimension data view.

[0109] In some preferred embodiments, the method further includes: When any configuration item of a data model changes, the matching calculation of the relevant configuration item is automatically triggered, and the parent-child dependency relationship is updated.

[0110] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0111] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0112] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0113] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0115] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0116] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0117] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for supporting unified dimensional display of multi-model data, characterized in that, include: Define multiple data models, and define at least one attribute for each data model; Configure model association rules, which include constraint relationships between data models and attribute association rules based on the attributes; Based on the model association rules, matching calculations are performed on configuration items in multiple data models to automatically generate and store parent-child dependency relationships between the configuration items. In response to a view query request, a unified dimension data view is generated and output based on the stored parent-child dependency relationship.

2. The method for supporting unified dimensional display of multi-model data according to claim 1, characterized in that, The configuration model association rules include: Configure model constraints, which are used to define the association type and data correspondence rules between the source model and the target model; Configure attribute association rules, which are used to define the matching conditions between attributes of the source model and attributes of the target model.

3. The method for supporting unified dimensional display of multi-model data according to claim 2, characterized in that, The attribute association rules are defined through structured logical expressions; the logical expressions include logical operators and at least one conditional judgment unit, which is used to define the matching relationship between an attribute of the source model and an attribute of the target model.

4. The method for supporting unified dimensional display of multi-model data according to claim 2, characterized in that, The process of matching and calculating configuration items in multiple data models based on the model association rules, automatically generating and storing parent-child dependency relationships between the configuration items, includes: The source and target configuration items to be matched are determined based on the model constraint relationships. Based on the matching conditions defined in the attribute association rules, determine whether the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association conditions; If the attribute value of the source configuration item and the attribute value of the target configuration item satisfy the association condition, then a parent-child dependency relationship is established between the source configuration item and the target configuration item.

5. The method for supporting unified dimensional display of multi-model data according to claim 4, characterized in that, The parent-child dependency relationship is stored in a relational data table, which contains at least a parent configuration item identifier field and a child configuration item identifier field.

6. The method for supporting unified dimensional display of multi-model data according to claim 1, characterized in that, In response to a view query request, based on the stored parent-child dependency relationship, a unified dimension data view is generated and output, including: Parse the view query request to obtain the target dimension identifier and the target configuration item identifier; Starting with the target configuration item identifier as the starting node, a recursive traversal is performed based on the parent-child dependency relationship to obtain all associated configuration items with direct or indirect relationships. Based on the target dimension identifier, filter out the configuration items belonging to the specified data model from the associated configuration items; The selected configuration items are combined according to their model type and dependencies to generate and output the unified dimension data view.

7. The method for supporting unified dimensional display of multi-model data according to claim 1, characterized in that, The method further includes: When any configuration item of a data model changes, the matching calculation of the relevant configuration item is automatically triggered, and the parent-child dependency relationship is updated.

8. A device that supports unified dimensional display of multi-model data, characterized in that, Includes a unit for performing the method as described in any one of claims 1-7.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1-7.

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