Event-Driven Automated Warehouse Lifecycle Management System and Method

Abstract

This invention relates to an event-driven automated warehouse lifecycle management system and method, and pertains to the field of data processing technology. The system includes: a dynamic monitoring module that monitors the target warehouse's original warehouse event pool and acquires the first original event; a standard processing module that introduces a structured pre-defined plan for structured standard processing to obtain the first structured event; a matching analysis module that activates a management decision engine to perform matching analysis and obtain the first matching result; an operation instruction generation module that generates a first automated operation instruction set based on the successful matching result; and a management execution module that performs lifecycle management execution for the target warehouse. This invention solves the technical problems in existing technologies, such as low efficiency, poor real-time performance, fragmented processes, and task conflicts and inconsistent states in high-concurrency scenarios caused by the high reliance on manual labor in warehouse operations. It achieves the technical effects of improving the real-time performance, operational consistency, system scalability, and task scheduling reliability of warehouse operations.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an event-driven automated warehouse lifecycle management system and method. Background Technology

[0002] In current code hosting and model sharing platforms such as GitHub, Hugging Face, and GitLab, repositories, as core digital asset units, still rely heavily on manual operation for lifecycle management, including the discovery of potential repositories, quality assessment, classification and tagging, uploading and recommendation, and traffic allocation. With the rapid growth of platform scale and the increasing frequency of content publishing, traditional manual operation has many shortcomings. Platform repository-related metrics change rapidly, exhibiting high dynamism and unpredictability, making real-time monitoring difficult and often resulting in missed optimal promotion opportunities, preventing high-quality content from receiving the exposure it deserves. Furthermore, differences in experience and judgment standards among different operators lead to a lack of unified and quantifiable execution standards for repository tagging, uploading, and recommendation, hindering effective operational strategies. It is somewhat difficult to scale up and replicate; as the number of warehouses grows exponentially, relying on manual continuous monitoring and operation requires a large investment of human resources, and the linear or even superlinear growth of operating costs severely limits the platform's scalability at the content operation level; the existing management system lacks a unified event-driven mechanism and state coordination scheduling capability in warehouse status perception, lifecycle assessment, operational action execution, and effect feedback, making it difficult for the entire process to form an efficient automated growth loop; and when multiple events such as Star changes, visibility changes, and external rankings are triggered simultaneously in the same warehouse, the lack of a unified task status management and anti-conflict scheduling mechanism may lead to systemic problems such as duplicate execution, resource waste, and inconsistent states, affecting the atomicity and reliability of operational operations.

[0003] Therefore, current technologies suffer from technical problems such as low efficiency, poor real-time performance, fragmented processes, and task conflicts and inconsistent states in high-concurrency scenarios due to the high reliance on manual labor in warehouse operations. Summary of the Invention

[0004] This application provides an event-driven automated warehouse lifecycle management system and method, which solves the technical problems of low efficiency, poor real-time performance, fragmented processes, and task conflicts and inconsistent states in high-concurrency scenarios caused by the high dependence of warehouse operations on manual labor in the prior art. It achieves the technical effect of improving the real-time performance, operational consistency, system scalability, and task scheduling reliability of warehouse operations.

[0005] This application provides an event-driven automated warehouse lifecycle management system, comprising: a dynamic monitoring module for dynamically monitoring a target warehouse to obtain an original warehouse event pool and acquiring a first original event from the original warehouse event pool; a standard processing module for introducing a structured pre-defined plan to perform structured standard processing on the first original event to obtain a first structured event; a matching analysis module for activating a management decision engine and performing matching analysis on the first structured event in conjunction with the rule base embedded in the management decision engine to obtain a first matching result; an operation instruction generation module for generating a first automated operation instruction set corresponding to the first structured event based on the first matching result, if the matching is successful; and a management execution module for performing lifecycle management execution on the target warehouse based on the first automated operation instruction set.

[0006] In a possible implementation, the dynamic monitoring module further includes: capturing the database binary log event source of the target repository; capturing the version control system commit event source of the target repository; capturing the external business system message event source of the target repository; and analyzing the database binary log event source, the version control system commit event source, and the external business system message event source to form the original repository event pool.

[0007] In a possible implementation, the standard processing module further includes: extracting predetermined event features from the structured plan; analyzing the first original event based on the predetermined event features to obtain first predetermined feature parameters; and constructing the first structured event based on the first predetermined feature parameters; wherein the predetermined event features include event type, event source, triggering entity attribute, timestamp, and event key attribute payload.

[0008] In a possible implementation, the matching analysis module further includes: routing the first structured event to the rule base according to its corresponding first event type or first triggering subject attribute; extracting a first rule from the rule base, wherein the first rule corresponds to a first triggering constraint; determining whether the first structured event matches the first triggering constraint, and obtaining the first matching result; wherein the first triggering constraint refers to a first state change condition, a first numerical threshold condition, a first composite logic condition, or a first external signal condition.

[0009] In a possible implementation, the matching analysis module further includes: the first numerical threshold condition refers to the number of followers of the target warehouse reaching a first preset threshold, or the number of downloads reaching a second preset threshold, or the update frequency being higher than a third preset threshold.

[0010] In a possible implementation, the matching analysis module further includes: the first composite logical condition refers to having a specific business tag and an update frequency higher than a preset threshold within a predetermined period when the target warehouse is in a public state.

[0011] In a possible implementation, the operation instruction generation module further includes: if the first structured event matches the first triggering constraint according to the first matching result, the management decision engine adds the first operation instruction corresponding to the first rule to the first automated operation instruction set.

[0012] In a possible implementation, the management execution module further includes: determining whether the first operation instruction belongs to the category of predetermined instructions; if it does, issuing an audit signal, and pushing the first operation instruction to the manual audit platform based on the audit signal.

[0013] In a possible implementation, the management execution module further includes: continuously collecting automated execution records; obtaining automated execution effect information based on the automated execution records; performing normalization analysis on the automated execution effect information to obtain a real-time effect index; and evaluating and optimizing the rule base when the real-time effect index is lower than a predetermined effect threshold; wherein the automated execution effect information includes click-through rate, conversion rate, and user retention rate.

[0014] This application also provides an event-driven automated warehouse lifecycle management method, the method comprising: dynamically monitoring a target warehouse to obtain an original warehouse event pool, and acquiring a first original event from the original warehouse event pool; introducing a structured pre-defined scheme to perform structured standard processing on the first original event to obtain a first structured event; activating a management decision engine, and performing matching analysis on the first structured event in conjunction with the rule base embedded in the management decision engine to obtain a first matching result; based on the first matching result, if the matching is successful, the management decision engine generates a first automated operation instruction set corresponding to the first structured event; and executing lifecycle management of the target warehouse based on the first automated operation instruction set.

[0015] This application proposes an event-driven automated warehouse lifecycle management system and method, comprising: a dynamic monitoring module that monitors the target warehouse's original warehouse event pool and obtains the first original event; a standard processing module that introduces a structured plan for structured standard processing to obtain the first structured event; a matching analysis module that activates the management decision engine to perform matching analysis and obtain the first matching result; an operation instruction generation module that generates the first automated operation instruction set based on the successful matching result; and a management execution module that performs lifecycle management of the target warehouse. This addresses the technical problems in existing technologies, such as low efficiency, poor real-time performance, fragmented processes, and task conflicts and inconsistencies in high-concurrency scenarios caused by the high reliance on manual labor in warehouse operations. It achieves the technical effects of improving the real-time performance, operational consistency, system scalability, and task scheduling reliability of warehouse operations. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a schematic diagram of the structure of an event-driven automated warehouse lifecycle management system provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of an event-driven automated warehouse lifecycle management method provided in an embodiment of this application.

[0019] Figure labeling: Dynamic monitoring module 10, standard processing module 20, matching analysis module 30, operation instruction generation module 40, management execution module 50. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0021] This application provides an event-driven automated warehouse lifecycle management system, such as... Figure 1 As shown, the system includes: The dynamic monitoring module 10 is used to dynamically monitor the target warehouse to obtain the original warehouse event pool and acquire the first original event in the original warehouse event pool.

[0022] Furthermore, the specific configuration of the dynamic monitoring module 10 also includes capturing the database binary log event source of the target repository; capturing the version control system commit event source of the target repository; capturing the external business system message event source of the target repository; and analyzing the database binary log event source, the version control system commit event source, and the external business system message event source to form the original repository event pool.

[0023] Preferably, the dynamic monitoring module is the core component of the system responsible for real-time and proactively capturing changes in the target repository's state. It continuously monitors multiple data sources, aggregates various captured raw events into a raw repository event pool, and extracts the first raw event from it. Specifically, it uses the Canal component to monitor the binary logs of the core business database, such as MySQL's Binlog, capturing change events in the target repository's metadata, such as repository creation, updates, deletions, public / private state switching, tag additions / deletions, changes in Star count, changes in download count, and visibility switching, to capture the database binary log event source. It also receives push, tag, and release events through GitHub or GitLab Webhooks to identify the repository's code update frequency and content changes, thus capturing version control system commit event sources. Finally, it receives operational backend instructions and third-party trend lists (such as Hugging Face Daily). HTTP push messages from paper or data analysis systems are used to capture external business system message event sources. Then, the database binary log event sources, version control system commit event sources, and external business system message event sources are analyzed, and their events are collected into a Kafka message queue to form a temporary event storage queue, i.e., the original repository event pool. Finally, by subscribing to database Binlog, Git event streams, and external API messages, the first original event in the original repository event pool is captured in real time. That is, an event is selected as the first original event according to the time order or priority strategy. For example, the earliest unprocessed event in the pool is selected, or "visibility change" events are processed first and "Star change" events are processed second.

[0024] The standard processing module 20 is used to introduce a structured plan to perform structured standard processing on the first original event to obtain the first structured event.

[0025] Furthermore, the specific configuration of the standard processing module 20 also includes: extracting predetermined event features from the structured plan; analyzing the first original event based on the predetermined event features to obtain first predetermined feature parameters; and constructing the first structured event based on the first predetermined feature parameters; wherein the predetermined event features include event type, event source, triggering subject attribute, timestamp, and event key attribute payload.

[0026] Preferably, the standard processing module is the core component of the system responsible for converting raw, heterogeneous, and unstructured event data into structured event objects in a unified format. By introducing a structured pre-defined plan as a conversion template, the first raw event is parsed, mapped, and reorganized into a first structured event that the decision engine can process uniformly. Specifically, the predefined structured plan is read from the configuration center or local cache, and its predetermined event features are extracted, including event type, such as "change in number of stars" or "repository visibility switch", event source, such as "database log" or "Git commit webhook", trigger subject attributes, such as repository ID, the precise timestamp of the event occurrence, and the event key attribute payload, such as the number of stars changing from 45 to 100. Then, according to the field mapping rules in the structured plan, the corresponding event features are analyzed and extracted from the first raw event to determine the first predetermined feature parameters. Then, the five predetermined feature parameters are assembled in a unified format (such as JSON format) to obtain the first structured event, which can be read and used directly and without obstacles. With standardized processing, the subsequent decision engine does not need to develop three different parsing logics for "database events", "Git events" and "external API events". It only needs to understand the standardized JSON format, which greatly reduces the coupling and complexity of the system.

[0027] The matching analysis module 30 is used to activate the management decision engine and perform matching analysis on the first structured event in combination with the rule base embedded in the management decision engine to obtain the first matching result.

[0028] Preferably, the matching analysis module is the core decision-making component in the system responsible for matching standardized events with predefined business rules. It receives the first structured event, activates the management decision engine, searches the rule base for a matching rule, and finally outputs the result of "match" and "which rule matches." Specifically, it starts or wakes up the decision engine service, putting it into a ready state to receive and process structured events. If the decision engine is a persistent service, it pulls events from a message queue (such as Kafka) to begin processing. If the decision engine is started on demand, it instantiates an engine object and loads the rule base. The rules in the library support dynamic addition, deletion, modification, and querying without requiring a system restart. Each rule contains two parts: "triggering conditions" and "operation instructions." For example, if the event type is star_change, the new value is ≥50, and the repository status is public, then the operation instructions are ADD_TAG (high potential) and TRIGGER_PROMOTION. Then, the event type, triggering subject, payload, and other fields in the first structured event are compared with the triggering conditions of each rule in the rule library to determine whether the event meets the triggering conditions of the rule. The matching / non-matching results are recorded and output as the first matching result.

[0029] Furthermore, the specific configuration of the matching analysis module 30 also includes routing the first structured event to the rule base according to its corresponding first event type or first triggering subject attribute; extracting the first rule in the rule base, wherein the first rule corresponds to the first triggering constraint; determining whether the first structured event matches the first triggering constraint, and obtaining the first matching result; wherein the first triggering constraint refers to the first state change condition, the first numerical threshold condition, the first composite logic condition, or the first external signal condition.

[0030] Preferably, a high-performance rule engine (such as Drools) or a self-developed rule matching service is used to load the rule base stored in the configuration center (such as Apollo). Based on the first event type or the first trigger subject attribute key attribute of the event, a subset of rules related to it in the rule base is quickly located. For example, if the first event type is star_change, the event is only sent to the rules that are interested in "Star change"; if the first trigger subject attribute is Repo_X, all rules related to that repository are found, thereby greatly improving the matching efficiency.

[0031] Preferably, within the rule subset filtered by routing, the system begins to extract and process each rule one by one, referred to as the first rule. Each rule defines one or more conditions that must be met, i.e., the first trigger constraint. The actual data in the first structured event is then substituted into the first trigger constraint judgment formula for calculation. If the formula result is "true", the match is successful; if it is "false", the match fails. The success / failure result is the first matching result. For example, if the event_type of the first structured event is "star_change" and the "payload" is {"new_value": 100}, the first trigger constraint judgment formula is (event_type="star_change") AND (payload.new_value>=50). The system judges that the event_type of the first structured event is star_change (true) and 100>=50 (true). The final result is true AND true = true, so the first matching result is a successful match.

[0032] Preferably, the first triggering constraint refers to a first state change condition, a first numerical threshold condition, a first compound logic condition, or a first external signal condition. The first state change condition refers to a warehouse attribute changing from state A to state B, such as warehouse visibility changing from private to public, or the warehouse tag adding "AI" from none. The first numerical threshold condition refers to a quantifiable indicator reaching or exceeding a preset threshold, such as Star count ≥ 50, monthly downloads > 1000, or update frequency ≥ 3 times in the last 7 days. The first compound logic condition refers to multiple single conditions combined using logical operators such as AND, OR, and NOT, such as (warehouse state is public) AND (has "SOTA" tag) AND (update frequency exceeds the threshold). The first external signal condition refers to a signal originating from an external platform or system, not from the warehouse itself, such as the warehouse appearing in the top 10 of the Hugging Face trending list or the "emergency promotion" signal being manually triggered in the operations backend.

[0033] Furthermore, the specific configuration of the matching analysis module 30 also includes that the first numerical threshold condition refers to the number of followers of the target warehouse reaching a first preset threshold, or the number of downloads reaching a second preset threshold, or the update frequency being higher than a third preset threshold.

[0034] Preferably, the number of followers for a target repository refers to the number of times a repository has been favorited, liked, or followed by users. On platforms like GitHub, the most typical corresponding metric is the number of stars, representing the repository's popularity and community recognition. The first preset threshold is a pre-defined number of followers for a repository. If the number of followers for a target repository changes from below the first preset threshold to equal to or above the first preset threshold, this condition is triggered. The rule is configured to automatically add the "Hot Repository" tag when the number of followers is ≥100. For example, if the number of stars for a repository increases from 95 to 100, the system detects this change, determines that the condition 100 ≥ 100 is true, and automatically tags the repository as a "Hot Repository". The number of downloads for a target repository refers to the number of times the repository has been cloned, downloaded, or referenced, directly reflecting its practical value and usage frequency. The second preset threshold is a pre-defined cumulative or periodic download volume for the repository. The rule is configured to automatically trigger "Light Promotion" when the monthly download volume is ≥1000. For example, if a utility library, Repo_B, accumulates 1000 downloads within a month, the system detects this change and, if the condition is true, automatically initiates a small advertising promotion plan for that repository. The update frequency of a target repository refers to the number of code commits, version releases, or file updates within a unit of time, representing the project's activity and maintenance status. The third preset threshold is a pre-defined average number of updates to the repository over a period of time. The rule is configured to automatically add a "highly active" tag when the number of updates in the last 30 days is greater than 10. For example, if an AI model library, Repo_C, has 15 code commits and 2 version releases in the last 30 days, the system evaluates its update frequency as "15 times / 30 days." If the condition 15 > 10 is true, it will be tagged as "highly active" and prioritized for recommendation to users, thereby ensuring the automatic and accurate identification of high-value, high-potential, and highly active high-quality repositories.

[0035] Furthermore, the specific configuration of the matching analysis module 30 also includes that the first composite logic condition refers to having a specific business tag and an update frequency higher than a preset threshold within a predetermined period when the target warehouse is in a public state.

[0036] Preferably, the first composite logical condition is a judgment formed by combining multiple individual conditions through logical operators. The entire composite condition is true only when all sub-conditions are true. The three sub-conditions that must be met simultaneously include: the target repository is in a public state; it has a specific business tag; and its update frequency within a predetermined period is higher than a preset threshold. Among these, the visibility attribute of the target repository is public, which is used for any external operation and promotion. The repository must have been tagged with one or more predefined tags with business meaning for content or domain segmentation. For example, the platform may only want to promote high-quality repositories in the "artificial intelligence / machine learning" domain. Alternatively, it may only promote tools related to "Web development frameworks," with example tags including "SOTA model," "LLM," "front-end framework," "data processing," and "high potential." A frequency higher than a preset threshold within a predetermined period refers to the target repository having more than a preset activity threshold for code commits, version releases, or file updates within a recent fixed, configurable time window. This helps identify continuously maintained and vibrant projects. By analyzing version control system event sources or codebase change events, the total number of updates to the repository within a specified period is calculated, for example, configured to be >10 updates in the last 30 days. This enables refined, scenario-based intelligent operation, improves accuracy, and reduces misjudgments.

[0037] The operation instruction generation module 40 is used to generate a first set of automated operation instructions corresponding to the first structured event based on the first matching result, if the matching is successful.

[0038] Preferably, the operation instruction generation module is responsible for converting the action description corresponding to the event matching a certain rule determined by the decision engine into a standardized instruction set that can be understood and executed by downstream services. Specifically, the operation instruction generation module receives the first matching result and extracts the operation instruction template bound to the successfully matched rule. That is, based on the operation instruction list provided, it converts it into a structured instruction object defined internally by the system, including an executable structured first automated operation instruction set, a list or set containing one or more specific operation instructions, wherein each instruction object contains all the information required to execute the operation.

[0039] Furthermore, the specific configuration of the operation instruction generation module 40 also includes, based on the first matching result, if the first structured event matches the first triggering constraint, the management decision engine adds the first operation instruction corresponding to the first rule to the first automated operation instruction set.

[0040] Preferably, the first matching result is used as the input and judgment basis. If the first matching result is a successful match, that is, the first structured event matches the first triggering constraint, for example, the match_success field in the matching result is true (i.e., the match is successful), then the management decision engine adds the first operation instruction corresponding to the first rule to the first automated operation instruction set. Here, the first rule refers to the rule that is successfully matched, the first operation instruction is the operation instruction that is predefined and bound in the first rule, and the first automated operation instruction set is used to temporarily store all the instructions to be executed triggered by this event. If a rule corresponds to multiple operation instructions, then each instruction will be added to the first automated operation instruction set in turn.

[0041] The management execution module 50 is used to perform lifecycle management execution of the target warehouse based on the first automated operation instruction set.

[0042] Preferably, the management execution module receives the first automated operation instruction set and is responsible for scheduling the corresponding executors, such as tagging executors, shelf-listing executors, and delivery executors. It calls the platform's tag service API to complete specific management operations on the target warehouse, thereby realizing automated management of the warehouse lifecycle. Specifically, it parses the first automated operation instruction set and assigns each instruction to the corresponding executor in a sequential or parallel / pipeline manner. This allows for the automatic addition or removal of preset business tags for the warehouse, listing the warehouse on the platform's public recommendation slot or removing it from the recommendation slot, triggering the external delivery system and using the warehouse as promotional material for targeted traffic introduction. After execution, the module returns the execution result to the central control center and updates the warehouse's task status, thus achieving full automation of warehouse operations.

[0043] Furthermore, the specific configuration of the management execution module 50 also includes determining whether the first operation instruction belongs to the scope of a predetermined instruction; if it does, issuing an audit signal and pushing the first operation instruction to the manual audit platform based on the audit signal.

[0044] Preferably, not all automatically generated instructions are executed directly. Sensitive operations are intercepted and manually reviewed. For operations marked as "sensitive," the system proactively "applies the brakes" and transfers them to a manual review platform for approval. Only after approval is the operation actually executed. Specifically, before executing any operation instruction, the system first classifies and identifies it, checking whether the current instruction is predefined within a "predefined instruction category." The predefined instruction category is a configurable, predefined blacklist or sensitive list of operation types. Any operation falling into this category is considered to require additional approval. If the judgment result is that the current operation instruction belongs to the predefined instruction category, the system does not execute the instruction directly, but immediately generates an approval signal, which is an event notification that triggers the manual review process. This signal contains all the context information required for the review and switches the operation from "automatic execution" mode to "awaiting approval" mode. Then, the generated approval signal, along with the original operation instruction, is sent to the manual review platform. The manual review platform is responsible for handling approval tasks, typically providing a task list and implementing functions such as approval / rejection buttons and operation details viewing through API calls, message queues (such as Kafka), or internal RPC.

[0045] Furthermore, the specific configuration of the management execution module 50 also includes: continuously collecting automated execution records; obtaining automated execution effect information based on the automated execution records; performing normalization analysis on the automated execution effect information to obtain a real-time effect index; and evaluating and optimizing the rule base when the real-time effect index is lower than a predetermined effect threshold; wherein the automated execution effect information includes click-through rate, conversion rate, and user retention rate.

[0046] Preferably, the system continuously tracks the business effects after automated operations. When the performance is unsatisfactory, the system proactively sends signals to automatically optimize the rules, thus achieving a complete closed loop from perception to decision-making to execution and optimization. Specifically, by continuously recording the entire process of each automated operation's business data, the system determines the automated execution record, which includes at least the operation instructions, execution time, target warehouse, triggering rules, and execution results. The execution record is then correlated with the performance data of the business system to extract key indicators for measuring operational effectiveness, namely, automated execution performance information, including click-through rate (CTR), conversion rate, and user retention rate. CTR is the ratio of warehouse exposure to clicks on the recommended placement: CTR = number of clicks ÷ number of exposures × 100%, indicating the attractiveness of the warehouse title and description to users. Conversion rate is the proportion of deep interactions after a click: Conversion rate = number of favorites / clones / downloads ÷ number of clicks × 100%, indicating the actual value of the warehouse content to users. User retention rate is the return visit behavior of users after interacting with the warehouse: User retention rate = number of returning users ÷ number of first-time users × 100%, indicating the long-term value of the warehouse and user stickiness.

[0047] Preferably, multiple metrics such as click-through rate (CTR), conversion rate, and user retention rate are normalized to map each metric value to a unified scoring range (e.g., 0-100 points). Then, a real-time performance index is calculated by weighting the CTR values ​​according to preset weights. The real-time performance index = (CTR score × weight 1) + (conversion rate score × weight 2) + (retention rate score × weight 3). The preset weights are configured based on specific business strategies, operational goals, and data characteristics to reflect the relative importance of different performance metrics to the current business objectives. For example, if the core objective of the business is to attract user clicks, then the CTR has the highest weight; if the core objective of the business is to facilitate in-depth interaction, then the conversion rate has the highest weight; if the core objective of the business is to cultivate long-term user loyalty, then the retention rate has the highest weight. The calculated real-time performance index is compared with a preset performance threshold, which is a configurable passing grade. For example, if it is set to 75, a value below this indicates that the operational performance is not up to expectations. If it is below the preset performance threshold, it means that the current rule is not effective, and the rule optimization process is triggered. For example, an optimization suggestion report is generated, and after the operations staff reviews it, the rules are manually adjusted. The rule parameters are automatically adjusted according to the preset strategy, thereby improving the operational management effect.

[0048] In the above text, refer to Figure 1 An event-driven automated warehouse lifecycle management system based on embodiments of the present invention is described in detail. Next, reference will be made to... Figure 2 This paper describes an event-driven automated warehouse lifecycle management method according to embodiments of the present invention. The event-driven automated warehouse lifecycle management method, such as... Figure 2 As shown, the method includes: dynamically monitoring the target warehouse to obtain an original warehouse event pool, and acquiring a first original event from the original warehouse event pool; introducing a structured plan to perform structured standard processing on the first original event to obtain a first structured event; activating a management decision engine, and combining the rule base embedded in the management decision engine to perform matching analysis on the first structured event to obtain a first matching result; based on the first matching result, if the matching is successful, the management decision engine generates a first automated operation instruction set corresponding to the first structured event; and performing lifecycle management of the target warehouse based on the first automated operation instruction set.

[0049] In one possible implementation, dynamically monitoring the target repository to obtain the original repository event pool includes: capturing the database binary log event source of the target repository; capturing the version control system commit event source of the target repository; capturing the external business system message event source of the target repository; and analyzing the database binary log event source, the version control system commit event source, and the external business system message event source to form the original repository event pool.

[0050] In one possible implementation, a structured pre-plan is introduced to perform structured standard processing on the first original event to obtain a first structured event, including: extracting predetermined event features from the structured pre-plan; analyzing the first original event based on the predetermined event features to obtain first predetermined feature parameters; and constructing the first structured event based on the first predetermined feature parameters; wherein the predetermined event features include event type, event source, triggering subject attribute, timestamp, and event key attribute payload.

[0051] In one possible implementation, the management decision engine is activated, and the first structured event is matched and analyzed using the rule base embedded in the management decision engine to obtain a first matching result. This includes: routing the first structured event to the rule base based on its corresponding first event type or first triggering subject attribute; extracting a first rule from the rule base, wherein the first rule corresponds to a first triggering constraint; determining whether the first structured event matches the first triggering constraint to obtain the first matching result; wherein the first triggering constraint refers to a first state change condition, a first numerical threshold condition, a first composite logic condition, or a first external signal condition.

[0052] In one possible implementation, the first numerical threshold condition refers to the number of followers of the target repository reaching a first preset threshold, the number of downloads reaching a second preset threshold, or the update frequency being higher than a third preset threshold.

[0053] In one possible implementation, the first composite logic condition refers to having a specific business tag and an update frequency higher than a preset threshold within a predetermined period when the target warehouse is in a public state.

[0054] In one possible implementation, based on the first matching result, if the first structured event matches the first triggering constraint, the management decision engine adds the first operation instruction corresponding to the first rule to the first automated operation instruction set.

[0055] In one possible implementation, lifecycle management of the target warehouse is performed based on the first automated operation instruction set, including: determining whether the first operation instruction belongs to the scope of predetermined instructions; if it does, issuing an audit signal, and pushing the first operation instruction to the manual audit platform based on the audit signal.

[0056] In one possible implementation, the lifecycle management of the target warehouse is performed based on the first automated operation instruction set, and then the process further includes: continuously collecting automated execution records; obtaining automated execution effect information based on the automated execution records; performing normalization analysis on the automated execution effect information to obtain a real-time effect index; and evaluating and optimizing the rule base when the real-time effect index is lower than a predetermined effect threshold; wherein the automated execution effect information includes click-through rate, conversion rate, and user retention rate.

[0057] The event-driven automated warehouse lifecycle management system provided in this embodiment of the invention can execute the event-driven automated warehouse lifecycle management method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An event-driven automated warehouse lifecycle management system, characterized in that, include: The dynamic monitoring module is used to dynamically monitor the target warehouse to obtain the original warehouse event pool and acquire the first original event in the original warehouse event pool. The standard processing module is used to introduce a structured pre-plan to perform structured standard processing on the first original event to obtain the first structured event; The matching analysis module is used to activate the management decision engine and perform matching analysis on the first structured event in combination with the rule base embedded in the management decision engine to obtain the first matching result; The operation instruction generation module is used to generate a first set of automated operation instructions corresponding to the first structured event based on the first matching result, if the matching is successful. The management execution module is used to perform lifecycle management execution of the target warehouse based on the first automated operation instruction set.

2. The event-driven automated warehouse lifecycle management system as described in claim 1, characterized in that, The steps performed by the dynamic monitoring module include: Capture the database binary log event source of the target warehouse; Capture the version control system commit event source of the target repository; Capture the external business system message event source of the target warehouse; The original repository event pool is formed by analyzing the database binary log event sources, the version control system commit event sources, and the external business system message event sources.

3. The event-driven automated warehouse lifecycle management system as described in claim 1, characterized in that, The steps performed by the standard processing module include: Extract the features of predetermined events from the structured contingency plan; The first original event is analyzed based on the predetermined event characteristics to obtain the first predetermined feature parameters; The first structured event is constructed based on the first predetermined feature parameters; The predetermined event characteristics include event type, event source, triggering entity attribute, timestamp, and event key attribute payload.

4. The event-driven automated warehouse lifecycle management system as described in claim 3, characterized in that, The steps performed by the matching analysis module include: The first structured event is routed to the rule base according to its corresponding first event type or first triggering subject attribute; Extract the first rule from the rule base, wherein the first rule corresponds to the first triggering constraint; Determine whether the first structured event matches the first triggering constraint, and obtain the first matching result; Wherein, the first triggering constraint refers to the first state change condition, the first numerical threshold condition, the first composite logic condition, or the first external signal condition.

5. The event-driven automated warehouse lifecycle management system as described in claim 4, characterized in that, The first numerical threshold condition refers to the number of followers of the target warehouse reaching a first preset threshold, the number of downloads reaching a second preset threshold, or the update frequency being higher than a third preset threshold.

6. The event-driven automated warehouse lifecycle management system as described in claim 4, characterized in that, The first composite logic condition refers to the situation where the target warehouse is in a public state, has a specific business tag, and has an update frequency higher than a preset threshold within a predetermined period.

7. The event-driven automated warehouse lifecycle management system as described in claim 4, characterized in that, Based on the first matching result, if the first structured event matches the first triggering constraint, the management decision engine adds the first operation instruction corresponding to the first rule to the first automated operation instruction set.

8. The event-driven automated warehouse lifecycle management system as described in claim 7, characterized in that, The steps performed by the management execution module include: Determine whether the first operation instruction belongs to the predetermined instruction category; If it is found to be true, an audit signal is issued, and the first operation instruction is pushed to the manual audit platform based on the audit signal.

9. The event-driven automated warehouse lifecycle management system as described in claim 1, characterized in that, The steps performed by the management execution module also include: Continuously collect automated execution records; The automation execution effect information is obtained based on the automation execution records; The real-time performance index is obtained by normalizing the automated execution performance information. When the real-time performance index is lower than the predetermined performance threshold, the rule base is evaluated and optimized. The automated execution performance information includes click-through rate, conversion rate, and user retention rate.

10. An event-driven automated warehouse lifecycle management method, characterized in that, The method is applied to the event-driven automated warehouse lifecycle management system according to any one of claims 1-9, the method comprising: The target warehouse is dynamically monitored to obtain the original warehouse event pool, and the first original event in the original warehouse event pool is obtained. The first original event is processed using a structured pre-defined scheme to obtain the first structured event. Activate the management decision engine and perform matching analysis on the first structured event using the rule base embedded in the management decision engine to obtain the first matching result; Based on the first matching result, if the matching is successful, the management decision engine generates a first set of automated operation instructions corresponding to the first structured event; The lifecycle management of the target warehouse is executed based on the first set of automated operation instructions.

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