A model-driven IT resource full life cycle management system and method

Abstract

This invention relates to the field of computer software technology, specifically to a model-driven IT resource lifecycle management system and method. The method includes: hierarchically grouping resource models to build a standardized IT resource model system; creating IT resource instances based on the standardized IT resource models; managing resource instance-level relationships according to model association rules, combining manual configuration and automatic identification, and constructing two types of topology graphs; optimizing and building a full-text search engine based on MongoDB document indexes; integrating multi-dimensional data of resource instances and collecting asset performance data in real time. The system includes: a standardized construction module, a resource instance creation module, an instance association module, a permission-based search module, and a data integration and linkage monitoring module. This achieves standardized management and precise positioning of IT resources, improves operational efficiency, provides reliable data support for enterprise IT management, and reduces management costs.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, and in particular to a model-driven IT resource lifecycle management system and method. Background Technology

[0002] As enterprises undergo profound digital transformation, IT architectures across industries are becoming increasingly complex. IT resources cover a wide range of types, including physical servers, network devices, databases, middleware, cloud servers, and containers, and their scale is growing explosively, becoming the core support for enterprise business operations. Currently, most enterprises manage their IT resources by manually entering and conducting offline inventory checks to record resource information, with each business department managing its own resources in a decentralized manner.

[0003] During the current digital transformation process, enterprises have large-scale and complex IT resources. Traditional management models rely heavily on manual recording and decentralized management, resulting in problems such as inconsistent resource information, unclear relationships, and lack of lifecycle control. This leads to low operation and maintenance efficiency, difficulty in fault location, and lack of data support for architecture planning. At the same time, existing resource management tools often have rigid models and poor scalability, making it difficult to adapt to the rapidly changing business needs of enterprises, especially in terms of complex field management, visualization of relationships, and efficient retrieval.

[0004] In conclusion, a model-driven approach to IT resource lifecycle management is essential for providing standardized management and precise location of IT resources, improving operational efficiency, providing reliable data support for enterprise IT management, and reducing management costs. Summary of the Invention

[0005] The purpose of this invention is to provide a model-driven IT resource lifecycle management system and method, which achieves standardized management and precise positioning of IT resources, improves operational efficiency, provides reliable data support for enterprise IT management, and reduces management costs.

[0006] To achieve the above objectives, this invention employs a model-driven IT resource lifecycle management method, comprising the following steps: Implement hierarchical grouping of resource models, customize basic model attributes and fine-grained field constraints, configure multi-type association rules between models, and build a standardized IT resource model system; Based on a standardized IT resource model, create IT resource instances and perform full lifecycle status marking and change operation tracking management for these instances; Based on the model association rules, resource instance-level association management is achieved by combining manual configuration and automatic recognition, two types of topology graphs are constructed, and abnormal instances are highlighted. A full-text search engine was built based on MongoDB document index optimization, enabling multi-type search functions and performing permission verification during the search process. Integrate comprehensive data from resource instances, collect asset performance data in real time, and issue anomaly alerts to build a coordinated monitoring system for assets, applications, and data.

[0007] Among the steps involved in implementing hierarchical grouping of resource models, customizing basic model attributes and refined field constraints, configuring multi-type association rules between models, and building a standardized IT resource model system: Build a parent-child hierarchical model grouping structure, classify resource models according to IT resource type, and realize hierarchical management of different types of resource models; Configure basic attributes such as name, code, and description for resource models at each level. The system automatically generates unique identifiers for models and supports model sorting adjustments. Configure basic and complex types of fields for the resource model, and set fine-grained constraints for each type of field, including mandatory field rules, numerical range, regular expression validation, and field linkage. Based on the business relationship logic of IT resources, configure one-to-one, one-to-many, and many-to-many relationship types between models, customize the relationship name, description, and scope of effect, and realize the linkage update of relationship and model.

[0008] Among these steps, the creation of IT resource instances based on a standardized IT resource model, and the full lifecycle status marking and change operation traceability management of these instances are as follows: It provides a visual input interface, allowing users to manually fill in resource information according to the field requirements of the standardized model, and complete the creation of a single IT resource instance after data validity verification; Receive resource information tables in Excel format, automatically verify the matching between the data in the table and the constraints of the model fields, generate resource instances in batches for qualified data, and generate error logs with the reasons for unqualified data; Scan various IT assets within a specified network range, automatically identify the core information of asset models and configuration parameters, and match the corresponding standardized models to complete the automated creation of resource instances; Mark the entire lifecycle status of the created resource instance, which includes running, maintenance, idle and offline. When the status changes, fill in the remarks and record the person who changed the status and the time of the change.

[0009] Among them, in the steps of managing resource instance-level relationships based on model association rules, combining manual configuration and automatic recognition, constructing two types of topology graphs, and highlighting abnormal instances: Based on the association rules preset in the model layer, the business dependencies and connection relationships between resource instances are automatically identified, and the automatic configuration of instance-level association relationships is completed. It provides an entry point for instance association operations, supports quick location of target instances through fuzzy search, and allows manual addition, modification, and deletion of associations between instances; Based on the relationships between instances, an instance dependency topology graph and a connection relationship topology graph are automatically constructed, and instance names, core attributes and real-time status information are labeled on the topology nodes. Configure scaling, panning, and layout adjustment functions for the topology map.

[0010] Among the steps for configuring scaling, panning, and layout adjustment functions for the topology map: When an abnormal instance status is detected, the abnormal instance node and its corresponding associated dependency link are automatically highlighted in the topology map.

[0011] Among them, in the steps of building a full-text search engine based on MongoDB document index optimization, performing multi-type search functions, and performing permission verification during the search: Optimize document indexing for resource instance data in MongoDB databases, and create dedicated indexes for core fields and complex tabular fields respectively; Develop keyword fuzzy search, field name + keyword precise search, and multi-condition combination search functions in the full-text search engine, and realize in-depth subfield search of complex table-type fields. After a search request is initiated, the search scope is automatically verified based on the user's role and permission configuration, filtering out resource instance data that the user does not have access to; The system provides sorting functionality for search results after permission verification, based on matching degree, creation time, and update time. It also retains users' search history and supports the rapid reuse of search criteria.

[0012] Among the steps involved in integrating comprehensive data from resource instances, collecting asset performance data in real time, issuing anomaly alerts, and building a coordinated monitoring system for assets, applications, and data: The basic information, performance data, runtime load information, full lifecycle status information and all operation logs of resource instances are integrated and stored in a unified data platform to form a complete resource data archive. Real-time collection and synchronization of data from servers, databases and middleware in IT assets, including performance data such as CPU utilization, memory usage, disk I / O and network bandwidth. Preset abnormal thresholds for performance metrics; Associate the asset performance status with the data layer load of the applications running on the asset and the associated databases.

[0013] In the step of setting an abnormal threshold for performance metrics: When an indicator exceeds a threshold, an alarm is triggered immediately, and the alarm information and trigger time are recorded. The alarm methods include system pop-ups and message pushes.

[0014] This invention also provides a model-driven IT resource lifecycle management system, including a standardized construction module, a resource instantiation and creation module, an instance association module, a permission-based retrieval module, and a data integration and linkage monitoring module; wherein: The standardized building module is used to hierarchically group resource models, customize basic model attributes and fine-grained field constraints, configure multi-type association rules between models, and build a standardized IT resource model system. The resource instantiation creation module is used to create IT resource instances based on a standardized IT resource model, and to perform full lifecycle status marking and full-process trace management of change operations for the instances. The instance association module is used to manage resource instance-level association relationships based on model association rules, combining manual configuration and automatic recognition, constructing two types of topology graphs, and highlighting abnormal instances. The permission-based retrieval module is used to build a full-text search engine based on MongoDB document index optimization, perform multi-type search functions, and perform permission verification during the search. The data integration and linkage monitoring module is used to integrate all-dimensional data of resource instances, collect asset performance data in real time, and issue anomaly alarms to build a linkage monitoring system for assets, applications, and data.

[0015] This invention discloses a model-driven IT resource lifecycle management system and method, which employs a standardized construction module, a resource instantiation creation module, an instance association module, a permission-based retrieval module, and a data integration and linkage monitoring module to perform the following steps: Hierarchical grouping of resource models, customization of basic model attributes and refined field constraints, configuration of multi-type association rules between models, and construction of a standardized IT resource model system; creation of IT resource instances based on the standardized IT resource models, and full lifecycle status marking and change operation traceability management for instances; management of resource instance-level association relationships based on model association rules, combining manual configuration and automatic identification, constructing two types of topology diagrams, and highlighting abnormal instances; optimization of a full-text search engine based on MongoDB document indexing, performing multi-type search functions, and performing permission verification during retrieval; integration of full-dimensional data of resource instances, real-time collection of asset performance data, and anomaly alerts, establishing a linkage monitoring system for assets, applications, and data. Through the above methods, standardized management and precise positioning of IT resources are achieved, improving operational efficiency, providing reliable data support for enterprise IT management, and reducing management costs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the steps of the model-driven IT resource lifecycle management method of the present invention.

[0018] Figure 2 This is a flowchart of steps S100 of the present invention.

[0019] Figure 3 This is a flowchart of steps S200 of the present invention.

[0020] Figure 4 This is a flowchart of steps S300 of the present invention.

[0021] Figure 5 This is a flowchart of steps S400 of the present invention.

[0022] Figure 6 This is a flowchart of steps S500 of the present invention.

[0023] Figure 7 This is a schematic diagram illustrating the principle of the model-driven IT resource lifecycle management system of the present invention.

[0024] 601 - Standardized construction module, 602 - Resource instantiation creation module, 603 - Instance association module, 604 - Permission-based retrieval module, 605 - Data integration and linkage monitoring module. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0026] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0027] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0028] Please see Figures 1-6 This invention provides a model-driven method for full lifecycle management of IT resources, comprising the following steps: S100: Implement hierarchical grouping of resource models, customize basic model attributes and fine-grained field constraints, configure multi-type association rules between models, and build a standardized IT resource model system.

[0029] In this implementation, the resource model is hierarchically grouped, basic model attributes and refined field constraints are customized, multi-type association rules between models are configured, and a standardized IT resource model system is built. The specific process is as follows: S101: Build a parent-child hierarchical model grouping structure, classify and divide resource models according to IT resource types, and realize hierarchical management of different types of resource models; S102: Configure basic attributes such as name, code and description for resource models at each level. The system automatically generates unique identifiers for the models and supports model sorting adjustments. S103: Configure basic and complex type fields for the resource model, and set fine-grained constraints for each type of field, including mandatory field rules, numerical range, regular expression validation, and field linkage. S104: Based on the business relationship logic of IT resources, configure one-to-one, one-to-many, and many-to-many relationship types between models, customize the relationship name, description, and scope of effect, and realize the linkage update of relationship and model.

[0030] In the above process, a parent-child hierarchical model grouping structure is built, and resource models are classified and hierarchically divided according to IT resource types to achieve hierarchical management of different types of resource models. Then, basic attributes such as name, code, and description are configured for resource models at each level. The system automatically generates unique identifiers for models and supports model sorting adjustments. Next, basic and complex type fields are configured for resource models, and fine-grained constraints such as mandatory rules, numerical ranges, regular expression validation, and field linkage are set for each type of field. Then, according to the business association logic of IT resources, one-to-one, one-to-many, and many-to-many association types between models are configured, and the association name, description, and effective scope are customized to achieve linkage updates between association relationships and models.

[0031] S200: Based on a standardized IT resource model, create IT resource instances and perform full lifecycle status marking and change operation traceability management for the instances.

[0032] In this implementation, based on a standardized IT resource model, IT resource instances are created, and full lifecycle status marking and change operation tracking management are implemented for these instances. The specific process is as follows: S201: Provides a visual input interface, allowing users to manually fill in resource information according to the field requirements of the standardized model, and complete the creation of a single IT resource instance after data validity verification; S202: Receive resource information table in Excel format, automatically verify the matching between the data in the table and the constraints of the model fields, generate resource instances in batches for qualified data, and generate error logs and indicate the reasons for unqualified data; S203: Scan various IT assets within a specified network range, automatically identify the core information of asset models and configuration parameters, and match the corresponding standardized models to complete the automated creation of resource instances; S204: Mark the full lifecycle status of the created resource instance, which includes running, maintenance, idle and offline. When the status changes, fill in the remarks and record the person who changed the status and the time of the change.

[0033] In the above process, a visual input interface is provided, allowing users to manually fill in resource information according to the field requirements of the standardized model. After data validity verification, a single IT resource instance is created. By receiving resource information tables in Excel format, the system automatically verifies the matching between the data in the table and the model field constraints. For qualified data, resource instances are generated in batches, while for unqualified data, error logs are generated with the reasons noted. The system also scans various IT assets within a specified network range, automatically identifies the core information of asset models and configuration parameters, and matches them with the corresponding standardized models to complete the automated creation of resource instances. Finally, the system marks the full lifecycle status of the created resource instances, including running, under maintenance, idle, and offline, and fills in remarks when the status changes, recording the person making the change and the time of the change.

[0034] S300: Based on model association rules, it combines manual configuration and automatic recognition to achieve resource instance-level association management, constructs two types of topology graphs, and highlights abnormal instances.

[0035] In this implementation, resource instance-level relationship management is achieved based on model association rules, combining manual configuration and automatic identification. Two types of topology graphs are constructed, and abnormal instances are highlighted. The specific process is as follows: S301: Based on the association rules preset in the model layer, automatically identify the business dependencies and connection relationships between resource instances and complete the automatic configuration of instance-level association relationships; S302: Provides an entry point for instance association operations, supporting quick location of target instances through fuzzy search, and manual addition, modification, and deletion of associations between instances; S303: Based on the relationships between instances, automatically construct instance dependency topology graphs and connection relationship topology graphs, and label the instance names, core attributes and real-time status information on the topology nodes; S304: Configure scaling, panning, and layout adjustment functions for the topology map. When an abnormal instance status is detected, the abnormal instance node and its corresponding associated dependency link will be automatically highlighted in the topology map.

[0036] In the above process, based on the association rules preset in the model layer, the business dependencies and connection relationships between resource instances are automatically identified, and the automatic configuration of instance-level association relationships is completed. An entry point for instance association operations is provided, supporting quick location of target instances through fuzzy search, and manual addition, modification, and deletion of association relationships between instances. Then, based on the association relationships between instances, an instance dependency topology graph and a connection relationship topology graph are automatically constructed, and instance names, core attributes, and real-time status information are marked on the topology nodes. The topology graph is configured with scaling, panning, and layout adjustment functions. When an instance status abnormality is detected, the abnormal instance node and its corresponding association dependency link are automatically highlighted in the topology graph.

[0037] S400: A full-text search engine built on MongoDB document index optimization, enabling multi-type search functions and performing permission verification during the search process.

[0038] In this implementation, a full-text search engine is built based on MongoDB document index optimization to perform multi-type search functions and perform permission verification during the search. The specific process is as follows: S401: Optimize document indexing for resource instance data in the MongoDB database, and create dedicated indexes for core fields and complex tabular fields respectively; S402: Develop keyword fuzzy search, field name + keyword precise search, and multi-condition combination search functions in the full-text search engine, and realize in-depth search of subfields of complex table-type fields. S403: After a search request is initiated, the search scope is automatically verified based on the user's role and permission configuration, and resource instance data that the user does not have access to is filtered out; S404: Provides sorting functions for search results after permission verification by matching degree, creation time, and update time, while retaining the user's search history and supporting the rapid reuse of search conditions.

[0039] In the above process, document indexing of resource instance data in the MongoDB database was optimized, with dedicated indexes created for core fields and complex tabular fields to improve retrieval response speed. Keyword fuzzy search, precise search using field name + keyword, and multi-condition combination search functions were developed in the full-text search engine, while deep sub-field search of complex tabular fields was implemented. After a search request is initiated, the search scope is automatically verified based on user roles and permission configurations, filtering out resource instance data for which the user has no access rights. The search results after permission verification are sorted by matching degree, creation time, and update time, while retaining user search history and supporting rapid reuse of search conditions.

[0040] S500: Integrates full-dimensional data of resource instances, collects asset performance data in real time, and issues anomaly alerts to build a linkage monitoring system for assets, applications, and data.

[0041] In this implementation, comprehensive data from resource instances is integrated, asset performance data is collected in real time, and anomaly alerts are issued to establish a coordinated monitoring system for assets, applications, and data. The specific process is as follows: S501: Integrates the basic information of resource instances, performance data, runtime load information, full lifecycle status information and all operation logs into a unified data platform to form a complete resource data archive. S502: Real-time collection and synchronization of data from servers, databases and middleware in IT assets, including performance data such as CPU utilization, memory usage, disk I / O and network bandwidth. S503: Preset abnormal thresholds for performance indicators. When an indicator exceeds the threshold, an alarm is triggered immediately, and the alarm information and trigger time are recorded. The alarm methods include system pop-ups and message pushes. S504: Associates the asset performance status with the data layer load of the applications running on the asset and the associated databases.

[0042] In the above process, the basic information, performance data, runtime load information, full lifecycle status information, and all operation logs of resource instances are integrated and stored in a unified data platform to form a complete resource data archive. Real-time data collection and synchronization updates are performed on servers, databases, and middleware within IT assets. The collected performance data includes CPU utilization, memory usage, disk I / O, and network bandwidth. Preset abnormal thresholds for performance metrics; when a metric exceeds the threshold, an alarm is triggered immediately, and the alarm information and trigger time are recorded. Alarm methods include system pop-ups and message pushes. Furthermore, the asset performance status is correlated with the data layer load of applications running on the asset and associated databases.

[0043] Please see Figure 7 This invention provides a model-driven IT resource lifecycle management system, including a standardized construction module 601, a resource instantiation creation module 602, an instance association module 603, a permission-based retrieval module 604, and a data integration and linkage monitoring module 605; wherein: The standardized construction module 601 is used to hierarchically group resource models, customize basic model attributes and fine-grained field constraints, configure multi-type association rules between models, and build a standardized IT resource model system. The resource instantiation creation module 602 is used to create IT resource instances based on a standardized IT resource model, and to perform full lifecycle status marking and full-process trace management of change operations for the instances. The instance association module 603 is used to manage resource instance-level association relationships based on model association rules, combining manual configuration and automatic recognition, constructing two types of topology graphs, and highlighting abnormal instances. The permission-based retrieval module 604 is used to build a full-text retrieval engine based on MongoDB document index optimization, perform multi-type retrieval functions, and perform permission verification during retrieval. The data integration and linkage monitoring module 605 is used to integrate all-dimensional data of resource instances, collect asset performance data in real time, and issue anomaly alarms to build a linkage monitoring system for assets, applications and data.

[0044] In this embodiment, the standardization construction module 601 hierarchically groups the resource models, customizes basic model attributes and refined field constraints, configures multi-type association rules between models, and builds a standardized IT resource model system. Based on the standardized IT resource model, the resource instantiation creation module 602 creates IT resource instances and performs full lifecycle status marking and full-process traceability management of instance changes. According to the model association rules, the instance association module 603 combines manual configuration and automatic identification to achieve resource instance-level association management, constructs two types of topology graphs, and highlights abnormal instances. Based on MongoDB document index optimization, a full-text search engine is built, and the permission-based search module 604 performs multi-type search functions and performs permission verification during the search. The data integration and linkage monitoring module 605 integrates full-dimensional data of resource instances, collects asset performance data in real time, and issues anomaly alarms to build a linkage monitoring system for assets, applications, and data.

[0045] Beneficial effects: Intelligent asset discovery reduces labor costs: Breaking through the limitations of traditional manual data entry and inventory, it supports automatic scanning of IT assets (servers, network devices, databases, middleware, etc.) within the network, automatically identifying core information such as asset models and configuration parameters, and completing instance creation. Asset discovery efficiency is improved by more than 80%, reducing manual data entry workload by 90%, and avoiding data deviations caused by human error.

[0046] Real-time status monitoring and accurate fault early warning: Real-time collection of asset performance data (CPU utilization, memory usage, disk I / O, network bandwidth, etc.) and operating status, automatic marking of abnormal thresholds, and immediate triggering of alarms when indicators exceed the set range; at the same time, it is associated with the upper-level loads such as applications and databases running on the assets, forming a linkage monitoring of "asset-application-data", transforming fault location from "post-event investigation" to "pre-event early warning", and reducing the average fault response time by 70%.

[0047] Visualizing dependencies improves operational efficiency: The system automatically identifies the dependency links between assets, applications, databases, and middleware, generating a visual topology map that clearly presents the hierarchical relationship between "server-middleware-application-database". When a node malfunctions, the system automatically highlights the problematic node and its associated dependency links, intuitively showing the scope of the fault's impact. This solves the dependency black hole problem of "a single problem affecting the whole system" in traditional operations and maintenance, reducing fault diagnosis time from hours to minutes.

[0048] Integrated data management provides strong decision support: It integrates basic asset information, performance data, operating load, and lifecycle status into a unified platform to form a complete asset data archive. It supports multi-dimensional statistical analysis by business line, department, region, etc., and provides data support for decisions such as resource expansion, architecture optimization, and asset disposal, avoiding resource idleness and waste, and reducing IT operating costs by more than 30%.

[0049] Flexible and adaptable to meet diverse needs: Based on a model-driven architecture, it supports rapid model configuration when adding new asset types (such as virtualized resources, cloud servers, containers, etc.) without modifying the core code; it is compatible with mainstream operating systems, databases, middleware, and cloud platforms, and can flexibly adapt to the IT architectures of different industries such as government, finance, and the Internet. Its adaptability and scalability far exceed those of traditional fixed asset management systems.

[0050] 6. Automated compliance audit and enhanced risk control: Automatically records changes throughout the entire asset lifecycle (configuration modification, status change, load adjustment, etc.) to form an unalterable operation log; supports the generation of asset audit reports in accordance with compliance requirements, clearly defining key information such as asset ownership, operating status, and security configuration, meeting the requirements of information security compliance and internal control audit, and reducing compliance risks and audit costs.

[0051] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0052] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A model-driven method for full lifecycle management of IT resources, characterized in that, Includes the following steps: Implement hierarchical grouping of resource models, customize basic model attributes and fine-grained field constraints, configure multi-type association rules between models, and build a standardized IT resource model system; Based on a standardized IT resource model, create IT resource instances and perform full lifecycle status marking and change operation tracking management for these instances; Based on the model association rules, resource instance-level association management is achieved by combining manual configuration and automatic recognition, two types of topology graphs are constructed, and abnormal instances are highlighted. A full-text search engine was built based on MongoDB document index optimization, enabling multi-type search functions and performing permission verification during the search process. Integrate comprehensive data from resource instances, collect asset performance data in real time, and issue anomaly alerts to build a coordinated monitoring system for assets, applications, and data.

2. The model-driven IT resource lifecycle management method as described in claim 1, characterized in that, In the steps of implementing hierarchical grouping of resource models, customizing basic model attributes and refined field constraints, configuring multi-type association rules between models, and building a standardized IT resource model system: Build a parent-child hierarchical model grouping structure, classify resource models according to IT resource type, and realize hierarchical management of different types of resource models; Configure basic attributes such as name, code, and description for resource models at each level. The system automatically generates unique identifiers for models and supports model sorting adjustments. Configure basic and complex types of fields for the resource model, and set fine-grained constraints for each type of field, including mandatory field rules, numerical range, regular expression validation, and field linkage. Based on the business relationship logic of IT resources, configure one-to-one, one-to-many, and many-to-many relationship types between models, customize the relationship name, description, and scope of effect, and realize the linkage update of relationship and model.

3. The model-driven IT resource lifecycle management method as described in claim 1, characterized in that, In the process of creating IT resource instances based on a standardized IT resource model, and conducting full lifecycle status marking and change operation traceability management for these instances: It provides a visual input interface, allowing users to manually fill in resource information according to the field requirements of the standardized model, and complete the creation of a single IT resource instance after data validity verification; Receive resource information tables in Excel format, automatically verify the matching between the data in the table and the constraints of the model fields, generate resource instances in batches for qualified data, and generate error logs with the reasons for unqualified data; Scan various IT assets within a specified network range, automatically identify the core information of asset models and configuration parameters, and match the corresponding standardized models to complete the automated creation of resource instances; Mark the entire lifecycle status of the created resource instance, which includes running, maintenance, idle and offline. When the status changes, fill in the remarks and record the person who changed the status and the time of the change.

4. The model-driven IT resource lifecycle management method as described in claim 1, characterized in that, In the steps of managing resource instance-level relationships based on model association rules, combining manual configuration and automatic recognition, constructing two types of topology graphs, and highlighting abnormal instances: Based on the association rules preset in the model layer, the business dependencies and connection relationships between resource instances are automatically identified, and the automatic configuration of instance-level association relationships is completed. It provides an entry point for instance association operations, supports quick location of target instances through fuzzy search, and allows manual addition, modification, and deletion of associations between instances; Based on the relationships between instances, an instance dependency topology graph and a connection relationship topology graph are automatically constructed, and instance names, core attributes and real-time status information are labeled on the topology nodes. Configure scaling, panning, and layout adjustment functions for the topology map.

5. The model-driven IT resource lifecycle management method as described in claim 4, characterized in that, In the steps of configuring scaling, panning, and layout adjustment functions for the topology map: When an abnormal instance status is detected, the abnormal instance node and its corresponding associated dependency link are automatically highlighted in the topology map.

6. The model-driven IT resource lifecycle management method as described in claim 1, characterized in that, In the process of building a full-text search engine based on MongoDB document index optimization, implementing multi-type search functions, and performing permission verification during the search: Optimize document indexing for resource instance data in MongoDB databases, and create dedicated indexes for core fields and complex tabular fields respectively; Develop keyword fuzzy search, field name + keyword precise search, and multi-condition combination search functions in the full-text search engine, and realize in-depth subfield search of complex table-type fields. After a search request is initiated, the search scope is automatically verified based on the user's role and permission configuration, filtering out resource instance data that the user does not have access to; The system provides sorting functionality for search results after permission verification, based on matching degree, creation time, and update time. It also retains users' search history and supports the rapid reuse of search criteria.

7. The model-driven IT resource lifecycle management method as described in claim 1, characterized in that, In the process of integrating comprehensive data from resource instances, collecting asset performance data in real time, issuing anomaly alerts, and building a coordinated monitoring system for assets, applications, and data: The basic information, performance data, runtime load information, full lifecycle status information and all operation logs of resource instances are integrated and stored in a unified data platform to form a complete resource data archive. Real-time collection and synchronization of data from servers, databases and middleware in IT assets, including performance data such as CPU utilization, memory usage, disk I / O and network bandwidth. Preset abnormal thresholds for performance metrics; Associate the asset performance status with the data layer load of the applications running on the asset and the associated databases.

8. The model-driven IT resource lifecycle management method as described in claim 7, characterized in that, In the step of setting an outlier threshold for performance metrics: When an indicator exceeds a threshold, an alarm is triggered immediately, and the alarm information and trigger time are recorded. The alarm methods include system pop-ups and message pushes.

9. A model-driven IT resource lifecycle management system, applied to the model-driven IT resource lifecycle management method as described in claim 1, characterized in that, It includes a standardized construction module, a resource instantiation and creation module, an instance association module, a permission-based retrieval module, and a data integration and linkage monitoring module; among which: The standardized building module is used to hierarchically group resource models, customize basic model attributes and fine-grained field constraints, configure multi-type association rules between models, and build a standardized IT resource model system. The resource instantiation creation module is used to create IT resource instances based on a standardized IT resource model, and to perform full lifecycle status marking and full-process trace management of change operations for the instances. The instance association module is used to manage resource instance-level association relationships based on model association rules, combining manual configuration and automatic recognition, constructing two types of topology graphs, and highlighting abnormal instances. The permission-based retrieval module is used to build a full-text search engine based on MongoDB document index optimization, perform multi-type search functions, and perform permission verification during the search. The data integration and linkage monitoring module is used to integrate all-dimensional data of resource instances, collect asset performance data in real time, and issue anomaly alarms to build a linkage monitoring system for assets, applications, and data.

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