Internet self-research operation and maintenance management method, device and storage medium

Abstract

The application discloses an internet self-research operation and maintenance management method, device and storage medium, comprising: analyzing current operation and maintenance management requirements, and determining at least one target instance according to the analysis result; extracting the associated foreign key of the target instance, and dynamically associating the target instance based on the associated foreign key to form a target entity set; obtaining business configuration information based on the target entity set, updating the dynamic key-value pair configuration of the target entity set according to the business configuration information; generating a task flow arrangement template based on the updated target entity set, and scheduling a task object to execute a task chain through the task flow arrangement template. The application establishes a multi-level configuration management architecture and a templated task chain mechanism, supports environment adaptive differentiated execution after automatically merging multi-level configuration parameters, improves operation and maintenance efficiency and full-link execution monitoring and auditing traceability capability, and reduces the risk of manual operation errors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, in particular to an Internet self-research operation and maintenance management method, device and storage medium. BACKGROUND

[0002] At present, multi-cloud architecture has become mainstream, and a single large project often needs to be deployed and run across multiple cloud platforms, involving dozens of different types of service components. The operation and maintenance interfaces, configuration specifications and deployment methods provided by each cloud platform are significantly different, and this heterogeneous environment brings great difficulties to operation and maintenance.

[0003] Although traditional operation and maintenance methods have introduced Jenkins (a continuous integration and continuous deployment tool), Ansible (an open source configuration management, application deployment and task automation tool), etc. basic automation tools, there are obvious deficiencies in configuration management. The main problem is the lack of a unified configuration management system, which leads to the dispersion of parameter configurations in different environments in various independent configuration files, and cannot guarantee the consistency and integrity of the configuration. In practical application, operation and maintenance personnel need to manually maintain the configuration files of multiple environments such as development, testing and production, which not only increases the complexity of operation and maintenance, but also is prone to inconsistent or missing configurations. With the expansion of the project scale, the number of configuration items grows exponentially, and the complexity of configuration management has exceeded the capacity of manual processing, seriously affecting the efficiency and quality of operation and maintenance.

[0004] The above content is only used to assist in understanding the technical solutions of the present application, and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0005] The main purpose of the present application is to provide an Internet self-research operation and maintenance management method, device and storage medium, which aims to solve the technical problems that the existing operation and maintenance technology lacks a unified configuration management system and environment differentiation support capability, and cannot effectively realize the hierarchical management of configuration and the automatic arrangement of task execution.

[0006] To achieve the above purpose, the present application provides an Internet self-research operation and maintenance management method, which comprises:

[0007] Analyzing the current operation and maintenance management requirements, and determining to select at least one target instance according to the analysis result, the target instance including a project instance, a platform instance and a service instance;

[0008] Extracting the associated foreign key of the target instance, and dynamically associating the target instance based on the associated foreign key to form a target entity set;

[0009] Obtaining business configuration information based on the target entity set, and updating the dynamic key-value pair configuration of the target entity set according to the business configuration information;

[0010] generate a task flow orchestration template based on the updated target entity set, and schedule task objects to execute a task chain through the task flow orchestration template.

[0011] In an embodiment, the extracting the associated foreign keys of the target instances, and dynamically associating the target instances based on the associated foreign keys to form a target entity set comprises:

[0012] obtaining the association relationship between the target instances, and extracting the foreign key references pointing to the primary entity identifiers in the association relationship;

[0013] dynamically constructing the combination relationship between the target instances according to the foreign key references, and encapsulating the target instances into the target entity set based on the combination relationship.

[0014] In an embodiment, the obtaining the business configuration information based on the target entity set, and updating the dynamic key-value pair configuration of the target entity set according to the business configuration information comprises:

[0015] determining the business configuration items with key name conflicts in the business configuration information, and retaining the business configuration items with high priority;

[0016] integrating the retained business configuration items to generate a configuration parameter set, and updating the dynamic key-value pair configuration of the target entity set with the configuration parameter set.

[0017] In an embodiment, the generating a task flow orchestration template based on the updated target entity set comprises:

[0018] extracting the configuration parameters in the target entity set, and replacing the parameter placeholders of the task functions with the configuration parameters;

[0019] verifying the parameter dependency relationship between the task functions, and determining the task execution order based on the parameter dependency relationship;

[0020] instantiating the task functions, and assembling the instantiated task functions into an executable task chain in the task execution order.

[0021] In an embodiment, the instantiating the task functions, and assembling the instantiated task functions into an executable task chain in the task execution order comprises:

[0022] detecting the task parameter transmission matching between the adjacent task functions in the task chain;

[0023] if the task parameters are detected to be unmatched, obtaining the parameter conversion function between the adjacent task functions based on the parameter mapping rule;

[0024] The parameter conversion function is inserted between adjacent task parameters to form the task chain.

[0025] In an embodiment, the step of scheduling a task object to execute the task chain through the task flow orchestration template comprises:

[0026] The task chain is parsed into a set of asynchronous execution task units, and an execution topology graph is constructed based on the dependency relationship of each task unit in the set of task units.

[0027] The task object is scheduled to execute the task chain according to the execution topology graph.

[0028] In an embodiment, after the step of scheduling a task object to execute the task chain through the task flow orchestration template, the method further comprises:

[0029] The execution status of each task object is monitored in real time.

[0030] If the task object fails to execute, a rollback operation of a related task object is triggered based on the dependency relationship of the task object.

[0031] In an embodiment, the internet self-developed operation and maintenance management method further comprises:

[0032] A business operation and maintenance task is obtained, and a template entity and an associated entity corresponding to the business operation and maintenance task are created.

[0033] A configuration storage structure of the template entity and the associated entity is added in an extension configuration layer, and a configuration merging algorithm is updated based on the configuration storage structure.

[0034] In addition, to achieve the above-mentioned purposes, the present application also proposes an internet self-developed operation and maintenance management device, which comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the internet self-developed operation and maintenance management method as described above.

[0035] In addition, to achieve the above-mentioned purposes, the present application also proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program, the computer program being executed by a processor to implement the steps of the internet self-developed operation and maintenance management method as described above.

[0036] The one or more technical solutions proposed by the present application have at least the following technical effects:

[0037] Analyzing current operation and maintenance management requirements, and determining to select at least one target instance according to the analysis result, the target instance includes a project instance, a platform instance and a service instance; extracting the associated foreign key of the target instance, and dynamically associating the target instance based on the associated foreign key to form a target entity set; obtaining business configuration information based on the target entity set, updating the dynamic key-value pair configuration of the target entity set according to the business configuration information; generating a task flow arrangement template based on the updated target entity set, and scheduling a task object to execute a task chain through the task flow arrangement template.

[0038] Therefore, by establishing a multi-level configuration management architecture and a template task chain mechanism, the application realizes the decoupling of configuration and execution, supports environment adaptive differentiated execution after automatically merging multi-level configuration parameters, significantly improves the operation and maintenance efficiency. At the same time, it provides full-link execution monitoring and audit tracing capability, reduces the risk of manual operation errors, and effectively guarantees the reliability and consistency of the operation and maintenance process. BRIEF DESCRIPTION OF DRAWINGS

[0039] The accompanying drawings incorporated in and forming a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0041] Figure 1 The flowchart of the first embodiment of the internet self-research operation and maintenance management method of the present application;

[0042] Figure 2 The flowchart of the second embodiment of the internet self-research operation and maintenance management method of the present application;

[0043] Figure 3 The flowchart of the third embodiment of the internet self-research operation and maintenance management method of the present application;

[0044] Figure 4 The device structure diagram of the hardware running environment involved in the internet self-research operation and maintenance management method in the embodiments of the present application.

[0045] The purpose of the present application, the functional characteristics and advantages will be further explained in conjunction with the embodiments, with reference to the drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0047] For better understanding of the technical solutions of the present application, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0048] The main solution of the embodiment of the present application is: analyzing the current operation and maintenance management demand, and determining to select at least one target instance according to the analysis result, the target instance includes project instance, platform instance and service instance; extracting the associated foreign key of the target instance, and dynamically associating the target instance based on the associated foreign key to form a target entity set; obtaining business configuration information based on the target entity set, updating the dynamic key-value pair configuration of the target entity set according to the business configuration information; generating a task flow arrangement template based on the updated target entity set, and scheduling a task object to execute a task chain through the task flow arrangement template.

[0049] Because the existing operation and maintenance technology lacks unified configuration management system and environment differentiation support capability, it cannot effectively realize hierarchical management of configuration and automatic arrangement of task execution.

[0050] The present application provides a solution, which realizes decoupling of configuration and execution by establishing a multi-level configuration management architecture and a templated task chain mechanism, supports environment adaptive differentiation execution after automatically merging multi-level configuration parameters, and significantly improves operation and maintenance efficiency. At the same time, it provides full-link execution monitoring and audit tracing capability, reduces the risk of manual operation error, and effectively guarantees the reliability and consistency of the operation and maintenance process.

[0051] Based on this, the embodiment of the present application provides an internet self-research operation and maintenance management method, referring to Figure 1 , Figure 1 The flowchart of the first embodiment of the internet self-research operation and maintenance management method of the present application. In this embodiment, the internet self-research operation and maintenance management method includes steps S10-S40:

[0052] Step S10, analyzing the current operation and maintenance management demand, and determining to select at least one target instance according to the analysis result, the target instance includes project instance, platform instance and service instance;

[0053] In this embodiment, the current operation and maintenance management requirements are processed by a requirement analysis engine, which uses a natural language understanding model based on deep learning. Specifically, the user inputs the operation and maintenance requirement text into the input text of the natural language understanding model and performs multi-level semantic analysis on the operation and maintenance requirement text. Specifically, lexical analysis is performed to segment the operation and maintenance requirement text into a sequence of lexical units, identify technical terms and entity names, and perform syntactic analysis to construct a dependency syntax tree to identify subject-predicate-object structures and modification relationships. Finally, semantic role labeling is performed to identify the semantic roles of the subject, object, time, location, and other semantic roles of the requirement action. The requirement analysis engine uses a bidirectional long short-term memory network combined with an attention mechanism to capture long-distance dependencies in the text and accurately extract operation and maintenance operation intentions.

[0054] In the entity recognition phase, a conditional random field model is used for named entity recognition to identify key entities such as project names, platform types, and service components in the operation and maintenance requirement text. During the identification process, the domain dictionary and synonym library are combined to handle term variants and abbreviations. For the identified entities, the metadata knowledge graph is queried for entity disambiguation and verification. The knowledge graph uses a graph database to store entity attributes and relationships, and uses a graph traversal algorithm to verify the legality and availability of the entities.

[0055] Finally, the parsed results obtained by the requirement analysis engine are generated into structured requirement description objects, which are packaged in JSON-LD format and include entity sets, operation types, execution contexts, and technical constraints.

[0056] Based on the parsed results, at least one target instance is selected, and a multi-objective optimization-based instance matching algorithm is executed. The instance matching algorithm retrieves a candidate instance set from the instance registration center, constructs an instance feature matrix, and the feature dimensions of the instance feature matrix include instance state, resource quota, performance indicators, geographic location, and cost factors. The matching process based on the instance matching algorithm uses the analytic hierarchy process to determine the weight of each feature dimension, and combines the TOPSIS method to calculate the closeness of each candidate instance to the ideal solution. Specifically, based on the project instance, the project phase matching degree and resource surplus are evaluated; based on the platform instance, the platform version compatibility and service level agreement compliance are investigated; based on the service instance, the service dependency satisfaction and load balancing status are analyzed.

[0057] In the specific implementation process, the multi-round screening mechanism is selected to select at least one target instance, the first round is based on hard constraints to filter instances that do not meet the basic requirements, the second round is based on soft constraints to calculate the fitness score of the instance, and the last round comprehensively considers the operation and maintenance strategy preference and the real-time system state to make a final decision. In the selection process based on the multi-round screening mechanism, the optimization strategy combining the greedy algorithm and the simulated annealing algorithm is adopted, so that the calculation complexity is controlled while the selection quality is ensured. The finally screened target instance set ensures the topological connectivity and optimal resource allocation between instances, and provides a reliable execution basis for subsequent operation and maintenance.

[0058] In another possible implementation, a blockchain-based instance reputation mechanism is introduced to evaluate the reliability of the screened target instance, providing an additional trust dimension for instance selection. In addition, an instance capacity prediction module is designed to predict the future resource utilization of the instance by combining time series analysis, realizing forward-looking instance selection decisions.

[0059] Step S20, extracting the associated foreign key of the target instance, and dynamically associating the target instance based on the associated foreign key to form a target entity set;

[0060] In this embodiment, the extraction component extracts the associated foreign key from the selected target instance metadata. The extraction component constructs the association relationship based on the graph database query of the target instance, so as to obtain the complete foreign key reference chain from the association relationship graph of the instance registry of the target instance.

[0061] The extraction process based on the foreign key reference chain adopts an improved breadth-first traversal algorithm, sets the maximum traversal depth from the node of the target instance, and collects all directly and indirectly associated foreign key values along the association edge. The algorithm maintains the access history record during execution, detects and processes the circular reference scenario, and avoids infinite recursion. For project instances, the project identifier, organizational structure path and tenant information are extracted; for platform instances, the platform type identifier, deployment area code and available area identifier are extracted; for service instances, the service template version hash, dependent service topology and interface contract identifier are extracted.

[0062] In the extraction process, a foreign key integrity verification mechanism is implemented, and the verification process based on the integrity verification mechanism checks whether the target instance of the foreign key reference exists and is available, and evaluates the timeliness of the association relationship. For invalid or expired foreign key references, an automatic repair process or manual intervention is performed. After the extraction is completed, a foreign key dependency graph is generated, and the strength weight and dependency type of each association are marked, providing an accurate topology basis for subsequent entity association.

[0063] When the target instances are dynamically associated based on the associated foreign keys to form a target entity set, a semantic-based entity relationship construction algorithm is implemented to analyze the semantic types of the foreign keys, identify different association modes such as combination relationship, aggregation relationship, and dependency relationship, and determine the coupling strength between the target instances according to the association modes. A dynamic proxy mode is used to create a lightweight proxy object for each target instance, the foreign key parser is used to realize the lazy loading and cache management of the associated target instances, an adjacency list storage structure is used to create an entity relationship network, and fast relationship traversal and subgraph extraction are supported.

[0064] In the entity set construction process, multi-dimensional relationship consistency verification is performed, and the verification content includes foreign key reference integrity, associated relationship acyclic property, entity state compatibility, and resource configuration consistency. In the case of discovering association conflicts, automatic repair or repair suggestions are generated based on predefined conflict resolution strategies. The completed target entity set is stored in an attribute graph model, the vertex represents an entity instance, the edge represents an associated relationship, and the edge attribute records the associated metadata information, supporting complex graph query and analysis operations.

[0065] In addition, a distributed entity association cache mechanism based on consistent hashing is used to store hot association relationships in multiple cache nodes, and the query performance is improved through read-write separation. At the same time, a space-time version management of the associated relationship is designed, and the valid time interval and version sequence of each associated relationship are maintained, supporting the historical state query and time travel function of the associated relationship. In addition, through the stream association update processing, the entity relationship change events are captured in real time, and the latest state of the entity set is dynamically maintained, ensuring the real-time accuracy of the associated relationship.

[0066] Specifically, the associated foreign keys of the target instances are extracted, and the target instances are dynamically associated based on the associated foreign keys to form a target entity set.

[0067] The associated relationships between the target instances are obtained, and the foreign key references pointing to the primary entity identifiers in the associated relationships are extracted.

[0068] The combination relationships between the target instances are dynamically constructed according to the foreign key references, and the target instances are encapsulated into the target entity set based on the combination relationships.

[0069] In this embodiment, the associated relationship extraction engine obtains the associated relationships between the target instances. The extraction engine identifies all direct and indirect association paths from the instance relationship graph based on the path query algorithm of the graph database. A multi-condition filtering strategy is used to filter the effective associated relationships according to the association type, relationship strength, and timeliness. For each identified associated relationship, the source instance, target instance, associated attribute, and metadata information are recorded to construct a complete associated relationship set.

[0070] In extracting the foreign key reference pointing to the primary entity identifier in the association relationship, the foreign key resolution algorithm analyzes the target endpoint of the association relationship and identifies the primary entity identifier reference contained therein. During the resolution process, foreign key validity verification is implemented, including checking the target instance existence, state availability, and permission compliance. For composite foreign key references, the system adopts a segmented resolution strategy to decompose the nested foreign key structure layer by layer. The extracted foreign key references are stored in categories according to entity types and association dimensions, providing basic data for subsequent composite relationship construction.

[0071] When dynamically constructing the composite relationship between the target instances according to the foreign key references, a graph-based composite relationship modeling method is used to analyze the topology between foreign key references and identify different types of composite patterns, including inclusion, aggregation, and dependency. According to the composite strength coefficient, the coupling degree between entities is determined, and a hierarchical composite relationship network is constructed. For the identified composite relationships, composite relationship descriptors are generated, recording attributes such as composite type, directionality, and lifecycle dependency.

[0072] When encapsulating the target instances into the target entity set based on the composite relationship, an entity set encapsulation algorithm is implemented to group the associated instances into logical entity sets according to the topology of the composite relationship, and assign each entity set a unique identifier and version number. During the encapsulation process, the internal consistency and external interface contract of the entity set are maintained to ensure the integrity and availability of the entity set as a whole unit.

[0073] In addition, a machine learning-based relationship strength prediction model is used to analyze historical association data and automatically optimize the construction strategy of composite relationships. At the same time, an association relationship caching mechanism is introduced to preload frequently accessed composite relationships into memory, improving the response performance of entity set encapsulation.

[0074] Step S30, obtaining business configuration information based on the target entity set, and updating the dynamic key-value pair configuration of the target entity set according to the business configuration information;

[0075] In this embodiment, the business configuration information based on the target entity set is obtained from the configuration management module through the configuration acquisition service. The acquisition process adopts a multi-level configuration merging algorithm to obtain configuration data from low to high according to the five-level configuration hierarchy.

[0076] The specific execution process of the multi-level configuration merging algorithm is as follows: first, obtain the platform global configuration as the basic configuration template, then superimpose the service-level parameters in the service global configuration, then merge the project-specific parameters in the project configuration, then apply the environment differentiation parameters in the project platform configuration, and finally load the instance-level parameters in the project platform service configuration. During the configuration merging process, a priority-based configuration overriding rule is adopted, where high-priority configuration items completely override low-priority configuration items, while preserving the underlying configurations that have not been overridden.

[0077] A depth-first configuration tree traversal strategy is adopted to establish a complete inheritance chain for each configuration item, and a configuration item source tracking record is maintained to record the source level and version information of each final configuration value. For configuration items with dependencies, topological sorting is performed to ensure that dependent configurations are processed first. A multi-level cache strategy is implemented to use long-term caching for stable global configurations and short-term caching for frequently changing instance-level configurations, and a cache invalidation mechanism is used to ensure the real-time nature of the configurations.

[0078] When updating the dynamic key-value pair configuration of the target entity set according to the business configuration information, a transaction-based configuration update process is executed. The update process based on the configuration update process uses a two-phase commit protocol to ensure the atomicity of configuration changes in a distributed environment. Specifically, first, the configuration storage area of the target entity set is locked in the preparation phase, and the legality of the configuration changes is verified; then, all configuration changes are applied in the commit phase. For key-value pair configurations, an incremental update algorithm is implemented to identify changed items by comparing the MD5 hash values of new and old configurations, and only write operations are performed on configuration items that have changed.

[0079] Explicit and implicit dependency relationships between configuration items are identified using configuration item dependency analysis to ensure the update order of dependent configurations. For sensitive configuration items, encrypted storage and access auditing are implemented. After the update is complete, the system triggers a configuration change event propagation mechanism to notify related components of the configuration change details through a publish-subscribe mode. A complete configuration version history is recorded to support time-based configuration snapshots and one-key rollback functions.

[0080] Further, a configuration sandbox verification mechanism is used to simulate the system behavior after the configuration takes effect in an independent environment, and configuration conflicts and performance impacts are detected through static analysis and dynamic testing. At the same time, configuration hot loading capability is implemented based on class loader isolation technology to enable runtime dynamic updating of configurations, ensuring the continuity of system services. In addition, a configuration drift detection mechanism is provided to detect and repair unexpected configuration changes in a timely manner through regular configuration consistency checks.

[0081] Specifically, the step of obtaining business configuration information based on the target entity set and updating the dynamic key-value pair configuration of the target entity set according to the business configuration information includes:

[0082] Determine the business configuration items with key name conflicts in the business configuration information, and retain the business configuration items with high priority;

[0083] Integrate the retained business configuration items to generate a configuration parameter set, and update the dynamic key-value pair configuration of the target entity set with the configuration parameter set.

[0084] In this embodiment, the key name conflict detection algorithm is implemented to determine the key name conflict service configuration items in the service configuration information. The key name conflict detection algorithm is based on the comparison of the complete namespace path of the configuration item, and identifies the configuration items with the same key name in different configuration levels. Hash index technology is used to quickly locate potential conflicts, and the data type, value range and business semantics of the configuration item are considered for conflict verification. For the detected key name conflict, the source level, timestamp and version information of the conflict item are recorded.

[0085] When high-priority service configuration items are retained, a configuration level-based priority arbitration rule is executed, which decides in the order of priority of project platform service configuration, project platform configuration, project configuration, service global configuration, and platform global configuration. A priority scoring model is established for each configuration item, considering factors such as the timeliness, business importance and modification frequency of the configuration source. A multi-dimensional evaluation algorithm is used to ensure the selection of the optimal configuration item in complex conflict scenarios.

[0086] Furthermore, a configuration item merging strategy is implemented to integrate the retained service configuration items to generate a configuration parameter set. The configuration item merging strategy constructs a configuration dependency graph based on the dependency relationship between the configuration items, and integrates the configuration items in the topological order of the configuration dependency graph. Semantic consistency check is performed on the integrated configuration parameter set to verify the logical relationship and numerical compatibility between the configuration items. The generated configuration parameter set is stored in a standardized data structure, which contains complete information such as configuration key name, configuration value, data type, source identifier and version metadata.

[0087] An atomic update transaction mechanism is used to update the dynamic key-value pair configuration of the target entity set with the configuration parameter set. Specifically, a configuration change snapshot is created, then a batch key-value pair update operation is performed, and the integrity of the update result is verified. A configuration update log is maintained to record the change history and operation context of each key-value pair. For sensitive configuration items, additional security checks and permission verification are implemented during the update process.

[0088] Further, a conflict prediction model based on machine learning is introduced to analyze historical configuration change patterns to identify potential key name conflict risks in advance. A distributed configuration management architecture is used to shard the configuration parameter set and store it in multiple nodes, and a consensus algorithm is used to ensure the consistency of configuration updates.

[0089] Step S40, generating a task flow orchestration template based on the updated target entity set, and scheduling task objects to execute a task chain through the task flow orchestration template.

[0090] In this embodiment, the template engine generates a task flow orchestration template based on the updated target entity set. An abstract syntax tree-based template instantiation algorithm is used to convert abstract task templates into specific executable task flows. The template instantiation algorithm parses the configuration parameters and associated relationships of the target entity set, and based on the parsing results, constructs a parameter binding context, traverses the template syntax tree, and recursively replaces variable placeholders and expressions therein.

[0091] During instantiation, strict parameter type checking and dependency verification are implemented to ensure that all required parameters are correctly assigned and the parameter types meet the expected requirements. The template engine supports conditional branching and loop control structures to dynamically generate differentiated task flows based on the specific characteristics of the target entity set. For complex task chains, a phased instantiation strategy is adopted to process task framework structures and fill in specific execution parameters to perfect dependency relationship definitions. The generated task flow orchestration template is described in a standardized JSON format, which includes complete information such as task step definitions, parameter binding mappings, execution dependency graphs, and exception handling strategies.

[0092] An asynchronous task scheduling architecture based on message-driven is used to schedule task objects to execute task chains through the task flow orchestration template. Specifically, the task steps and dependencies in the task flow orchestration template are parsed, and a task execution directed acyclic graph is constructed based on the parsing results. A topological sorting algorithm is used to determine the task execution sequence. During the scheduling of the task objects, heuristic algorithms are used to optimize task allocation strategies to maintain task execution contexts based on multi-dimensional factors such as task priority, resource constraints, and system load, ensuring data transfer and state synchronization between tasks.

[0093] A full-link monitoring mechanism is implemented during task execution to collect task execution status, resource consumption, and performance indicators through burying points. A rule engine-based fault detection strategy is used to identify task execution abnormalities in real time. For task objects that fail to execute, a multi-level retry mechanism is automatically triggered, including immediate retry, interval retry, and exponential backoff retry strategies. When retries reach the upper limit and still fail, a pre-defined fault-tolerant processing flow is executed, including task degradation, service switching, and transaction rollback operations.

[0094] After task execution is completed, a structured execution report is generated, which records the task execution timeline, resource usage, execution result data, and exception event records in detail. A hierarchical storage strategy is used, with key indicators saved for a long time and detailed logs archived on demand, supporting subsequent audit analysis and performance optimization.

[0095] In addition, a task scheduling optimization algorithm based on deep reinforcement learning is adopted, and a scheduling strategy model is trained through historical execution data to dynamically adjust the execution order and resource allocation of the task object. Meanwhile, task execution digital twin technology is introduced to simulate the task chain execution process in a virtual environment, identify performance bottlenecks and resource conflicts in advance, and optimize actual execution efficiency. Furthermore, a distributed task queue architecture is provided to support cross-regional task scheduling and execution, improving the scalability and fault tolerance of the system.

[0096] Further, the step of generating a task flow orchestration template based on the updated target entity set includes:

[0097] extracting configuration parameters in the target entity set and replacing parameter placeholders of the task functions with the configuration parameters;

[0098] verifying parameter dependency relationships between the task functions and determining a task execution order based on the parameter dependency relationships;

[0099] instantiating the task functions and assembling the instantiated task functions into an executable task chain in the task execution order.

[0100] In this embodiment, the parameter extraction engine extracts configuration parameters from the target entity set, uses a depth-first traversal algorithm to scan the configuration tree structure of the entity set, and collects parameter key-value pairs of all leaf nodes. A parameter namespace mapping table is established to classify and merge scattered configuration parameters according to business domains. After the configuration parameter extraction is completed, parameter template matching is performed to accurately match the collected configuration parameters with the parameter placeholders of the task functions, and a regular expression-based replacement algorithm is used to complete parameter injection.

[0101] The parameter dependency relationships between the task functions are verified through a pre-constructed parameter dependency directed graph, the input and output parameter signatures of each task function are analyzed based on the parameter dependency directed graph, and parameter transmission links are established through the analysis results. A topological sorting algorithm is used to detect dependency loops and perform dependency breaking processing on discovered cyclic dependencies. In addition, the verification of the parameter dependency relationships also includes parameter type compatibility checking and data format consistency checking to ensure the effectiveness of parameter transmission between task functions.

[0102] Based on the verified parameter dependency relationships, a critical path analysis algorithm is used to determine the task execution order. The earliest start time and the latest start time of each task node are calculated through the critical path analysis algorithm, and the critical task path is identified based on the calculation results. For task nodes on non-critical paths, parallelization optimization is implemented to improve task execution efficiency. After the execution order of the task nodes is determined, a task scheduling sequence is generated to clearly define the timing constraints and synchronization points between the task nodes.

[0103] And, a dynamic class loading technique is adopted to create a task execution instance for instantiating the task function, and the instantiation process includes steps such as memory allocation, context initialization, and resource binding. An independent execution sandbox is allocated for each task instance to ensure the running isolation between tasks. After instantiation, the function verification of the task function is performed, including interface connectivity test and exception handling mechanism verification.

[0104] The instantiated task functions are assembled into an executable task chain in a pipeline-filter architecture mode according to the determined task execution order, and the assembly process establishes the data flow channel between tasks and configures the task execution monitoring point. A unified execution context is generated for the task chain to maintain the global state and shared resources. The finally generated task chain contains complete execution control logic, error recovery mechanism, and performance monitoring configuration.

[0105] In addition, a parameter optimization algorithm based on machine learning is adopted to automatically adjust the parameter passing strategy through historical execution data. A distributed task chain assembly mechanism is introduced to decompose a complex task chain into multiple sub-chains for parallel assembly on different nodes, improving the efficiency of large-scale task processing.

[0106] And, the steps of instantiating the task function and assembling the instantiated task function into an executable task chain in the task execution order include:

[0107] Detecting the task parameter passing matching between adjacent task functions in the task chain;

[0108] If the task parameters are detected to be unmatched, a parameter conversion function between the adjacent task functions is obtained based on the parameter mapping rule;

[0109] Inserting the parameter conversion function between the adjacent task parameters to form the task chain.

[0110] In this embodiment, the parameter matching detector is used to detect the task parameter passing matching between adjacent task functions in the task chain, and a multi-dimensional matching algorithm is used to verify the parameter type compatibility check, data structure consistency, and data semantic equivalence. A type system mapping table is established to perform implicit type conversion check on basic data types and structure similarity calculation on complex object types. At the same time, the number, order, and optional requirements of the parameters are considered to generate a detailed matching degree evaluation report.

[0111] When detecting that the task parameters do not match, a parameter conversion function between adjacent task functions is obtained based on a pre-created parameter mapping rule library, wherein the mapping rule library adopts a storage structure based on a graph database, and stores conversion paths between various parameter types and corresponding conversion function metadata. A conversion path search algorithm is executed in the parameter mapping rule library to find an optimal conversion path from a source parameter type to a target parameter type. For complex conversion scenarios, a multi-level conversion function chain is combined to ensure the integrity and correctness of parameter conversion.

[0112] And, a runtime verification is performed on the obtained parameter conversion function, which includes function signature checking, resource dependency verification, and exception handling capability evaluation. After verification, an execution context is created for the conversion function, necessary auxiliary parameters and configuration information are initialized, and the conversion function is loaded using dynamic link library technology.

[0113] The verified parameter conversion function is inserted between adjacent task functions and a function chain reconstruction operation is implemented. The insertion process maintains the topological structure of the task chain unchanged, and creates a conversion function node between adjacent task nodes. And, appropriate data buffering mechanisms are configured for the conversion function to balance the processing speed difference between producers and consumers. The final task chain formed maintains the original execution semantics while ensuring smooth parameter transfer between tasks.

[0114] In another possible implementation, a machine learning-based parameter conversion recommendation mechanism is used to intelligently recommend an optimal conversion scheme by analyzing historical conversion records, and a conversion function performance optimization strategy is introduced to pre-compile and cache high-frequency conversion paths to improve task chain execution efficiency. In addition, a distributed conversion function library is provided to support cross-node conversion function sharing and load balancing.

[0115] And, the step of scheduling a task object to execute the task chain based on the task flow arrangement template includes:

[0116] The task chain is parsed into a set of asynchronous execution task units, and an execution topology graph is constructed based on the dependency relationship of each task unit in the set of task units.

[0117] The task object is scheduled to execute the task chain according to the execution topology graph.

[0118] In this embodiment, the task chain is decomposed into a set of asynchronous execution task units by a task chain parser. A task division algorithm based on control flow analysis is used to identify parallel execution segments and sequential execution segments in the task chain. According to the identification result, an independent execution context is created for each task unit, which includes input parameter cache, output result container and exception handling framework. A loosely coupled communication mechanism is established between each task unit through a message queue to ensure the reliability and orderliness of data transmission.

[0119] In constructing the execution topology graph based on the dependency relationship of each task unit in the task unit set, a graph theory modeling method is adopted, the data dependency and control dependency between task units are analyzed first, and a dependency relationship matrix is established; then a topological sorting algorithm is applied to determine the task execution sequence, and a circular dependency is detected and processed. A resource constraint edge and a timing constraint edge are added to the execution topology graph to form a complete execution constraint network. The execution topology graph adopts an adjacency list storage structure, which supports efficient execution path query and dynamic adjustment.

[0120] When scheduling the task object to execute the task chain according to the execution topology graph, a priority-based resource allocation strategy is used to analyze the resource requirements and execution characteristics of each task unit, and intelligent scheduling decisions are made in combination with the current system load. Specifically, a work-stealing algorithm is used to optimize load balancing, allowing an idle worker thread to steal a task unit execution from a busy thread. Moreover, through a fault-tolerant mechanism, a task unit that fails to execute is automatically triggered for retry or alternative solution.

[0121] During task execution, distributed transaction management is used to ensure the atomicity and consistency of the task chain. A global execution state machine is maintained, and the execution progress and resource usage of each task unit are tracked in real time. For long-running task chains, a snapshot and recovery mechanism is supported for the execution process to ensure task continuity in the event of system failure.

[0122] Further, an intelligent scheduling algorithm based on machine learning is used to train a scheduling model based on historical execution data to predict task execution time and resource consumption. A multi-cluster collaborative scheduling mechanism is used to distribute the task chain to multiple computing clusters for execution, improving the scalability and disaster recovery capability of the system.

[0123] In this embodiment, by establishing a multi-level configuration management architecture and a templated task chain mechanism, the decoupling of configuration and execution is realized, and after automatically merging multi-level configuration parameters, environment-adaptive differentiated execution is supported, significantly improving the operation and maintenance efficiency. At the same time, full-link execution monitoring and audit tracing capabilities are provided, reducing the risk of human operation errors, effectively ensuring the reliability and consistency of the operation and maintenance process.

[0124] Further, it can also be viewed Figure 2 , Figure 2 The flowchart of the second embodiment of the internet self-research operation and maintenance management method of the present application is based on Figure 2 , after the step of scheduling the task object to execute the task chain through the task flow arrangement template, steps S50-S60 are further included:

[0125] Step S50, real-time monitoring of the execution status of each task object;

[0126] Step S60, if the task object execution fails, the rollback operation of the related task object is triggered based on the dependency relationship of the task object.

[0127] In this embodiment, the execution status of each task object is monitored in real time through a distributed monitoring agent. The monitoring agent is deployed on the execution node of each task object, adopts an event-driven architecture to collect task execution data, and the task execution data includes task heartbeat signals, resource usage indicators, execution progress percentages, and abnormal event records. A time series database is established to store the monitoring data, and a stream processing technology is used to analyze the task health status in real time. A multi-level alarm mechanism is implemented in the monitoring process, and different levels of alarm events are automatically triggered according to the task execution deviation.

[0128] In addition, the life cycle state of each task object is tracked through a state machine engine, and the key performance indicators of the task object execution are calculated in real time based on predefined state transition rules, including start execution, running, pause, completion, and failure, and the key performance indicators include execution duration, resource consumption rate, and progress completion rate. Execution timeout detection is implemented based on long-running task objects, and the timeout threshold is dynamically adjusted according to the task type.

[0129] When the task object execution failure is detected, a rollback analysis engine based on the dependency relationship is started to traverse the task execution topology graph, and all downstream task nodes that depend on the failed task object are identified. The rollback range determination adopts a reverse breadth-first search algorithm, which starts from the failed task node and traverses all affected task nodes in reverse along the dependency edge. A rollback operation sequence is generated for each task object that needs to be rolled back, ensuring the atomicity and data consistency of the rollback operation.

[0130] The rollback operation is executed in a compensation transaction mode, and a corresponding rollback operation log is maintained for each task object. The rollback process follows the reverse order of the dependency relationship, and the most downstream task is rolled back first, and then the rollback is gradually performed to the failed task node. A retry mechanism and timeout control are provided when implementing the rollback operation, ensuring the reliability of the rollback in complex dependency scenarios. After the rollback is completed, a detailed rollback report is generated, recording the rollback operation sequence and the execution result.

[0131] Further, a fault prediction model based on machine learning is used to identify potential failure risks in advance by analyzing the task execution mode, and to implement preventive rollback. The task execution and rollback logs are recorded through blockchain technology, ensuring the non-tamperability and traceability of the operation records.

[0132] Further, it can also be seen that Figure 3 , Figure 3 The flowchart of the third embodiment of the internet self-research operation and maintenance management method of the present application is based on Figure 3The Internet self-developed operation and maintenance method further includes steps S70-S80:

[0133] In step S70, a business operation and maintenance task is acquired, and a template entity and an associated entity corresponding to the business operation and maintenance task are created.

[0134] In step S80, a configuration storage structure of the template entity and the associated entity is newly added in an extension configuration layer, and a configuration merging algorithm is updated based on the configuration storage structure.

[0135] In this embodiment, a business operation and maintenance task is acquired through an entity modeling engine, and a corresponding template entity and an associated entity are created. In the process of creating the template entity and the associated entity, an entity definition method based on metadata is adopted to first parse a task specification of the business operation and maintenance task, and extract entity attributes and relationship constraints in the parsing result.

[0136] Further, a data structure of the template entity is generated according to an entity type template, the data structure including an entity identifier, a version number and a basic attribute field. Further, a many-to-many relationship mapping table is established based on the associated entity, and an association strength coefficient and a life cycle binding strategy are defined.

[0137] In the process of creating the template entity and the associated entity, the integrity of entity field definition and the consistency of relationship constraints are checked through entity integrity verification. Specifically, the template entity adopts a hierarchical storage architecture, and basic attributes are stored in a main entity table and extended attributes are stored in an extended attribute table; the associated entity maintains bidirectional reference integrity, which is double-protected through a database foreign key constraint and an application program level check. A globally unique identifier is allocated to each newly created entity, and is registered in an entity metadata registration center.

[0138] In the extension configuration layer, a configuration storage structure of the template entity and the associated entity is newly added, a dynamic table structure extension mechanism is adopted, and the configuration storage structure is designed based on a key-value pair model. An entity-specific configuration table is created, and a table structure of the configuration table includes core fields such as a configuration key, a configuration value, a data type and a version number. Further, a configuration inheritance relationship chain is provided based on the configuration of the associated entity, to ensure correct propagation of configuration information.

[0139] When the configuration merging algorithm is updated based on the configuration storage structure, a configuration priority calculation model needs to be restructured. The new merging algorithm considers the configuration level position of the newly added entity, to redefine the configuration coverage rule. The configuration merging algorithm adopts a multi-source configuration fusion strategy, and for different values of the same configuration item in multiple entities, weighted calculation is performed based on entity level weights. An independent merging strategy is configured for the newly added entity type, to support a self-defined configuration parsing logic and a conflict resolution mechanism.

[0140] The configuration consistency verification is performed after the configuration merging algorithm is updated, to ensure smooth transition of the new and old configuration systems. The verification includes configuration item integrity check, dependency verification and business rule compliance test, and a configuration change tracking mechanism is established to record the version evolution history of the configuration merging algorithm.

[0141] It should be noted that the above examples are only used for understanding the present application and do not constitute a limitation on the internet self-research operation and maintenance management method of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0142] The present application provides an internet self-research operation and maintenance management device, which comprises at least one processor and a memory in communication connection with the at least one processor. The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the internet self-research operation and maintenance management method in the above-mentioned embodiment one.

[0143] Reference will be made to the following description of the embodiments of the present application, taken in conjunction with the accompanying drawings, in which Figure 4 which shows a structural schematic diagram of an internet self-research operation and maintenance management device suitable for implementing the embodiments of the present application. The internet self-research operation and maintenance management device in the embodiments of the present application can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description) and the like, and fixed terminals such as digital TVs, desktop computers and the like. Figure 4 The internet self-research operation and maintenance management device shown is only an example and should not bring any limitation on the functions and use range of the embodiments of the present application.

[0144] As Figure 4As shown, the Internet self-developed operation and maintenance management device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the Internet self-developed operation and maintenance management device are also stored in the random access memory 1004. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the Internet self-developed operation and maintenance management device to communicate wirelessly or by wire with other devices to exchange data. Although the Internet self-developed operation and maintenance management device with various systems is shown in the figure, it should be understood that all the systems shown are not required to be implemented or possessed. More or fewer systems can be alternatively implemented or possessed.

[0145] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carrying computer program code for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

[0146] The internet self-research operation and maintenance management device provided by the present application adopts the internet self-research operation and maintenance management method in the above embodiment, and can solve the technical problems that the existing operation and maintenance technology lacks a unified configuration management system and environment differentiation support capability, and cannot effectively realize hierarchical management of configuration and automatic arrangement of task execution. Compared with the prior art, the internet self-research operation and maintenance management device provided by the present application has the same beneficial effects as the internet self-research operation and maintenance management method provided by the above embodiment, and other technical features in the internet self-research operation and maintenance management device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0147] It should be understood that parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0148] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0149] The present application provides a storage medium, which is a computer readable storage medium, has computer readable program instructions (i.e. computer programs) stored thereon, and the computer readable program instructions are used to execute the internet self-research operation and maintenance management method in the above embodiment.

[0150] The computer readable storage medium provided in the application may be, for example, a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), or any suitable combination of the above.

[0151] The above computer readable storage medium may be contained in the Internet self-developed operation and maintenance management device; or may exist separately and not be assembled into the Internet self-developed operation and maintenance management device.

[0152] The above computer readable storage medium carries one or more programs, which, when executed by the Internet self-developed operation and maintenance management device, enable the Internet self-developed operation and maintenance management device to realize the technical content of the Internet self-developed operation and maintenance management method embodiment as shown above.

[0153] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0154] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0155] The modules involved in the embodiments of the present application can be implemented in software or hardware. In some cases, the names of the modules do not constitute a limitation on the modules themselves.

[0156] The readable storage medium provided by the application is a computer readable storage medium, which stores computer readable program instructions (namely, a computer program) for executing the above-mentioned internet self-research operation and maintenance management method, and can solve the technical problems that the existing operation and maintenance technology lacks a unified configuration management system and environment differentiation support capability, and cannot effectively realize hierarchical management of configuration and automatic arrangement of task execution. Compared with the prior art, the computer readable storage medium provided by the application has the same beneficial effects as the internet self-research operation and maintenance management method provided by the above-mentioned embodiments, and will not be repeated here.

Claims

1. A self-developed internet operation and maintenance management method, characterized in that, The self-developed internet operation and maintenance management method includes the following steps: The current operation and maintenance management requirements are analyzed, and at least one target instance is selected based on the analysis results. The target instance includes project instance, platform instance and service instance. The analysis of the current operation and maintenance management requirements adopts a natural language understanding model based on deep learning to perform multi-level semantic analysis on the operation and maintenance requirements text, and selects the target instance based on multi-objective optimization algorithm and instance reputation mechanism. Extract the foreign key of the target instance and dynamically associate the target instance based on the foreign key to form a target entity set. When dynamically associating the target instance based on the foreign key, a distributed entity association caching mechanism based on consistent hashing is used to manage the association relationship and maintain a spatiotemporal version for each association relationship. Obtain business configuration information based on the target entity set, and update the dynamic key-value pair configuration of the target entity set according to the business configuration information. The business configuration information is obtained by adopting a multi-level configuration merging algorithm, and the configuration data is merged from low to high according to the five-level configuration hierarchy of platform global configuration, service global configuration, project configuration, project platform configuration and project platform service configuration. During the merging, a complete inheritance chain of configuration items is established and the source tracking record is maintained. A task flow orchestration template is generated based on the updated target entity set. The task flow orchestration template is used to schedule task objects to execute task chains. When generating the task flow orchestration template, if a parameter mismatch is detected between adjacent task functions in the task chain, a parameter conversion function is obtained and inserted based on the parameter mapping rule base. During scheduling and execution, the execution status is monitored in real time and a rollback operation is triggered based on task dependencies. Furthermore, a machine learning model is used for fault prediction and preventive analysis.

2. The Internet self-developed operation and maintenance management method as described in claim 1, characterized in that, The step of extracting the foreign keys of the target instances and dynamically associating the target instances based on the foreign keys to form a target entity set includes: Obtain the association relationships between the target instances, and extract the foreign key references pointing to the main entity identifier from the association relationships; Dynamically construct composition relationships between the target instances based on the foreign key references, and encapsulate the target instances into the target entity set based on the composition relationships.

3. The Internet self-developed operation and maintenance management method as described in claim 1, characterized in that, The step of obtaining service configuration information based on the target entity set and updating the dynamic key-value pair configuration of the target entity set according to the service configuration information includes: Identify the business configuration items with conflicting key names in the business configuration information, and retain the business configuration items with higher priority; The retained business configuration items are integrated to generate a set of configuration parameters, and the dynamic key-value pair configuration of the target entity set is updated with the set of configuration parameters.

4. The Internet self-developed operation and maintenance management method as described in claim 1, characterized in that, The step of generating a task flow orchestration template based on the updated target entity set includes: Extract the configuration parameters from the target entity set and replace the parameter placeholders in the task function with the configuration parameters; Verify the parameter dependencies between the task functions, and determine the task execution order based on the parameter dependencies; Instantiate the task function, and assemble the instantiated task function into an executable task chain in the order of task execution.

5. The Internet self-developed operation and maintenance management method as described in claim 4, characterized in that, The step of instantiating the task function and assembling the instantiated task function into an executable task chain in the task execution order includes: Detect the matching of task parameter passing between adjacent task functions in the task chain; If a mismatch in the task parameters is detected, the parameter conversion function between adjacent task functions is obtained based on the parameter mapping rules; The parameter conversion function is inserted between adjacent task parameters to form the task chain.

6. The Internet self-developed operation and maintenance management method as described in claim 1, characterized in that, The step of scheduling task objects to execute task chains through the task flow orchestration template includes: The task chain is parsed into a set of asynchronous execution task units, and an execution topology graph is constructed based on the dependency relationships of each task unit in the set of task units; The task chain is executed by scheduling task objects according to the execution topology graph.

7. The Internet self-developed operation and maintenance management method as described in claim 1, characterized in that, After the step of scheduling the task object to execute the task chain through the task flow orchestration template, the method further includes: Real-time monitoring of the execution status of each task object; If the task object fails to execute, a rollback operation of the relevant task objects is triggered based on the dependency relationship of the task object.

8. The Internet self-developed operation and maintenance management method as described in any one of claims 1 to 7, characterized in that, The self-developed internet operation and maintenance management method also includes: Obtain the business operation and maintenance task, and create the template entity and associated entity corresponding to the business operation and maintenance task; In the extended configuration layer, a configuration storage structure for the template entity and associated entities is added, and the configuration merging algorithm is updated based on the configuration storage structure.

9. A self-developed internet operation and maintenance management device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the Internet self-developed operation and maintenance management method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the Internet self-developed operation and maintenance management method as described in any one of claims 1 to 8.

Images