Financial digital intelligent management system

Abstract

The invention discloses a financial digital intelligent management system, and relates to the technical field of computers, and the system comprises an atomic power unit ACU, a side effect metadata management module which is used for decomposing a financial process into ACU and defining the structured side effect metadata of the ACU, and the structured side effect metadata comprises an influence object, a type, a condition, a range, a preset risk level and the importance of an associated data object; the dynamic process arrangement engine module is used for generating candidate execution paths according to the process definition and the ACU side effect metadata, and selecting a final execution plan by taking the score minimization as an optimization target; and the execution control and risk relief module selectively performs influence domain sandbox rehearsal before actual execution of the ACU according to a risk assessment result, and executes conditional rollback or compensation when execution fails or unacceptable side effects are generated. According to the invention, through prospective side effect management and intelligent risk avoidance and control, the stability, reliability, auditing performance and combinable innovation capability of a financial automation process are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a financial digital intelligent management system. Background Art

[0002] As enterprises deepen their digital transformation, financial process automation has become a key means of improving efficiency and reducing costs. Robotic process automation, application programming interface integration, and various business process management systems are widely used to automate various financial operations, such as order-to-cash, procure-to-pay, financial report generation, and month-end closing.

[0003] However, existing financial process automation systems have exposed inherent flaws when handling increasingly complex business scenarios. First, automated processes typically consist of multiple independent automation units (such as RPA scripts, API calls, and microservices) running in series or in parallel. The design of these units often focuses on the implementation of their own functions, while lacking systematic and explicit description and management of potential side effects that their execution may have on shared data resources and the status of other system modules.

[0004] Secondly, traditional process orchestration engines primarily schedule these automated units based on pre-set business logic sequences and conditional branches. They typically lack proactive awareness and proactive mitigation mechanisms for unexpected interactions and side effects that may arise between units due to concurrent execution, data contention, or implicit dependencies. When such conflicts occur, they often lead to process failures, data inconsistencies, critical business interruptions, and even serious financial risks and compliance issues.

[0005] In addition, for some automated units that inherently have high operational risks, existing systems usually rely on post-audits or limited error handling mechanisms, lacking runtime risk rehearsals and sophisticated rollback or compensation capabilities, making it difficult to effectively control potential destructive impacts. Summary of the Invention

[0006] The present invention provides a financial digital intelligent management system to solve the technical problems in the existing technology that when financial automation processes handle complex interactions, they lack effective management and foresight of the side effects of atomic capability units (ACUs), which often lead to poor system stability, high potential risks, difficult maintenance, and difficulty in achieving safe and flexible process innovation and capability reuse.

[0007] In view of the above problems, the present invention provides a financial digital intelligent management system, comprising: Atomic Capability Unit (ACU), used to decompose a financial process into one or more ACUs, and to define, capture, and store structured side effect metadata for the ACUs; A side effect metadata management module, wherein the side effect metadata describes the potential impact type, impact conditions, impact scope, and preset side effect risk level of the ACU on the data object when it is executed, and is associated with the importance level information of the data object; a dynamic process orchestration engine module, configured to generate one or more candidate ACU execution paths based on the side effect metadata of the ACU defined in the business process and the importance level information of the associated data objects, evaluate potential side effect conflicts between ACUs in the candidate ACU execution paths by analyzing the side effect metadata, quantify an expected side effect risk score based on the side effect risk level and the data object importance level information, and select and determine a final execution plan based on the optimization goal of minimizing the expected side effect risk score; An execution control and risk mitigation module is configured to selectively rehearse the ACU in an impact domain sandbox before actual execution to assess actual side effects based on the preset side effect risk level defined in the side effect metadata of the ACU or the real-time risk assessment performed by the dynamic process orchestration engine module based on the expected side effect risk score, and to perform conditional rollback or compensation operations based on the recoverability mechanism defined in the side effect metadata when the ACU actually fails to execute or produces unacceptable side effects.

[0008] Preferably, an orchestration decision logging module is further included to record in detail each decision point of the dynamic process orchestration engine module when generating candidate ACU execution paths, performing side effect conflict analysis and expected side effect risk score quantification, selecting the final execution plan, and performing dynamic path adjustment, as well as the side effect metadata summary, judgment logic, and related context information.

[0009] Preferably, it also includes: a process interaction impact knowledge graph construction and analysis module, which is used to collect and integrate the side effect metadata and the importance level information of the data objects managed by the side effect metadata management module, the orchestration decision log generated by the dynamic process orchestration engine module, and the sandbox rehearsal log and rollback event log generated by the execution control and risk mitigation module; construct a knowledge graph based on the collected and integrated data, the nodes of the knowledge graph include ACU, data objects, and business processes, the edges include the call relationship between ACUs and the impact relationship of ACU on data objects, and the attributes of the impact relationship are derived from the side effect metadata; and analyze based on the knowledge graph to identify process bottlenecks and risk hotspots, recommend ACUs that can be safely reused, or generate process reconstruction optimization suggestions.

[0010] The technical solution provided by this application has at least the following technical effects or advantages: By defining structured side effect metadata for each atomic capability unit (ACU), and using a dynamic process orchestration engine to perform forward-looking side effect conflict detection and risk-based path optimization, a large number of process execution failures and data errors caused by unexpected interactions can be proactively avoided, thereby significantly improving the success rate and operational stability of complex financial automation processes. By quantitatively assessing the side effect risks of ACUs, combining the impact domain sandbox rehearsal mechanism to pre-verify high-risk operations, and providing post-remediation through conditional rollback or compensation mechanisms when unexpected events occur during actual execution, the present invention can more effectively control the potential destructive impacts of automated processes, ensure the accuracy and consistency of financial data, and enhance the system's overall risk resistance and fault recovery capabilities.

[0011] The present invention uses structured side effect metadata and logs that record the orchestration engine's decision-making process in detail, enabling auditors or automated audit tools to clearly track and analyze the internal data dependencies, state impacts, and potential conflict points of complex processes from before, during, to after the event. This deep transparency and auditability at the interactive impact level far exceeds traditional audit methods that only focus on execution steps, and is more conducive to meeting strict financial compliance requirements and conducting accurate fault diagnosis. Clear ACU side effect statements and a risk-aware intelligent orchestration engine reduce concerns about introducing new errors due to unknown interactions, encourage bolder and more flexible business process innovation and service orchestration, and thus accelerate the accumulation and evolution of the company's overall automation capabilities.

[0012] By continuously recording and analyzing historical side effect events, sandbox rehearsal results, the orchestration engine's risk avoidance decisions, and rollback and recovery processes, this invention reveals systemic process vulnerabilities, interaction bottlenecks, and architectural improvement opportunities. These data-driven insights provide an unprecedented basis for continuous optimization, intelligent reconstruction, and iterative improvement of the ACU side effect metadata, driving the introspective evolution of the entire automation system towards greater robustness, efficiency, and controllable risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is an architecture diagram of a financial digital intelligent management system according to the present invention. DETAILED DESCRIPTION

[0014] This application provides a financial digital intelligent management system based on an automatic orchestration and risk isolation mechanism for financial processes that minimizes unexpected interactions. This aims to address technical issues in existing technologies such as poor system stability, high potential risks, difficult maintenance, and difficulty in achieving safe and flexible process innovation and capability reuse, caused by the lack of effective management and foresight of side effects of atomic capability units (ACUs) when handling complex interactions. This application significantly improves the transparency, reliability, and auditability of automated processes through structured side effect metadata, risk-aware dynamic process orchestration, and proactive risk rehearsal and mitigation mechanisms, and promotes the healthy ecological development and continuous optimization of enterprise automation capabilities.

[0015] Below, the technical solutions in this application will be described clearly and completely. Obviously, the described embodiments are only some of the embodiments of this application, rather than all of them. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the sake of ease of description, the following description may only show the parts related to the core idea of this application.

[0016] like Figure 1 As shown in the architecture diagram of a financial digital intelligent management system, the system includes the following steps: Atomic Capability Unit (ACU), used to decompose a financial process into one or more ACUs, and to define, capture, and store structured side effect metadata for the ACUs; A side effect metadata management module, wherein the side effect metadata describes the potential impact type, impact conditions, impact scope, and preset side effect risk level of the ACU on the data object when it is executed, and is associated with the importance level information of the data object; a dynamic process orchestration engine module, configured to generate one or more candidate ACU execution paths based on the side effect metadata of the ACU defined in the business process and the importance level information of the associated data objects, evaluate potential side effect conflicts between ACUs in the candidate ACU execution paths by analyzing the side effect metadata, quantify an expected side effect risk score based on the side effect risk level and the data object importance level information, and select and determine a final execution plan based on the optimization goal of minimizing the expected side effect risk score; An execution control and risk mitigation module is configured to selectively rehearse the ACU in an impact domain sandbox before actual execution to assess actual side effects based on the preset side effect risk level defined in the side effect metadata of the ACU or the real-time risk assessment performed by the dynamic process orchestration engine module based on the expected side effect risk score, and to perform conditional rollback or compensation operations based on the recoverability mechanism defined in the side effect metadata when the ACU actually fails to execute or produces unacceptable side effects.

[0017] Specifically, regarding the Atomic Capability Unit (ACU) and its side-effect metadata management module, these modules are responsible for standardizing the management of the basic automation units that comprise business processes and explicitly describing their potential system impacts. Regarding the standardized definition and packaging of ACUs, complex end-to-end financial processes (such as order-to-cash, procure-to-pay, and month-end close) are first broken down into a series of independent "Atomic Capability Units" (ACUs) with clear business inputs, outputs, and single responsibilities through business analysis and process streamlining. These ACUs can encapsulate different types of automation capabilities, such as RPA robot units that execute specific user interface operation sequences (such as logging into the system, fetching data from the user interface, filling out forms within the application, and downloading report files); service units that call APIs of internal or third-party external systems (such as querying a customer's credit rating, creating an e-invoice via an API, and executing a payment instruction via a payment gateway); manual task units that require manual approval, judgment, or data entry (such as handling process exceptions and performing high-level approval for transactions exceeding specific thresholds); and data processing units or microservices that perform specific data transformation, cleansing, and validation logic. Then, a standardized interface is defined for each type of ACU, including the input parameters required for its execution (parameter name, data type, whether it is required, default value, format verification rules, etc.), the output results after execution (result name, data type, possible business-specific error codes and error message descriptions), and a clear set of execution statuses (for example: success, failure, pending / queued, in progress, canceled, execution timeout, etc.), to ensure that ACUs of different sources and types can be uniformly managed and orchestrated.

[0018] Structured definition and capture of ACU side effect metadata: To explicitly define the potential impacts of ACU execution, a structured side effect metadata model is designed. This model details the impacts that each ACU may have on data objects in different systems, in addition to its normal business output, during execution, as well as the specific conditions and scope of these impacts.

[0019] Furthermore, the structured side effect metadata defined by the side effect metadata management module further includes at least one or more of the following: a unique identifier and an impact scope of the affected data object; the type of the affected data object, the type including reading, creating, updating, deleting, locking or unlocking; the impact conditions for generating side effects, the conditions being logically judged based on the input parameter values of the ACU; the certainty or probability of the occurrence of side effects; the idempotence identification of the side effect; and the recoverability information or compensation mechanism identification of the side effect; the side effect metadata management module is also used to manage the importance level information of the data object, and the importance level information is associated with the corresponding data object.

[0020] Specifically, the structured side effect metadata: Affected data objects: including the name or path that uniquely identifies the data entity (for example, for a database, it can be the database name.table name.column name, and can even contain a WHERE clause pattern to limit the affected record subset; for a file, it can be the full path or path pattern; for an API resource, it can be its RESTful path; for shared memory, it can be its area name; for a more abstract business object, it can be the pattern of its globally unique ID), and describes the scope of its impact (for example, whether it is for a record with a specific ID, the entire data table, or all files under a certain file directory).

[0021] Data object importance level information: This information is managed by the side effect metadata management module and is associated with the data object definition itself (for example, as defined in the data governance platform). For example, data objects can be categorized into levels such as "critical financial master data," "general transaction data," "temporary configuration data," and "log audit data." Each level has a different importance weight, which is used in subsequent risk assessments. The side effect metadata of an ACU references or is associated with the importance level of the data object it affects.

[0022] Type of impact: Clearly specify whether it is read, create, update (which can be further subdivided into overwrite update, incremental update, field-level update, etc.), delete, or lock / unlock for concurrency control, etc.

[0023] Side Effect Conditions: Describes a side effect that doesn't always occur, but only occurs when certain preconditions are met. These conditions can be logically determined based on the values of the ACU's input parameters (for example, the "Create" side effect on the HighValueTransaction_Log table occurs only when the input parameter is greater than 10,000). These conditions can be described using a simple, machine-parseable conditional expression language.

[0024] Certainty / Probability of Side Effect Occurrence: Indicate whether the side effect is guaranteed to occur (with 100% certainty) or has a certain probability of occurrence (for example, an ACU that depends on an unstable external service has a 10% probability of not actually executing its update due to a timeout, but execution was attempted). Probabilistic side effects require the basis for the probability estimate.

[0025] Idempotence of side effects: This identifies whether executing the ACU (or its specific side effects) multiple times produces the same final effect as executing it once. Idempotence is very important for designing fault tolerance and retry mechanisms.

[0026] Information on the reversibility of side effects or identification of compensation mechanisms: If the side effects of an ACU are reversible, this section should describe the compensation method or link to the ID of another ACU that performs compensation. The complexity level of the compensation operation (e.g., simple, medium, complex, or uncompensable / requires manual intervention) may also be included.

[0027] Potential conflict types are pre-analyzed by the Side Effect Metadata Management Module during ACU registration, combining the impact type and affected data objects, or declared by the developer. These indicate the specific conflict patterns that may occur between the ACU's side effects and other side effects on the same data object (e.g., write-write conflicts, read-committed write conflicts, update loss risks, etc.). This provides direct input for subsequent conflict detection in the orchestration engine.

[0028] Preset side effect risk level: When registering an ACU, the developer can make a preliminary assessment and set a risk level (for example, low, medium, or high) based on their understanding of the ACU behavior, or the system can make a preliminary assessment based on analysis of other metadata attributes (such as impact on key data objects, impact type of deletion, complex compensation mechanism, etc.).

[0029] Side effect metadata can be captured in various ways: Manual declaration: When registering an ACU, the developer or owner of the ACU manually fills in the side effect information according to a predefined metadata model through a guided user interface or structured template.

[0030] Semi-automatic extraction: Tools can be developed to preliminarily identify potential data object interactions and impact types through static code analysis (for example, analyzing database operation instructions in RPA scripts, HTTP methods and request bodies in API call code, and file I / O operations in data processing scripts). Alternatively, historical ACU execution logs (for example, database binlogs or application audit logs) can be analyzed to attempt to infer their impact on the data. Guided question-and-answer systems can also assist developers in more comprehensive thinking and metadata generation.

[0031] Test-based inference (an optional advanced feature): Execute the ACU in a controlled test environment containing pre-set data objects. By monitoring the changes in the data object state before and after the ACU execution (for example, the addition, deletion, and modification of database records, and file changes), the system attempts to automatically infer some of its side effects. This approach requires precise environment isolation and a difference comparison mechanism.

[0032] All ACU definitions and their corresponding structured side effect metadata are stored in a central metadata repository (e.g., a version-controlled database or configuration management system). This repository provides APIs for querying and updating modules such as the dynamic process orchestration engine and ACU development tools. Side effect metadata is tied to the associated ACU version, ensuring that the side effect description that matches the ACU version is used during process orchestration.

[0033] Specifically, the dynamic process orchestration engine module is responsible for intelligently determining the execution order and method of ACU based on business needs and risk assessment results.

[0034] In terms of process definition and goal declaration, rather than hard-coding the execution order of ACUs, users or process designers declaratively define the business process's objectives (for example, "Complete the generation and verification of monthly financial reports"), the key business steps involved in the process (which can be mapped to one or more candidate ACUs), and key business constraints (for example, the entire process must be completed within two business days, specific approval steps must be performed by the finance manager role, and costs must not exceed the preset budget). This declarative definition leaves room for the dynamic process orchestration engine to optimize the process based on real-time conditions and risk assessments.

[0035] Process context information acquisition: When receiving a request to execute a process instance, the engine proactively retrieves or receives contextual information related to the current process. This information includes: business input data passed in when the process was started (for example, the ID for processing a specific purchase order and related invoice information); current system status (for example, the current health and average response time of the target business system API, the CPU and I / O load of the relevant database server, and whether there are planned system maintenance windows); the status of shared resources (for example, whether a critical shared file used for data exchange is currently locked by another process, the status of a semaphore used for concurrency control); and the identity, permissions, and role information of the user executing the process instance, which is used for subsequent permission verification and manual task allocation.

[0036] Furthermore, in terms of candidate execution path generation and side effect risk assessment: the dynamic process orchestration engine module evaluates the potential side effect conflicts between ACUs in the candidate ACU execution path by analyzing the side effect metadata, and quantifies an expected side effect risk score in combination with the preset side effect risk level and data object importance level information, including: identifying whether there is a write-write conflict, read-write conflict or write-read conflict between ACUs that execute the same data object identifier and overlapping range simultaneously or successively in the candidate ACU execution path; for each identified potential side effect conflict, a single point conflict risk value is calculated based on the type of conflict, the importance level information of the associated data object, the preset side effect risk level defined in the ACU side effect metadata, the certainty of the occurrence of the side effect, and the recoverability information; all single point conflict risk values in the candidate ACU execution path are accumulated or aggregated to obtain the expected side effect risk score.

[0037] Specifically, the logic of candidate path generation and risk assessment: First, the engine generates one or more logically feasible ACU execution paths or partial paths based on the logical dependencies between business steps in the declarative process definition (for example, A must precede B, C and D can be executed in parallel, and E requires the output of A and C as input) and the preconditions of each ACU itself (from its metadata). Then, for each candidate execution path (especially those path segments that contain a set of ACUs that can be executed in parallel), the engine uses the ACU side effect metadata stored in the metadata warehouse to perform in-depth analysis to identify potential side effect conflicts. The logic for conflict identification includes: Traverse ACU pairs in the path, especially those ACUs that are scheduled to execute in parallel, or ACUs on different branches that may concurrently access shared resources.

[0038] For each pair (or groups) of ACUs that may interact, compare the "affected data objects" lists in their side effect metadata.

[0039] If it is found that they affect the same (or overlapping) data object, it is further determined whether there is a conflict based on the "type of impact", for example: Write-write conflict: Multiple ACUs attempt to perform "create", "update", or "delete" operations on the same data object.

[0040] Read-write (or write-read) conflict: An ACU attempts to "read" a data object while another ACU performs a "create", "update", or "delete" operation on the data object at the same time or during the critical window of the read operation (for example, after the read and before a decision based on the read result is made).

[0041] Lost Update Risk: Identify patterns that cause an update by one ACU to be overwritten by a subsequent update by another concurrent ACU.

[0042] Data inconsistency risk: Identify the risk of temporary or permanent inconsistency between multiple related data objects due to improper ACU execution order or lack of synchronization.

[0043] After identifying potential side effect conflicts, the engine quantifies the risk of each conflict point and the entire candidate path, calculating an "expected side effect risk score." This score takes into account the following factors: The base severity of the conflict type: for example, a write-write conflict is generally more severe than a read-write conflict.

[0044] The importance level of the data objects involved: Conflicts involving critical financial master data (such as general ledger accounts and customer master files) carry a significantly higher risk weight than conflicts involving temporary data. This information is obtained from the importance levels associated with the data objects, which are managed by the Side Effect Metadata Management module.

[0045] Preset side effect risk levels defined in the ACU side effect metadata: If a side effect of an ACU is itself marked as high risk, any conflicts it participates in will have their risk score increased.

[0046] Certainty / probability of side effects: For probabilistic side effects, their risk contribution is adjusted according to their probability of occurrence.

[0047] Information on the recoverability of side effects: If a high-risk side effect has a simple and reliable compensation mechanism, its contribution to the overall pathway risk score will be moderately reduced; conversely, if the side effect is difficult to recover or the compensation cost is extremely high, the risk score will be significantly increased.

[0048] Impact Scope: Side effects that affect a large number of records are generally more risky than those that affect a single record. This can be calculated by assigning weights and scores to each factor, then summing the single-point conflict risk values calculated for all potential conflict points along the path, taking a weighted average, or employing other more complex aggregation models (for example, considering correlations between risks).

[0049] Furthermore, in terms of execution path selection and optimization based on "minimizing expected side effects": the dynamic process orchestration engine module selects and determines a final execution plan based on the optimization goal of minimizing the expected side effect risk score, including: when the expected side effect risk score exceeds a preset first risk threshold, attempting to adjust the execution order of the ACU in the candidate ACU execution path, or introducing an ACU corresponding to the synchronization mechanism between the ACUs with potential side effect conflicts, or selecting an alternative ACU with equivalent functionality but lower side effect risk, to generate an adjusted candidate ACU execution path and re-evaluate its expected side effect risk score; if after adjustment, the expected side effect risk score still exceeds the preset second risk threshold, or there is no effective adjustment plan, then triggering the execution control and risk mitigation module to perform an impact domain sandbox rehearsal on the ACU whose side effect metadata is defined as having the preset side effect risk level of high risk, or introducing a manual intervention node in the final execution plan.

[0050] Specifically, the path selection and optimization logic: The dynamic process orchestration engine's scheduling algorithm uses "minimizing the expected side effect risk score" as one of its core optimization goals, while also considering other business objectives such as process execution efficiency (total duration), resource utilization, and meeting all business constraints in the process definition. The decision-making process includes the following: For all candidate execution paths, the expected side effect risk score and other evaluation objectives (such as expected execution time) are calculated.

[0051] Use multi-objective optimization decision-making methods (for example, setting weights for each objective for weighted scoring, or adopting a Pareto optimal selection strategy) to preliminarily screen out one or several better paths.

[0052] For the selected pathway, check whether its “expected side effect risk score” is lower than a preset “acceptable risk threshold” (first risk threshold).

[0053] If the score exceeds this threshold, the engine will actively try to optimize the path to reduce risk: Adjust execution order: For parallelizable ACU sets in a path, if their concurrent execution results in a high risk of conflict, the engine attempts to adjust their execution order from parallel to serial, and selects the serial order that minimizes conflicts or reduces risks.

[0054] Introducing ACUs corresponding to the synchronization mechanism: If the conflict is mainly caused by concurrent access to shared resources, the engine can automatically insert an ACU of the acquisition type before the ACU accessing the conflicting resource, and insert an ACU of the release type after its operation is completed. These lock ACUs are also standardized ACUs with their own side effect metadata.

[0055] Select an alternative ACU: If there is an alternative ACU registered in the metadata repository that is functionally equivalent (or can meet the current business step requirements) but whose side effect metadata indicates that its "preset side effect risk level" is lower or has a smaller impact range, the engine can try to replace the original high-risk ACU in the path with the alternative ACU.

[0056] After each adjustment, the engine recalculates the risk score of the adjusted path.

[0057] If, after a series of automatic adjustments, the path's risk score still exceeds a higher "critical risk threshold" (secondary risk threshold), or the system determines that no acceptably low-risk path can be found through automatic adjustments, the engine takes a more cautious approach: Triggering sandbox rehearsal: For the ACUs with the highest risk in the path (especially those whose "preset side effect risk level" in the side effect metadata is marked as "high"), it is mandatory to perform an impact domain sandbox rehearsal before actual execution.

[0058] Introducing a manual intervention node: In the final execution plan, a manual task unit is automatically inserted before high-risk operations or at decision points, presenting the risk assessment results and optional processing solutions (for example, continue execution, select an alternative solution, abort the process) to operators in designated roles, who then make the final decision manually.

[0059] Select a predefined "fail-safe" path: If the process definition contains a known "safe mode" or "fallback" execution path that has minimal side effects but may also be less efficient, the engine can select that path.

[0060] In extreme cases, if no execution plan with acceptable risk can be found, the dynamic process orchestration engine can also decide to suspend or refuse to execute the process instance and record the detailed reasons.

[0061] Ultimately, the dynamic process orchestration engine will output a risk-assessed and optimized, specific ACU execution sequence and related scheduling instructions to form the final execution plan.

[0062] Furthermore, in terms of orchestration decision logging: the orchestration decision logging module is used to record in detail each decision point of the dynamic process orchestration engine module when generating candidate ACU execution paths, performing side effect conflict analysis and expected side effect risk score quantification, selecting the final execution plan, and performing dynamic path adjustment, as well as the side effect metadata summary, judgment logic, and related context information.

[0063] Specifically, the orchestration decision logging module records key information throughout the orchestration engine's decision-making process, enabling in-depth auditing of interaction impacts. Logs should include at least the process instance ID, timestamp, received process definition summary, a snapshot of the initial context, the structure of each generated candidate execution path, the expected side effect risk score calculated for each candidate path and its detailed components (i.e., which potential conflict points contribute how much risk), the evaluation of other optimization objectives (such as time and cost), the path ultimately selected for execution, and the rationale for that selection (e.g., because it had the best overall score or its risk score was below a certain threshold). If a path is dynamically adjusted, the path and risk before the adjustment are recorded, along with the specific adjustment measures taken (e.g., changing ACU-A and ACU-B from parallel to serial due to a detected write-write conflict on data object X), the adjusted path and risk, and the rationale for triggering sandbox rehearsals or introducing manual intervention. These logs are not only useful for post-audit and troubleshooting, but also provide a deeper understanding of why the system performs as it does.

[0064] Specifically, regarding the execution control and risk mitigation module: this module provides a runtime safety barrier for the actual execution of the ACU, with particular attention paid to those known or potential high-risk operations.

[0065] Furthermore, in terms of the risk-triggered sandbox rehearsal mechanism: the execution control and risk mitigation module rehearses the ACU in the impact domain sandbox, including: judging whether the preset sandbox trigger threshold is exceeded based on the preset side effect risk level defined in the side effect metadata of the ACU or the real-time risk assessment result of the dynamic process orchestration engine module, and if so, triggering the sandbox rehearsal; building a temporary sandbox environment isolated from the production environment for the ACU to be rehearsed, the sandbox environment containing copies or simulation objects of the data objects required to be affected by the execution of the ACU; simulating the execution of the ACU in the sandbox environment, and monitoring and recording the actual side effect list generated by it; comparing the actual side effect list with the side effect metadata predefined by the ACU. If it is found that the side effect is not declared in the metadata, or the actual impact of the declared side effect exceeds the preset acceptable range, the rehearsal is determined to have failed and the ACU is prevented from being executed in the actual environment or triggering further risk response processes.

[0066] Specifically, the logic of the sandbox preview: Trigger condition judgment: After the orchestration engine determines the final execution plan and is ready to schedule an ACU for execution, the execution control and risk mitigation module will check whether a sandbox rehearsal is required for the ACU. The trigger conditions mainly include: In the side effect metadata of this ACU, the “preset side effect risk level” is marked as “high”.

[0067] In a previous risk assessment, the dynamic process orchestration engine determined that the expected side effect risk score of the ACU (or the interactions it participates in) exceeded a preset "sandbox trigger threshold."

[0068] The ACU is newly registered or has a recent history of failing or producing unusual side effects in similar contexts.

[0069] The side effect metadata of this ACU indicates that it will perform "update" or "delete" operations on multiple "key data objects", or its impact range is expected to be large.

[0070] Isolation environment construction and simulation execution: Once a sandbox rehearsal is triggered, the system will attempt to build a temporary "impact domain sandbox" environment for the ACU to be rehearsed, which is logically isolated (or even physically isolated, if conditions permit) from the production environment. The construction strategy for this environment may include: For database interactions: use the database's snapshot technology to create a writable copy of the relevant tables or data subset; or redirect the operation to a temporary database instance pre-populated with relevant simulated data.

[0071] For API calls: redirect calls to pre-configured mock services or test stubs that simulate real API behavior.

[0072] For file system operations: They are performed in a temporary, restricted directory that contains a copy of the required file.

[0073] The key is that the sandbox environment needs to contain copies or simulation objects of the input data and dependent data objects necessary for ACU execution and related to the current process context, so that the ACU can "realistically" simulate its execution logic in it.

[0074] Impact Assessment and Result Feedback: The ACU runs in a sandbox environment with the same parameters as in actual execution. The module monitors all ACU I / O operations and modifications to mock data objects within the sandbox (for example, by comparing pre- and post-execution data snapshots, analyzing the sandbox database's transaction logs, and recording the parameters and number of calls to the mock API). This generates a "list of actual side effects." The structure of this list aligns with the side effect metadata model.

[0075] Decision and feedback: The "actual side effect list" captured by the sandbox rehearsal is compared in detail with the side effect metadata predefined by the ACU in the metadata warehouse.

[0076] If the ACU fails to execute in the sandbox, the failure reason is recorded and the rehearsal is judged as a failure.

[0077] If the execution succeeds, the following checks are performed: Are there any new side effects not declared in the metadata ("unexpected side effects")? If so, evaluate the type and potential risk of these unexpected side effects. Are there any side effects declared in the metadata, but their actual impact scope, impact level, or specific data values affected exceed the preset "acceptable range" or safety threshold for such side effects (for example, the metadata declares that 1-10 records will be updated, but 100 records are actually updated in the sandbox)? If high-risk "unexpected side effects" occur, or "expected side effects" exceed the acceptable range, the rehearsal will be judged as a failure.

[0078] The rehearsal is considered successful only when the ACU in the sandbox is successfully executed and all actual side effects are within the scope declared in its metadata and do not exceed the preset acceptable impact threshold.

[0079] The rehearsal results (success / failure, failure reason, actual side effects, differences with metadata, etc.) are fed back to the dynamic process orchestration engine. If the rehearsal fails, the engine typically prevents the ACU from executing in the actual production environment and may trigger an alarm, try to select an alternative ACU (if one has not been tried before), or transfer the process to manual processing.

[0080] Sandbox rehearsal logs: Detailed records of the sandbox's triggering conditions, build parameters, simulation execution process, actual captured side effects, comparison results with metadata, and final decisions. These logs are a crucial source of data for "unexpected technical effects C" (data-driven continuous process optimization and intelligent reconstruction).

[0081] Furthermore, in terms of conditional rollback and compensation mechanism: the execution control and risk mitigation module performs conditional rollback or compensation operations, including: monitoring the execution status of the ACU in the actual environment, and triggering the rollback or compensation mechanism when it is detected that the ACU execution fails and its side effect metadata indicates that side effects have been generated, or when the ACU execution is successful but the actual side effects generated exceed the preset acceptable safety range after evaluation; according to the recoverability information or compensation mechanism identifier defined in the side effect metadata of the ACU, select and schedule the execution of the corresponding compensation ACU, or perform a data snapshot recovery operation, or generate a manual intervention task for manual recovery.

[0082] Specifically, the logic of conditional rollback and compensation: Rollback trigger condition monitoring: During or after the actual execution of the ACU, the execution control and risk mitigation module monitors its execution status and (where possible) the actual side effects. The conditions that trigger rollback or compensation include: The ACU explicitly returns a "failed" status after execution, and its side effect metadata indicates that the ACU may have generated some side effects before failure, and these side effects were not properly handled by the ACU itself (for example, partial success of non-transactional operations).

[0083] ACU execution timed out and may have produced undefined side effects.

[0084] Although the ACU returns a "success" status, independent monitoring mechanisms (for example, the verification ACU of key data, or external audit log analysis) detect that the actual side effects it produces seriously deviate from the scope declared in its metadata and reach a preset "unacceptable actual side effect threshold" or "acceptable safety range".

[0085] Subsequent ACUs fail due to erroneous side effects of the current ACU, and the error can be clearly traced back to the current ACU.

[0086] Rollback strategy selection and execution: Once the rollback / compensation mechanism is triggered, the module will query the "recoverability information or compensation mechanism identifier" defined in the current ACU side effect metadata.

[0087] If the identifier points to one or more "compensating ACU IDs," the dynamic process orchestration engine is requested to schedule the execution of these compensating ACUs. The compensating ACU's input parameters may need to be constructed from the original ACU's input, the execution context, or recorded side effect information. The compensating ACU itself should also be a standard ACU with its own side effect metadata (ideally, the side effects of the compensating ACU should be idempotent and low-risk).

[0088] If the identifier points to a "data snapshot recovery" mechanism and the system supports snapshot rollback of the affected data objects, the module will issue an instruction to the data management system, requesting that the relevant data be restored to a consistent snapshot point before the ACU execution.

[0089] If the flag indicates that "manual handling" is required or there is no clear automatic compensation mechanism defined, the system will pause the relevant process (or at least the affected branch), create a manual intervention task containing detailed error information, side effects that have occurred, and potential risk assessment, and notify relevant business or technical support personnel to intervene.

[0090] If a sequence of compensating ACUs (such as a chain of compensating transactions in the Saga pattern) is defined, the engine executes them sequentially in the predefined order. If the compensation operation itself fails, the problem is usually escalated to a higher level of manual intervention.

[0091] Rollback result logging and notification: This records in detail the reason for the rollback, the selected rollback strategy, the execution process and results of the compensation ACU (success / failure, side effects), the status of the data recovery operation, and the final rollback results. This information is notified to the process owner and system administrator. These records also serve as the data source for "unintended technical effects C."

[0092] Furthermore, the process interaction impact knowledge graph construction and analysis module is used to collect and integrate the side effect metadata and importance level information of the data objects managed by the atomic capability unit (ACU) and the side effect metadata management module, the orchestration decision log generated by the dynamic process orchestration engine module, and the sandbox rehearsal log and rollback event log generated by the execution control and risk mitigation module; construct a knowledge graph based on the collected and integrated data, the nodes of the knowledge graph include ACU, data objects, and business processes, the edges include the call relationship between ACUs and the impact relationship of ACU on data objects, and the attributes of the impact relationship are derived from the side effect metadata; and perform analysis based on the knowledge graph to identify process bottlenecks and risk hotspots, recommend ACUs that can be safely reused, or generate process reconstruction optimization suggestions.

[0093] Specifically, the construction and analysis logic of the knowledge graph: This module structures and integrates and deeply analyzes various types of data on ACU behavior, side effects, orchestration decisions, and risk events generated during system operation, thereby extracting insights for continuous optimization and risk warning.

[0094] Data collection and integration: Periodically or in real time, obtain the latest ACU definitions, side effect metadata, and importance level information of data objects from the Atomic Capability Unit (ACU) and Side Effect Metadata Management Module; obtain detailed decision-making process logs of the dynamic process orchestration engine from the Orchestration Decision Logging Module; obtain detailed logs of all sandbox rehearsals (including triggering conditions, side effects of simulated execution, differences from expectations, final decisions, etc.) and detailed records of all conditional rollback and compensation events from the Execution Control and Risk Mitigation Module.

[0095] Knowledge graph construction: Node definition: The core node types in the graph include: Atomic Capability Unit (ACU) nodes (including their version, type, functional description, side effect metadata summary, historical execution statistics such as success rate and average risk, etc.); data object nodes (including their unique identifier, type, importance level, access and modification frequency, etc.); business process definition nodes (including their version, target, main ACU sequence included, etc.); and may also include risk event nodes, orchestration strategy nodes, etc.

[0096] Edge definition and attributes: The edges between nodes represent the relationship between them, for example: The "call" or "sequence" relationship between ACUs (derived from the process definition and execution plan).

[0097] The ACU "produces side effects on" the relationship between data objects (derived from the side effect metadata, the edge attributes can describe the impact type, conditions, scope, risk level, etc. in detail).

[0098] Potential “dependency” relationships between data objects (possibly inferred by analyzing the commonly affected ACUs).

[0099] An ACU "belongs" to a business process.

[0100] Orchestration decisions "affect" the ACU execution order.

[0101] Sandbox rehearsal or rollback events are "associated" with specific ACUs and data objects.

[0102] Dynamic Updates: The knowledge graph dynamically updates its node and edge information as new ACUs are registered, processes are executed, orchestration decisions are made, and sandbox / rollback events occur. For example, the "interaction frequency" or "average risk score" attributes of an edge are adjusted based on new execution data.

[0103] Spectrum analysis and application: Interaction impact path analysis: Use graph traversal algorithms (such as DFS and BFS, where edge weights can be based on side effect risk or interaction frequency) to analyze the upstream and downstream chain impact scope and path that may occur when an ACU is executed or the state of a data object changes.

[0104] Identification of process bottlenecks and risk hotspots: By analyzing the graph's topological structure (such as node degree, centrality, and clustering coefficient) and node / edge attributes (such as ACUs that frequently trigger orchestration adjustments, data objects that frequently cause side effect conflicts, and execution path segments with high cumulative risk scores), potential bottlenecks and systemic risk hotspots in the process are identified.

[0105] Automation capability reuse recommendations: When a new automation requirement arises, the knowledge graph is searched for existing ACUs that match the required functionality and the desired level of side effect control. Recommendations are made based on the required functionality and the desired level of side effect control. These ACUs have matching functionality and side effect metadata indicating that they are risk-controllable in the new context. These recommendations are based on historical execution stability and reuse compatibility.

[0106] Process refactoring suggestion generation: Based on long-term analysis of historical side effect risks, orchestration decisions, and sandbox / rollback events, the system can generate data-driven process optimization suggestions. For example: It is recommended to split up those "giant" ACUs whose side effects are too complex or that frequently trigger high-risk alarms.

[0107] It is recommended to introduce stricter access control policies or optimize the data models for data objects that often become the focus of concurrent access conflicts.

[0108] It is recommended to redesign process bottleneck segments that always require a lot of "side effect avoidance" logic (such as forced serialization and frequent locking) during orchestration.

[0109] Identify and flag ACUs with inaccurate side effect metadata definitions (frequently inconsistent with sandbox rehearsal results or actual side effects), prompting them to make corrections. These analysis results provide strong technical support for achieving "unexpected technical effect B" (promoting the safe development of the composable automation capability ecosystem) and "unexpected technical effect C" (providing a data-driven foundation for the continuous optimization and intelligent reconstruction of automation processes).

[0110] The financial digital intelligent management system provided by this application has the following beneficial effects: Through the collaborative work of the aforementioned modules, this system can significantly improve the inherent stability and reliability of financial automation processes. Through forward-looking side effect awareness and dynamic risk avoidance orchestration, it effectively reduces execution failures and financial data errors caused by unexpected interactions. Through impact domain sandbox rehearsals and conditional rollback and compensation mechanisms, it provides effective runtime protection and post-event recovery capabilities for high-risk financial operations, enhancing the overall risk control level of complex financial processes.

[0111] In one embodiment, the intelligent automatic payment approval system for small purchase orders relies primarily on a digital financial intelligent management system. Its core components include the Atomic Capability Unit (ACU), a side effect metadata management module, a dynamic process orchestration engine module, and an execution control and risk mitigation module. The following uses the processing of a specific small purchase order (order number: PO20240524001, amount: 800 yuan, supplier: Acme Supplies) as an example to illustrate the system's operational mechanisms.

[0112] Step 1: ACU definition and side effect metadata configuration: The system pre-defines and registers the ACUs and their side effect metadata required to process the small purchase order payment approval process: ACU1:Verify_PO_Details (Verify purchase order details): Functional description: Based on the entered purchase order number (PO20240524001), query the order amount, supplier information, material details, etc. from the order management system (OMS) through the API, and verify whether the order amount is less than the preset small purchase threshold (such as 1,000 yuan) and whether the supplier is in the qualified supplier list.

[0113] Example of side effect metadata: Affected data object 1: OMS_DB.PurchaseOrders_Table (OMS database purchase order table), impact type 1: Read.

[0114] Affected data object 2: Supplier_DB.ApprovedVendors_Table (Qualified Vendor Table in the Supplier Database), impact type 2: Read, preset side effect risk level: Low, data object importance level: OMS Purchase Order Table - Medium; Qualified Vendor Table - Medium.

[0115] ACU2:Request_Minor_Approval (Request for primary approval): Function description: For small purchase orders that have passed verification, an approval task is created for a designated junior approver (such as Alice) on the company's internal approval platform (such as the OA system), and the order details (PO20240524001) are pushed to Alice.

[0116] Example of side effect metadata: Affected data object 1: OA_System.ApprovalTasks_Table (OA system approval task table), impact type 1: Create (Create) - create a new approval task record.

[0117] Affected data object 2: Notification_Service.User_Alice_Inbox (Notification service Alice's inbox), impact type 2: Create - send approval notification, preset side effect risk level: low, data object importance level: Approval task table - medium; Notification inbox - low, ACU3:Update_ERP_Payable (Update ERP payable status): Functional description: After receiving Alice's "Approve" decision on PO20240524001, call the ERP system through the API to update the payment status of the purchase order to "Pending Payment" and record the expected payment date.

[0118] Example of side effect metadata: Affected data object 1: ERP_DB.AccountsPayable_Table (ERP database accounts payable table), impact type 1: Update - Update the PaymentStatus field of the corresponding record of PO20240524001 to 'PendingPayment', and update the ScheduledPaymentDate field. Impact condition 1: Approval decision is "Approve".

[0119] Default side effect risk level: high (because core ERP financial data is directly modified), data object importance level: accounts payable table - very high, recoverability / compensation mechanism identifier: ACU_Comp_ReversePayableUpdate (a predefined compensation ACU used to undo ERP payable status updates).

[0120] ACU4:Log_Audit_Trail (record audit trail): Function description: After ACU3 successfully updates the ERP payable status, a detailed audit record is written to the central audit log system, including the purchase order number PO20240524001, the approver Alice, the operation time, the updated ERP status, etc.

[0121] Example of side effect metadata: Affected data object 1: AuditLog_DB.Financial_Transactions_Log (Audit Log Database Financial Transaction Log Table); Impact type 1: Create - inserts a new audit log record.

[0122] Preset side effect risk level: low (but write failure will affect compliance), data object importance level: financial transaction log table - very high.

[0123] Step 2: Process startup and dynamic orchestration When purchase order PO20240524001 is submitted to the system for payment approval, the dynamic process orchestration engine module starts working: Process definition: Verification -> (if passed) -> Approval -> (if approved) -> Update ERP -> (if successful) -> Record audit.

[0124] Context information: Get order number PO20240524001.

[0125] Candidate execution path generation: For this linear process, the main candidate path is ACU1 -> ACU2 -> ACU3-> ACU4 (based on the output and conditions of each ACU).

[0126] Side effect risk assessment: The engine analysis showed that the risk of side effects of ACU1 and ACU2 was low.

[0127] The analysis focuses on ACU3 (Update_ERP_Payable): its side effect metadata indicates that its risk level is "high" and it affects data objects of "very high" importance.

[0128] The engine evaluates the interaction between ACU3 and ACU4: ACU4 depends on the successful execution of ACU3, and they affect different systems. The risk of direct side effect conflicts is not high, but the failure of ACU3 will affect the execution of ACU4.

[0129] The engine calculates an "expected side effect risk score" for the entire pathway. Assume that due to the high-risk nature of ACU3, the score exceeds the preset "first risk threshold" but does not reach the "second risk threshold."

[0130] Execution path selection and optimization: Since the risk score is high, the orchestration engine attempts to optimize the path according to the logic of claim 4.

[0131] In this simple linear process, adjusting the order or introducing synchronization mechanisms is of little significance. However, the engine identifies ACU3 as the main risk point.

[0132] The engine decided that before executing ACU3, an "Influence Domain Sandbox Rehearsal" needed to be triggered.

[0133] The final execution plan is determined as: ACU1 -> ACU2 -> (Sandbox preview ACU3) -> (If the preview is successful) ACU3 -> ACU4.

[0134] Step 3: Implement controls and risk mitigation: The execution control and risk mitigation module schedules according to the final execution plan: Execute ACU1 (Verify_PO_Details). Assume that PO20240524001 passes verification and outputs the order details.

[0135] Execute ACU2 (Request_Minor_Approval). Push the task to Alice. Alice approves it with "Approved."

[0136] Sandbox preview ACU3 (Update_ERP_Payable): Triggered: The ACU3 side effect risk level is "high", or the orchestration engine assesses the risk to reach the sandbox trigger threshold.

[0137] Build a sandbox: The system creates a temporary, isolated ERP database environment copy for ACU3 (or uses a highly simulated Mock service), which contains the relevant simulated data of PO20240524001.

[0138] Simulated execution: ACU3 attempts to update the payment status of PO20240524001 with an "Approved" decision in the sandbox.

[0139] Impact Assessment: Monitoring found that the PaymentStatus of PO20240524001 in the sandbox was correctly updated to 'PendingPayment', the ScheduledPaymentDate was also correctly filled in, and no other unrelated data was accidentally modified.

[0140] Comparison and Decision: The actual side effects are consistent with the ACU3 side effect metadata and are within acceptable limits. The preview was deemed successful.

[0141] Actual execution of ACU3 (Update_ERP_Payable): Due to the successful sandbox rehearsal, ACU3 is allowed to be executed in the real ERP system. Assume that the execution is successful.

[0142] Execute ACU4 (Log_Audit_Trail). Successfully write records to the audit log system.

[0143] The process instance ended successfully.

[0144] Assume that in step 4 of step 3 above, ACU3 (Update_ERP_Payable) fails to execute due to a sudden network connection timeout in the ERP system. However, its internal logic may have sent an update instruction to ERP before the timeout, resulting in partial or undefined side effects (for example, the status may have been updated, but ACU failed to receive a successful confirmation).

[0145] Triggering a rollback: The execution control and risk mitigation module detects the failure of ACU3 execution and determines that a rollback / compensation needs to be triggered based on its side effect metadata ("high risk", "compensation mechanism identifier: ACU_Comp_ReversePayableUpdate").

[0146] Select and execute the compensation ACU: The module schedules the execution of the predefined compensation ACUACU_Comp_ReversePayableUpdate. The logic of this ACU is to safely check the current status of PO20240524001 in ERP. If it has been incorrectly updated to "pending payment", it will be restored to the status before approval (for example, "approved pending payment") or marked as "payment processing exception".

[0147] Record and notify: Record the failure of ACU3, the triggered rollback operation, and the execution results of the compensation ACU in detail, and notify the financial operations staff and system administrator to pay attention to the payment status of PO20240524001, which may require manual verification.

[0148] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0149] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A financial digital intelligent management system, characterized in that: include: Atomic Capability Unit (ACU), used to decompose a financial process into one or more ACUs, and to define, capture, and store structured side effect metadata for the ACUs; A side effect metadata management module, wherein the side effect metadata describes the potential impact type, impact conditions, impact scope, and preset side effect risk level of the ACU on the data object when it is executed, and is associated with the importance level information of the data object; a dynamic process orchestration engine module, configured to generate one or more candidate ACU execution paths based on the side effect metadata of the ACU defined in the business process and the importance level information of the associated data objects, evaluate potential side effect conflicts between ACUs in the candidate ACU execution paths by analyzing the side effect metadata, quantify an expected side effect risk score based on the side effect risk level and the data object importance level information, and select and determine a final execution plan based on the optimization goal of minimizing the expected side effect risk score; An execution control and risk mitigation module is configured to selectively rehearse the ACU in an impact domain sandbox before actual execution to assess actual side effects based on the preset side effect risk level defined in the side effect metadata of the ACU or the real-time risk assessment performed by the dynamic process orchestration engine module based on the expected side effect risk score, and to perform conditional rollback or compensation operations based on the recoverability mechanism defined in the side effect metadata when the ACU actually fails to execute or produces unacceptable side effects.

2. A financial digital intelligent management system according to claim 1, characterized in that: The structured side effect metadata defined by the side effect metadata management module further includes at least one or more of the following: a unique identifier and an impact scope of the affected data object; a type of the affected data object, the type including read, create, update, delete, lock or unlock; an impact condition for generating a side effect, the condition being logically judged based on the input parameter value of the ACU; the certainty or probability of the side effect; and an idempotence identifier of the side effect. and information on the recoverability of side effects or identification of compensation mechanisms; The side effect metadata management module is further configured to manage importance level information of the data objects, where the importance level information is associated with the corresponding data objects.

3. A financial digital intelligent management system according to claim 1, characterized in that: The dynamic process orchestration engine module evaluates the potential side effect conflicts between ACUs in the candidate ACU execution path by analyzing the side effect metadata, and quantifies an expected side effect risk score based on the preset side effect risk level and data object importance level information, including: identifying whether there is a write-write conflict, read-write conflict, or write-read conflict between ACUs that execute the same data object identifier and overlapping range simultaneously or successively in the candidate ACU execution path; for each identified potential side effect conflict, a single point conflict risk value is calculated based on the type of the conflict, the importance level information of the associated data object, the preset side effect risk level defined in the ACU side effect metadata, the certainty of the side effect occurrence, and the recoverability information; and accumulating or aggregating all single point conflict risk values in the candidate ACU execution path to obtain the expected side effect risk score.

4. A financial digital intelligent management system according to claim 1, characterized in that: The dynamic process orchestration engine module selects and determines a final execution plan based on the optimization goal of minimizing the expected side effect risk score, including: when the expected side effect risk score exceeds a preset first risk threshold, attempting to adjust the execution order of the ACU in the candidate ACU execution path, or introducing an ACU corresponding to the synchronization mechanism between the ACUs with potential side effect conflicts, or selecting an alternative ACU with equivalent functionality but lower side effect risk, to generate an adjusted candidate ACU execution path and re-evaluate its expected side effect risk score; if after adjustment, the expected side effect risk score still exceeds the preset second risk threshold, or there is no effective adjustment plan, triggering the execution control and risk mitigation module to perform an impact domain sandbox preview on the ACU whose preset side effect risk level is defined as high risk in the side effect metadata, or introducing a manual intervention node in the final execution plan.

5. A financial digital intelligent management system according to claim 1, characterized in that: The execution control and risk mitigation module rehearses the ACU in the impact domain sandbox, including: judging whether the preset side effect risk level is exceeded based on the preset side effect risk level defined in the side effect metadata of the ACU or the real-time risk assessment result of the dynamic process orchestration engine module, and if so, triggering the sandbox rehearsal; building a temporary sandbox environment isolated from the production environment for the ACU to be rehearsed, and the sandbox environment contains copies or simulation objects of the data objects required to be affected by the execution of the ACU; simulating the execution of the ACU in the sandbox environment, and monitoring and recording the actual side effect list generated by it; comparing the actual side effect list with the side effect metadata predefined by the ACU. If a side effect is found that is not declared in the metadata, or the actual impact of the declared side effect exceeds the preset acceptable range, the rehearsal is determined to have failed and the ACU is prevented from being executed in the actual environment or triggering further risk response processes.

6. A financial digital intelligent management system according to claim 1, characterized in that: The execution control and risk mitigation module performs conditional rollback or compensation operations, including: monitoring the execution status of the ACU in the actual environment, and triggering a rollback or compensation mechanism when it is detected that the ACU execution fails and its side effect metadata indicates that side effects have been generated, or when the ACU execution is successful but the actual side effects generated exceed the preset acceptable safety range after evaluation; based on the recoverability information or compensation mechanism identifier defined in the side effect metadata of the ACU, selecting and scheduling the execution of the corresponding compensation ACU, or performing a data snapshot recovery operation, or generating a manual intervention task for manual recovery.

7. A financial digital intelligent management system according to claim 1, characterized in that: It also includes an orchestration decision logging module for recording in detail each decision point of the dynamic process orchestration engine module when generating candidate ACU execution paths, performing side effect conflict analysis and expected side effect risk score quantification, selecting the final execution plan, and performing dynamic path adjustment, as well as the side effect metadata summary, judgment logic, and related context information.

8. A financial digital intelligent management system according to claim 1, characterized in that: Also includes: A process interaction impact knowledge graph construction and analysis module is used to collect and integrate the side effect metadata and importance level information of the data objects managed by the side effect metadata management module, the orchestration decision log generated by the dynamic process orchestration engine module, and the sandbox rehearsal log and rollback event log generated by the execution control and risk mitigation module; construct a knowledge graph based on the collected and integrated data, the nodes of the knowledge graph include ACUs, data objects, and business processes, the edges include the call relationships between ACUs and the impact relationships of ACUs on data objects, and the attributes of the impact relationships are derived from the side effect metadata; and analyze based on the knowledge graph to identify process bottlenecks and risk hotspots, recommend ACUs that can be safely reused, or generate process reconstruction optimization suggestions.

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