Comprehensive management method and system for enterprise data

Abstract

The invention provides a comprehensive management method and system for enterprise data, and belongs to the technical field of data management, and the method specifically comprises the steps: determining a business type, belonging to a fusion optimization processing target, of a business system, and determining a change monitoring target in the business system in combination with fusion optimization processing target data in the business type, monitoring the change condition of the service data by using a change monitoring target, and determining a fusion processing strategy of the service data in the service type by using a fusion processing method of change monitoring target data in the service type and the service data of the change monitoring target, according to the fusion processing strategy of the business data of the change monitoring target in the different business types, the monitoring management method of the change monitoring target is determined, and the difficulty of calling and processing the business types of an enterprise is reduced.

Description

Technical Field

[0001] This invention belongs to the field of data management technology, and in particular relates to a comprehensive management method and system for enterprise data. Background Technology

[0002] Currently, during their digital transformation, enterprises commonly deploy multiple heterogeneous business systems (such as ERP, CRM, SCM, MES, HRM, OA, etc.), forming a typical "system silo" architecture. This coexistence of multiple systems has led to systemic technical bottlenecks and management challenges.

[0003] To address the aforementioned technical issues, the invention patent application CN202511447513.1, "Enterprise Data Visualization and Interactive Management System Integrating Digital Twin and BI Dashboard," avoids the impact of data chaos and bias on analysis results from the source by using a multi-source heterogeneous data adaptation and access module. Based on a dynamic digital twin, it achieves real-time synchronization of enterprise operating status and provides early warnings of equipment failures. However, the above technical solution has the following technical problems: When processing a business type, it is often necessary to integrate business data from multiple business systems. Therefore, it is crucial to identify the business systems that require change monitoring based on their integration processing needs across different business types, and to determine targeted integration processing strategies. This allows for proactive integration processing of business data when changes occur, thereby improving the data processing efficiency of the business type during data retrieval or usage. This is a pressing technical issue that needs to be addressed.

[0004] Therefore, there is an urgent need for a comprehensive management method and system for enterprise data. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a comprehensive management method for enterprise data, which includes: S1 obtains a method for fusion processing of business data between different business systems in a business type, and determines the fusion optimization processing target in the business type based on the fusion processing method; S2 determines that the business system belongs to the business type of the fusion optimization processing target, and determines the change monitoring target in the business system by combining the fusion optimization processing target data in the business type; S3 uses the change monitoring target to monitor and process changes in business data, and determines the fusion processing strategy for business data in the business type using the change monitoring target data and the fusion processing method of the change monitoring target business data in the business type. S4 determines the monitoring and management method for the change monitoring target based on the fusion processing strategy of business data of the change monitoring target in different business types.

[0006] The beneficial effects of this invention are as follows: This method identifies business data types requiring cross-system fusion processing under specific business categories, accurately pinpointing business systems with high data coupling and urgent integration needs, and defining them as "fusion optimization processing targets." Subsequently, these targets are used as key monitoring points to dynamically track changes in their business data, providing intelligent assurance for enterprise data consistency maintenance, process collaborative optimization, and real-time decision support, thereby achieving closed-loop management from data fusion to intelligent monitoring.

[0007] This system utilizes the data from change monitoring targets within business types and the fusion processing methods for the business data of those targets to determine the fusion processing strategy for each business type. By dynamically analyzing the monitoring characteristics and fusion structure of business types, it achieves intelligent adaptation of the fusion processing strategy: ensuring real-time performance in critical business scenarios, maintaining consistency in complex networks, optimizing resource utilization in loosely coupled scenarios, and leveraging monitoring advantages in tightly coupled networks. This refined strategy management enables enterprises to maximize the business value of data fusion while controlling computing costs.

[0008] By comprehensively analyzing the number of "real-time fusion business types" that the system participates in, as well as the monitoring coverage completeness (change monitoring ratio) of these types, the system can intelligently decide whether to adopt the resource-intensive "real-time monitoring processing" or the more economical "basic monitoring method", thereby achieving precise allocation and hierarchical management of monitoring resources.

[0009] Furthermore, the method for merging business data between the business systems is determined based on the business data that needs to be merged between the business system and other business systems in the business type.

[0010] Furthermore, the method for determining the fusion optimization processing target in the aforementioned business type is as follows: Using the fusion processing method of the business system and other business systems in the business type, the business data type that needs to be fused with the business data of other business systems in the business system is determined, and the business data type that needs to be fused with the business data of other business systems is taken as the fusion data type. Based on the data type of the business systems being merged, determine the business systems that need to undergo merge processing in the merged business data processing. Based on the data types of fusion across different business systems, the fusion optimization processing objectives for the aforementioned business type are determined.

[0011] Furthermore, the method for determining the change monitoring targets in the business system is as follows: The business types of the aforementioned business systems that belong to the fusion optimization processing target are defined as the fusion matching business types. Using the fusion optimization processing target data in the fusion matching service type, determine the proportion of the fusion optimization processing target in the fusion service type in the business system of the fusion service type, and use it as the optimization proportion; Based on the fusion matching business types of the business system and the optimization ratios in different fusion matching business types, it is determined whether the business system belongs to the change monitoring target.

[0012] Furthermore, the method for determining the monitoring and management method for the change monitoring target is as follows: Based on the fusion processing strategy of business data of the change monitoring target in different business types, determine the business type of real-time fusion processing of the change monitoring target, and take the business type of real-time fusion processing of the change monitoring target as the real-time fusion business type of the change monitoring target; Based on the change monitoring targets in the aforementioned real-time converged service types, determine the change monitoring ratio in the real-time converged service types; The monitoring and management method for the change monitoring target is determined based on the real-time fusion service type of the change monitoring target and the change monitoring ratio in the real-time fusion service type.

[0013] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned method for comprehensive management of enterprise data when running the computer program.

[0014] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of a comprehensive management method for enterprise data. Figure 2This is a flowchart illustrating the method for determining the fusion optimization processing objectives within a business type; Figure 3 This is a flowchart illustrating the method for determining change monitoring targets in a business system; Figure 4 It is a framework diagram of a computer system. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0019] Example 1 like Figure 1 As shown, this application provides a comprehensive management method for enterprise data, specifically including: S1 obtains a method for fusion processing of business data between different business systems in a business type, and determines the fusion optimization processing target in the business type based on the fusion processing method; S2 determines that the business system belongs to the business type of the fusion optimization processing target, and determines the change monitoring target in the business system by combining the fusion optimization processing target data in the business type; S3 uses the change monitoring target to monitor and process changes in business data, and uses the change monitoring target in the business system and the fusion processing method of the business data of the change monitoring target to determine the fusion processing strategy of business data in the business type. S4 determines the monitoring and management method for the change monitoring target based on the fusion processing strategy of business data of the change monitoring target in different business types.

[0020] Furthermore, the method for merging business data between the business systems is determined based on the business data that needs to be merged between the business system and other business systems in the business type.

[0021] Specifically, such as Figure 2 As shown, the method for determining the fusion optimization processing target in the aforementioned business type is as follows: The core objective of this embodiment is to achieve cross-business system data fusion governance and intelligent monitoring. Its core logic is as follows: In the complex business environment of an enterprise, there is a large amount of business data that needs to be correlated and analyzed between different systems (such as ERP, CRM, and SCM). This method identifies the types of business data that require cross-system fusion processing under specific business types, accurately locating those business systems with high data coupling and urgent integration needs, and defining them as "fusion optimization processing targets." Subsequently, these targets are used as key monitoring points to dynamically track changes in their business data, providing intelligent assurance for enterprise data consistency maintenance, process collaborative optimization, and real-time decision support, thereby achieving closed-loop management from data fusion to intelligent monitoring.

[0022] S11 uses the fusion processing method of the business system and other business systems in the business type to determine the business data type in the business system that needs to be fused with the business data of other business systems, and takes the business data type that needs to be fused with the business data of other business systems as the fusion data type. "Integrated data type" refers to a data type held by a business system in a specific business type (such as "order fulfillment" or "customer lifecycle management"), which needs to be associated, compared, and integrated with data from one or more other business systems to realize its full business value. For example, the "customer contract amount" in a CRM system needs to be integrated with the "actual payment receipts" in an ERP system to truly reflect customer value.

[0023] This step aims to accurately filter out data items with cross-system correlation value from massive amounts of business data. Its significance lies in the fact that it no longer simply lists all data, but rather identifies data points with inherent "data lineage" or "business coupling" relationships based on business logic. This is the first step in achieving effective data governance and in-depth business analysis, ensuring that subsequent optimization resources can be focused on key data that truly impacts business process continuity and data consistency.

[0024] S12 Based on the fusion data type of the business system, determine the business systems that need to undergo fusion processing in the fusion data type; "Business systems that require fusion processing" refers to those systems that hold "fusion data types". For a specific fusion data type, at least two business systems are involved: one is the "source system" (holder) of the data, and the other one or more are "related systems" (the party that needs to be fused with the data).

[0025] This step aims to clarify the responsible parties and stakeholders for data fusion. Its significance lies in translating the abstract requirement of "data type fusion" into concrete requirements for "inter-system interfaces or processes." By identifying all business systems involving a specific data type to be fused, the data's flow path and dependency network within the enterprise can be clearly depicted, laying the foundation for subsequent inter-system integration scheme design and responsibility allocation.

[0026] S13. Based on the fusion data types of different business systems and the business systems that need to be fused in the fusion data types, determine the fusion optimization processing target in the business type.

[0027] "Integration and optimization targets" specifically refer to key business systems identified under specific business types as requiring in-depth data integration governance and monitoring. Becoming a target means that the system is currently a bottleneck in data integration or a critical node in business collaboration, requiring priority resource allocation for interface optimization, data quality improvement, and high-frequency data change monitoring.

[0028] This step aims to prioritize and categorize the numerous business systems within an enterprise. Its significance lies in avoiding a "one-size-fits-all" approach to data governance. Instead, it uses multi-dimensional indicators (number of integrated data types, breadth of systems involved, and density of integrated data within systems) for scientific evaluation, accurately identifying the "critical few" systems that have the greatest impact on overall business collaboration and where data issues are most concentrated. This enables efficient allocation of governance resources and effective risk management.

[0029] Specifically, the above steps include the following situations: Case 1: If the number of fused data types in the business type is less than the preset data type number threshold, then it is determined that there is no fusion optimization processing target in the business type.

[0030] The demand for integration is sparse, and no special optimization is required. If the total number of identified "fusion data types" is very small (below the threshold) under a certain business type, it indicates that the cross-system data coupling within that business domain is very low, and the business processes are relatively independent. In this case, the return on investment forcibly implementing deep data fusion governance is not high. Therefore, the system determines that there are no "fusion optimization processing targets" requiring priority optimization under this business type. Resources should be invested in other more complex business domains.

[0031] Specific example: The "Fixed Asset Management" business type. Analysis revealed that only the "Equipment Code" data type requires consistency verification between the asset management system and the financial system. The number of merged data types is 1, less than the preset threshold (e.g., 3). Therefore, there is no optimization target for this business type; only basic data synchronization needs to be ensured.

[0032] Case 2: If the number of fused data types in the business type is not less than the preset data type number threshold, obtain the number of business systems in the business type that have fused data types. If the number of business systems that have fused data types is greater than the preset business system number threshold, then determine that all business systems in the business type belong to the fusion optimization processing target.

[0033] Extensive and deep integration requires global optimization: If a particular business type involves not only a large number of converged data types, but these data types are also widely distributed across numerous business systems (the number of systems involved exceeds a threshold), this indicates that the business type is a highly complex and deeply intertwined business network. Changes to data in any one system can generate widespread "ripple effects." Therefore, a global perspective must be adopted, listing all relevant business systems under this business type as "convergence optimization processing targets," and implementing unified data standards, interface specifications, and collaborative monitoring mechanisms to ensure the stability and coordination of the entire business network.

[0034] Specific example: The "end-to-end order fulfillment" business type. This involves more than 10 integrated data types (>threshold 1), such as "order status," "inventory quantity," "logistics trajectory," and "settlement status," and these data are scattered across more than 5 systems (>threshold 2, e.g., 4 systems) including OMS, WMS, TMS, ERP, and CRM. The system determines that all 5 systems under this business type are targets for integrated optimization, requiring the establishment of an integrated order data center and end-to-end monitoring.

[0035] Case 3: If the number of business systems with fused data types is not greater than a preset threshold for the number of business systems, obtain the number of fused data types of the business system in the business type. If the number of fused data types of the business system in the business type is greater than a preset threshold for the number of types, then determine that the business system belongs to the fusion optimization processing target in the business type.

[0036] This system has dense data coupling and requires focused optimization. When the breadth of merged data types is generally moderate (the number of systems does not exceed the threshold), but certain specific systems handle an unusually large number of merged data types (exceeding the threshold), these systems become the "data hubs" or "single-point bottlenecks" for that business type. Their data quality and processing efficiency directly determine the smoothness of the entire business chain. Therefore, these specific systems must be prioritized for optimization, with a focus on resolving their internal data governance issues and external interface performance.

[0037] Specific example (continuous): The "Marketing Campaign Analysis" business type. The integrated data types involve three systems: CRM, MA (Marketing Automation), and BI, totaling 3 (not exceeding the system breadth threshold). However, further analysis revealed that the CRM system alone carries 8 integrated data types, including "Customer Profile Tags," "Activity Participation Records," and "Lead Scoring," far exceeding the preset threshold for the number of types (e.g., 5). Therefore, the system determines the CRM system as the target for integrated optimization for this business type, requiring priority optimization of its data structure and data supply capabilities to the MA and BI systems.

[0038] Case 4: If the number of fused data types in the business type of the business system is not greater than the preset type number threshold, then the total number of business systems that need to be fused in the fused data types in the business type is determined. When the total number is greater than the preset system number threshold (e.g., 2), the business system is determined to be a fusion optimization processing target. Otherwise, the business system is not a fusion optimization processing target.

[0039] In this embodiment, precise investment in data governance is achieved: by scientifically identifying "integrated optimization processing targets," limited IT governance resources are concentrated on systems that can best enhance business collaboration and data value, avoiding ineffective "sprinkling pepper" style investment.

[0040] Build a proactive data consistency assurance system: transform the post-event remediation of data consistency issues into pre-event early warning and in-event synchronization based on key target monitoring, significantly reducing business decision-making errors and operational risks caused by data inconsistency.

[0041] Depicting the dynamic data lineage of an enterprise: The result of this method's continuous operation is essentially the construction and dynamic updating of a panoramic map reflecting the core business flows and data dependencies of an enterprise, providing a crucial blueprint for digital transformation and system architecture evolution.

[0042] Enhance business agility and decision-making speed: Ensure that data in the core business chain is real-time, accurate, and consistent, making it possible and more reliable to automate business processes based on this data (such as automatic reconciliation and intelligent replenishment) and conduct real-time decision analysis (such as dynamic pricing and risk warning).

[0043] Provide a basis for system microservice or middle platform transformation: The clearly identified "integration and optimization processing target" and the dense data flow it carries are often the best entry point and core category definition for system decoupling and building a business middle platform (such as an order middle platform or a customer data middle platform).

[0044] Specifically, such as Figure 3 As shown, the method for determining the change monitoring target in the business system is as follows: The core objective of this embodiment is to construct a precise and dynamic mechanism for screening targets for monitoring data changes in business systems. Its core logic is: based on the identification of "integration and optimization processing targets," further assess the comprehensive pivotal role of each business system within the enterprise data ecosystem and the risk of impact from changes, thereby determining which business systems need to be included in the high-priority real-time data change monitoring list. This method analyzes the breadth of the "integration and matching business types" involved by the business systems, as well as their core importance (optimization ratio) within each business type, comprehensively determining from multiple dimensions whether they should be considered "change monitoring targets," ensuring that enterprise data monitoring resources can focus on critical systems where data changes will trigger widespread chain reactions.

[0045] S21 defines the business types of the business systems that belong to the fusion optimization processing targets as the fusion matching business types; Determine the integration matching business types. "Integration matching business types" refers to those business types that the business system is listed as "integration optimization processing targets". For example, for a CRM system, if it is determined to be an integration optimization processing target in the two business types of "customer lifecycle management" and "marketing campaign analysis", then these two types are the integration matching business types of the CRM system.

[0046] This step aims to outline the scope of data integration responsibility for the business system from the perspective of business type. Its significance lies in transforming the abstract importance of data within a system into a set of specific business scenarios in which it participates and assumes key responsibilities. This provides a clear and concrete analytical unit for subsequent assessment of the system's overall impact breadth, avoiding vague overall evaluations.

[0047] S22 uses the fusion optimization processing target data in the fusion matching service type to determine the proportion of the fusion optimization processing target in the business system of the fusion service type, and uses it as the optimization proportion. "Optimization Ratio" refers to the proportion of the number of business systems listed as "fusion optimization processing targets" within a specific "fusion matching business type" to the total number of business systems involved in that business type. The formula is: Optimization Ratio = (Number of fusion optimization processing targets in this type) / (Total number of business systems involved in this type).

[0048] This step aims to quantify the relative coreness or pivotal role of the business system within a specific business type. A higher optimization ratio indicates that only a few systems are marked as key objectives in the collaborative network for that business type, and this system is one of them, making its position crucial; a lower ratio indicates that most systems in that type are marked as objectives, and this system is just one of them, potentially having lower relative importance. This metric transforms the absolute importance of a system (whether it is an objective) into its relative importance within a specific context.

[0049] S23 determines whether the business system belongs to the change monitoring target based on the fusion matching business type of the business system and the optimization ratio in different fusion matching business types.

[0050] "Change Monitoring Targets" is the final output, referring to business systems that the system determines require high-frequency, real-time monitoring and impact analysis of changes to their core business data. Becoming a monitoring target means that changes to the system's data will trigger automated impact chain analysis and early warning processes.

[0051] This step is a multi-layered decision funnel, significant for its ability to accurately define and categorize monitoring targets. By introducing multiple criteria such as "breadth of type," "coreness," and "target concentration," it aims to identify two types of systems that most require monitoring: one is key hubs spanning multiple core business areas (breadth priority); the other is systems playing an irreplaceable and unique core role in a few areas, a role not yet covered by other monitoring targets (depth and uniqueness priority). This avoids over-expansion of the monitoring scope or omission of key nodes.

[0052] It should be noted that if the number of business types that the business system matches is greater than the preset threshold for the number of matching types, then the business system is determined to be a change monitoring target.

[0053] Cross-domain hub systems are directly listed as monitoring targets: If a business system is listed as a target for integration and optimization across a large number (exceeding a threshold) of different business types, then it is essentially a core data hub or shared service platform spanning multiple business domains (e.g., a master data management system or an order center). Data changes in such systems have a very wide impact and must be unconditionally included in the highest priority monitoring. Therefore, as long as the number of business types it integrates exceeds the threshold, it is directly identified as a change monitoring target.

[0054] For example, an "Enterprise Data Bus (ESB)" or "Customer Master Data Management (MDM) system" may be listed as a target for integrated optimization processing by multiple business types such as "Sales Management," "Customer Service," "Financial Analysis," and "Marketing Management." Assuming that the number of its integrated matching types is 8, which is greater than a preset threshold (such as 5), the system will directly determine it as a change monitoring target.

[0055] Additionally, it should be noted that if the number of business types that the business system integrates and matches is not greater than a preset threshold for the number of integration types, the following content is also included: S231 Determine the optimization ratio of the fusion matching business types of the business system, and determine whether there are any fusion matching business types in the business system whose optimization ratio is less than a preset ratio threshold. If yes, proceed to the next step; otherwise, determine that the business system does not belong to the change monitoring target. Preliminary screening (checking for the existence of low-core business types): For systems involved in a limited number of business types, the first step is to examine whether there are any types where the system itself has a low "optimization ratio." A low optimization ratio means that within that type, it is merely one of many critical systems, not a unique core. If the system has a high optimization ratio (not lower than the threshold) across all involved types, it indicates that it is recognized as an absolute core in each involved domain and should be a monitoring target. Conversely, further analysis of these "low coreity" types is necessary.

[0056] Specific example (continuous): The "Warehouse Management System (WMS)" has two business types for fusion matching: "Inbound Management" (optimization ratio = 1 / 3 ≈ 33%) and "Outbound Management" (optimization ratio = 1 / 4 = 25%). The preset ratio threshold is 50%. The optimization ratios for both types are less than the threshold. Therefore, the condition is met, and the process proceeds to S232.

[0057] S232 takes the fusion matching service types with an optimization ratio less than a preset ratio threshold as fusion demand service types, and determines whether the number of fusion demand service types is greater than a preset demand type number threshold. If yes, it determines that the service system belongs to the change monitoring target; otherwise, it proceeds to the next step. Check the degree of clustering of low-core business types: This step focuses on business types with low optimization rates (i.e., business types with convergence requirements). If there are many such business types (exceeding the requirement type threshold), a new risk pattern emerges: while the system is not the absolute core of every domain, it is widely and superficially involved in many critical business processes. This characteristic of "broad connectivity but insufficient depth" makes it a potential weak link in data consistency and a potential intermediary for error propagation, and therefore also needs to be monitored.

[0058] Specific example (continuous): Continuing from the previous example, the number of converged business types (i.e., types with an optimization ratio of <50%) in the WMS system is 2. Assuming the preset threshold for the number of requirement types is 3, 2 is not greater than 3, so the condition is not met, and proceed to S233.

[0059] S233 determines whether all the business types in the integration requirements of the business system have change monitoring targets. If so, the business system is determined not to be a change monitoring target; otherwise, the business system is determined to be a change monitoring target.

[0060] Check target coverage (avoid redundant monitoring): This is the final and most meticulous check. The logic is as follows: if other systems are already listed as monitoring targets for changes in the "low-core" business types that a certain business system participates in, it means that the data change risk of that type has already been covered by other, more core monitoring targets. In this case, even if the data in this system changes, its impact can be indirectly perceived or analyzed by monitoring other systems. Therefore, this system does not need to be listed as a separate monitoring target, thus avoiding redundant configuration of monitoring resources. Conversely, if there is a low-core type that is not yet covered by any monitoring targets, then this system, as a participant in that type, needs to be listed as a monitoring target to fill the coverage gap in the monitoring network.

[0061] Specific example (continuous): Examine two converged business types in the WMS system: "Inbound Management" and "Outbound Management".

[0062] Assuming that under the "Inbound Management" business type, the "Purchasing Management System (PRM)" has already been listed as a change monitoring target, while under the "Outbound Management" business type, no system has currently been listed as a change monitoring target.

[0063] Since not all business types requiring integration have other change monitoring targets (the "Outbound Management" type is missing), the WMS system was ultimately determined to be a change monitoring target. This is because it is needed to supplement the data change monitoring coverage for the "Outbound Management" business type.

[0064] In this embodiment, intelligent targeted deployment of monitoring resources is achieved: expensive real-time data monitoring and computing resources are transformed from "comprehensive monitoring" to "precise targeting," and deployed only on a few key systems that can best reveal business risks and data flow bottlenecks, greatly improving operation and maintenance efficiency and return on investment.

[0065] Build a redundant and fully covered monitoring network: Through the "target coverage check" logic, it ensures that there is at least one monitoring perspective for each data change risk point on the enterprise's key business chain, while avoiding the waste of resources from multiple systems monitoring the same data stream, thus forming an efficient and complete monitoring system.

[0066] Dynamically identify hidden hubs and vulnerabilities in enterprise data architecture: This method can not only identify explicit core systems (such as ERP), but also discover "connector" systems that have potential systemic importance due to extensive connectivity, as well as "vulnerable blind spots" formed by gaps in monitoring coverage, which helps to prevent cascading data failures caused by such system problems in advance.

[0067] Providing decision-making support for data governance and system evolution: The results of change monitoring clearly reveal the key nodes in the enterprise's current data dependency network. This provides valuable prioritization references and architectural optimization directions for future system decoupling, data platform construction, or disaster recovery backup strategy formulation.

[0068] Specifically, the method for determining the fusion processing strategy for business data in the aforementioned business type is as follows: The core objective of this embodiment is to dynamically formulate optimal data fusion processing strategies for different business types to balance data consistency, system processing load, and real-time business requirements. Its core logic is that the triggering frequency and scope of business data fusion need to be intelligently decided based on the coverage density of "change monitoring targets" under that business type, the complexity of inter-system coupling, and the concentration of key data flows. This method automatically selects different strategies such as "real-time fusion," "condition-triggered fusion," or "delayed batch processing fusion" by quantitatively analyzing the "change monitoring ratio" and the structural characteristics of the "fused business system" for each business type. This achieves the best match between data processing efficiency and business needs, thereby constructing an adaptive and highly efficient enterprise data fusion processing engine.

[0069] S31 uses the change monitoring target data in the business type to determine the proportion of the change monitoring target in the business system, and uses the proportion of the change monitoring target in the business system as the change monitoring proportion. "Change Monitoring Ratio" refers to the proportion of the number of business systems identified as "change monitoring targets" within a specific business type, relative to the total number of all business systems involved in that business type. The formula is: Change Monitoring Ratio = (Number of Change Monitoring Targets under this Business Type) / (Total Number of Business Systems Involved in this Business Type).

[0070] This step aims to assess the overall monitoring intensity and sensitivity of data flows within this business type. A higher proportion of change monitoring indicates that most systems within this business type are under high-sensitivity monitoring, and data changes are captured almost in real time, resulting in high monitoring reliability. A lower proportion means that only a few core systems are under focused monitoring, leading to lower reliability and a greater need for integrated processing before business systems make calls.

[0071] S32, based on the fusion processing method of the business data of the change monitoring target, determines the business system of the change monitoring target that needs to be fused in the business type, and identifies it as the fused business system; "Integrated business systems" refers to a set of other business systems that need to be integrated with the target system for a given business type, specifically for a "change monitoring target". For example, for the change monitoring target "Order Center (OMS)", its integrated business systems may include "Warehouse System (WMS)", "Logistics System (TMS)" and "Package Management System (ERP)".

[0072] This step aims to characterize the specific connections of each monitored target within the data fusion network. Its significance lies in concretizing the abstract "fusion requirement" into a clear "system-to-system" fusion link. This provides crucial structured information for subsequent analysis of the complexity of the fusion structure (such as whether bidirectional fusion requirements exist) and for formulating precise triggering conditions.

[0073] S33 determines the fusion processing strategy for business data in the business type based on the change monitoring ratio and the fusion business system of the change monitoring target.

[0074] The "fusion processing strategy" defines the conditions under which a global data fusion computation task is triggered for this business type. It mainly includes three modes: Real-time fusion processing strategy: If any change occurs in the relevant data of any change monitoring target, global fusion calculation is immediately triggered.

[0075] Condition-triggered fusion processing strategy: Global fusion calculation is triggered only when specific conditions are met (such as the number of changing monitoring targets reaching a threshold). This is further divided into "first monitoring fusion processing strategy" (general threshold) and "second monitoring fusion processing strategy" (specific threshold).

[0076] This step is central to strategic decision-making, its significance lying in achieving the optimal trade-off between data processing costs and business value. High-frequency real-time fusion ensures absolute data freshness and consistency, but at a huge computational cost. By analyzing the structural characteristics of business types (monitoring density, number of systems, fusion link patterns), the system can intelligently determine: which business scenarios have extremely high real-time requirements and must be guaranteed at all costs (such as financial transaction risk control); which scenarios can tolerate minute-level latency in exchange for a hundredfold saving of computational resources (such as end-of-day reports); and which scenarios are in an intermediate state and require reasonable buffering conditions. This enables refined and intelligent scheduling of enterprise computing resources.

[0077] It is understandable that the above steps include the following situations: Case 1: If the change monitoring ratio of the business type is less than the preset monitoring ratio threshold, then the fusion processing strategy for the business data in the business type is determined to be a real-time fusion processing strategy. That is, as long as any change monitoring target changes the data type of the business in the business type, the fusion processing of the business data in the business type will be performed.

[0078] For scenarios with low monitoring density, a real-time fusion strategy is adopted (risk-sensitive): When the change monitoring ratio is low, it means that only a very small number of core systems within that business type are being closely monitored. Changes in the data of these core systems often represent critical business events or risk signals (such as changes to core customer information or adjustments to the price of main products). In this "sparse monitoring" environment, any change to a monitored target can be crucial. Therefore, to improve the real-time performance of business systems during invocation and processing, the most sensitive response strategy should be adopted: any change to a core monitored target should immediately trigger a fusion mechanism. This ensures immediate response to critical business events and avoids delays caused by waiting for changes in other non-core systems.

[0079] Specific example: "Omnichannel Inventory Synchronization" business type. Only online and offline warehouses, store POS, and e-commerce platform inventory are set as change monitoring targets. The remaining dozen or so business systems are not included in the change monitoring targets, and the change monitoring ratio is low. Any change in the payment status or credit status of a transaction in any system must immediately trigger a recalculation of the risk model (fusion processing) to update the risk rating in real time.

[0080] Scenario 2: If the change monitoring ratio of the business type is not less than the preset monitoring ratio threshold, the number of business systems in the business type is obtained. If the number of business systems in the business type is greater than the preset system number threshold, in order to improve the real-time performance of the business type in the call processing process, as long as any change monitoring target changes the business data type in the business type, the business data in the business type will be fused.

[0081] For scenarios with high monitoring density and numerous systems, a real-time fusion strategy (strong consistency) is adopted: When the proportion of changes monitored is high and a large number of business systems are involved, it indicates that the business type is a highly complex, deeply integrated business network with extremely high consistency requirements (such as omnichannel order fulfillment). In this scenario, changes to almost every node can affect the global state, and the dependencies between systems are intricate. To maintain strong consistency and real-time decision-making capability of the entire network view and ensure the real-time processing of data during invocation, a real-time fusion strategy is adopted.

[0082] A specific example: the "omnichannel inventory synchronization" business type. If more than 20 systems, including online and offline warehouses, store POS systems, and e-commerce platform inventory, are listed as change monitoring targets (high proportion, many systems), any change in inventory quantity at any node (such as an offline sale) must trigger a real-time calculation of the global available inventory to ensure that inventory data across all sales channels remains consistent within seconds.

[0083] Scenario 3: If the number of business systems in the business type is not greater than the preset system number threshold, based on the fused business systems of the change monitoring targets, it is determined that all fused business systems in the change monitoring targets belong to the change monitoring targets of the change monitoring targets, and they are taken as fusion requirement targets. If there are no fusion requirement targets in the business systems, the fusion processing strategy for the business data in the business type is determined to be the first monitoring fusion processing strategy. That is, when the number of change monitoring targets in the business type where the business data type changes is greater than the preset monitoring target number threshold, the fusion processing of the business data in the business type is performed.

[0084] High monitoring density but simple system, and no strong fusion dependency scenarios, adopting a condition-triggered strategy (efficiency-first): When monitoring density is high but the total number of systems is small, and there is no "fusion requirement target" (i.e., no single monitoring target needs to be fused with all other monitoring targets), it means that even with advance fusion processing, it's still necessary to determine whether the fusion business system for the changing monitoring target has changed during business calls. In this case, frequent global fusion calculations may be wasteful. Therefore, a "condition-triggered strategy" is adopted: only when the number of changed monitoring targets accumulates to a certain threshold is the change considered sufficient to affect the global state, thus triggering a batch fusion. This significantly reduces unnecessary calculations and improves processing efficiency.

[0085] Specific example: The "Department-level Project Collaboration Dashboard" business type. This involves four systems: "Design System," "Development System," "Testing System," and "Documenting System," all of which are monitored. In this case, there is no fusion requirement target. That is, if only the Design System is used as the change monitoring target, then the fusion processing target for the Design System is the Test System, which is not a change monitoring target. When the number of systems experiencing changes exceeds a threshold (e.g., 2), a data fusion is triggered to reduce the complexity of data processing when the business system makes calls.

[0086] Scenario 4: If there are fusion requirement targets in the business system, determine whether the proportion of the number of fusion requirement targets among the change monitoring targets in the business system is less than a preset proportion threshold. If yes, then determine the fusion processing strategy for the business data in the business type as the second monitoring fusion processing strategy. That is, when the number of change monitoring targets whose business data type changes in the business type is greater than the second preset monitoring target number threshold, then perform fusion processing for the business data in the business type. If no, then when performing fusion processing, once the business system is called, the need to determine whether the business data of the fusion business system has changed is relatively small. Therefore, the reuse degree of the fusion processing data is high when called. Then determine the fusion processing strategy for the business data in the business type as the real-time fusion processing strategy. That is, as long as any change monitoring target changes its business data type in the business type, then perform fusion processing for the business data in the business type.

[0087] High monitoring density and strong fusion dependency structure, dynamic decision-making strategy: The core of this scenario is identifying the "fusion requirement target"—that is, a certain change monitoring target, and all its "fusion business systems" are also "change monitoring targets." This means that the fusion processing of this target occurs entirely within the highly sensitive monitoring network.

[0088] If the number of such "fusion requirement targets" is small (accounting for a low proportion of change monitoring targets), it indicates that this tightly coupled structure is only an isolated phenomenon. In this case, when a business system makes a call, it is impossible to effectively monitor whether the data of the fusion business system has changed due to the change monitoring target, and it still needs to be reviewed and processed. When a change occurs, it still needs to be fused again. The efficiency of real-time fusion is not high. Therefore, a condition-triggered strategy is adopted (a second monitoring fusion processing strategy, such as triggering data fusion once when there are more than one, to reduce the complexity of data processing in the business system when making a call).

[0089] It should be noted that the second preset monitoring target number threshold is less than the preset monitoring target number threshold.

[0090] If the number of such "fusion requirement targets" is large (accounting for a high percentage, such as more than 70%), it indicates that the fusion network for this business type exhibits a highly cohesive and tightly coupled structure. The vast majority of data calls between critical systems have both their source and destination under real-time monitoring. In this case, real-time fusion processing offers significant efficiency advantages: since all related systems (fusion business systems) are also monitoring targets, their data changes are captured in real time. Therefore, when a business system makes a call, there is no need to additionally check the data update status of the fusion business system from the change monitoring system; its latest snapshot can be used directly. This "inspection-free" characteristic greatly reduces the computational overhead and complexity of business system calls, making the fusion strategy efficient and feasible.

[0091] In a specific implementation example, taking the "intelligent supply chain management" business type as an example, the dynamic adjustment of strategies at different stages is demonstrated: Phase 1 (Initial): Only the "Inventory Management System" and "Order Processing System" are listed as change monitoring targets (25%). The real-time fusion strategy of Phase 1 is adopted to ensure timely response to core inventory changes.

[0092] Phase 2 (Extended): Add "Procurement System," "Logistics Tracking System," and "Supplier Management System" as monitoring targets, bringing the total number of systems to 5, which exceeds the threshold of 4. Adopt the real-time fusion strategy of Phase 2 to ensure data consistency across the entire chain.

[0093] Phase 3 (Optimization): Assuming there are only 4 business systems, not exceeding the threshold of 4, we enter Case 3. Analysis reveals that the coupling between systems is low, and there is no fusion requirement. The strategy is adjusted to the conditional triggering approach of Case 3: data fusion processing between different business systems within a business type is only triggered when more than 3 systems change.

[0094] Phase 4 (Maturity): After deep system integration, all business systems within the "Intelligent Supply Chain Management" business type become change monitoring targets, falling under the category of integration requirements. The system is upgraded to the real-time integration strategy of Phase 4, leveraging the cohesive characteristics of the monitoring network to achieve efficient real-time processing.

[0095] This system achieves intelligent adaptation of fusion processing strategies by dynamically analyzing the monitoring characteristics and fusion structure of business types: ensuring real-time performance in critical business scenarios, maintaining consistency in complex networks, optimizing resource utilization in loosely coupled scenarios, and leveraging monitoring advantages in tightly coupled networks. This refined strategy management enables enterprises to maximize the business value of data fusion while controlling computing costs.

[0096] Specifically, the method for determining the monitoring and management method for the change monitoring target is as follows: The core objective of this embodiment is to formulate the most suitable real-time data monitoring and management method for each business system identified as a "change monitoring target." Its core logic is that whether a business system needs to be subject to high-intensity real-time monitoring depends not only on whether it is marked as a monitoring target, but more importantly, on analyzing in which business scenarios it requires real-time data fusion, and the monitoring maturity of these scenarios themselves. By comprehensively analyzing the number of "real-time fusion business types" the system participates in, and the completeness of monitoring coverage for these types (change monitoring ratio), the system can intelligently decide whether to adopt the resource-intensive "real-time monitoring processing" or the more economical "basic monitoring method," thereby achieving precise allocation and hierarchical management of monitoring resources.

[0097] S41 uses the fusion processing strategy of business data of the change monitoring target in different business types to determine the business type of real-time fusion processing of the change monitoring target, and takes the business type of real-time fusion processing of the change monitoring target as the real-time fusion business type of the change monitoring target. "Real-time fusion business type" refers to those business types in which the change monitoring target participates, and whose fusion processing strategy is determined to be a "real-time fusion processing strategy". That is, in this type of business, data changes in the system need to immediately trigger cross-system data fusion calculations.

[0098] This step aims to pinpoint the core business pressure points driving the monitoring objective to maintain high availability and data timeliness. A system may need real-time monitoring not because it is inherently important, but because certain critical business processes it supports require it to respond in real time. Identifying these business types reveals the root causes necessitating intensive monitoring.

[0099] S42 determines the change monitoring ratio in the real-time fused service type based on the change monitoring targets in the real-time fused service type; The "change monitoring ratio" here specifically refers to the change monitoring ratio (i.e., the number of change monitoring targets under that type / the total number of systems) for each "real-time converged service type" identified in step S41. This reflects the overall monitoring maturity of this key business scenario.

[0100] This step aims to assess the ability of this critical business scenario to support real-time monitoring. Even if a business type requires real-time fusion, if its monitoring coverage is low (small monitoring ratio), it means that only a few systems are monitored in real time, and the data status of most collaborative systems is unknown or delayed. In this scenario of "incomplete monitoring," even if real-time monitoring is implemented on the target system, the collaborative data obtained after triggering fusion computation may be outdated. This weakens the actual value of real-time fusion and may affect the assessment of the necessity of implementing high-intensity monitoring on the target system.

[0101] S43 determines the monitoring and management method for the change monitoring target based on the real-time fusion service type of the change monitoring target and the change monitoring ratio in the real-time fusion service type.

[0102] The “Monitoring Management Method” defines how to monitor data and report events for the target of this change.

[0103] Real-time monitoring and processing: Deploy real-time listeners (such as CDC change data capture, event bus listeners) at the database or API level to capture and publish events immediately upon any data change, including basic monitoring methods and secondary basic monitoring methods.

[0104] This step is the final decision based on the analysis in the previous two steps. Its significance lies in linking the intensity of system monitoring to the urgency of its critical business responsibilities and the feasibility of those responsibilities. This avoids applying high-cost real-time monitoring to all monitoring targets in a "one-size-fits-all" manner, and also avoids insufficient monitoring in scenarios where it is truly needed.

[0105] Furthermore, if the number of real-time integrated service types of the change monitoring target is greater than the preset threshold for the number of integrated service types, then the monitoring and management method for the change monitoring target is determined to be real-time monitoring processing. Core hub-type systems must be monitored in real time: If a system requires real-time data fusion across multiple (exceeding a threshold) business types, it is essentially a core hub or shared service spanning multiple critical business processes (such as an order center or customer master data platform). The stability and real-time data performance of such systems are crucial for the smooth operation of multiple business lines; any data delay can trigger widespread business problems. Therefore, regardless of the monitoring maturity of an individual business scenario, it must be given the highest priority of "real-time monitoring and processing," which is fundamental to ensuring the continuity of the enterprise's core business.

[0106] A specific example: A company's "Unified Payment Gateway" system. It requires real-time data fusion across multiple business types, including "online transaction processing," "order settlement and reconciliation," and "marketing campaign refunds." The number of business types requiring real-time fusion is five, exceeding a preset threshold (e.g., three). Therefore, real-time monitoring and processing of the "Unified Payment Gateway" are essential to ensure that every change in payment status is captured instantaneously.

[0107] Furthermore, if the number of real-time fused service types of the change monitoring target is not greater than a preset threshold for the number of fused service types, the following is also included: S431 determines whether there is a real-time fusion service system in the real-time fusion service type of the change monitoring target where the change monitoring ratio is less than the preset monitoring ratio threshold. If yes, proceed to step S431; otherwise, determine the monitoring management method of the change monitoring target as the basic monitoring method. For systems involving only a small number of real-time fusion types, it is necessary to further examine whether the monitoring environment for these "critical business scenarios" is robust. If the monitoring coverage for all real-time fusion business types involved in the system is complete (the monitoring ratio is not lower than the threshold), it indicates that these business scenarios have established a good real-time data foundation ecosystem. In this case, the system is not in a "monitoring silo," and a lower-cost "basic monitoring method" can be adopted. Data changes are detected through polling, which usually meets the timeliness requirements of the scenario (because the upstream and downstream data status is updated promptly). Therefore, the "basic monitoring method" is selected.

[0108] S432 identifies real-time fusion business systems with a change monitoring ratio less than a preset monitoring ratio threshold as monitoring deviation business systems, and determines whether the number of such monitoring deviation business systems exceeds a preset deviation business system threshold. If so, the monitoring management method for the change monitoring target is determined to be real-time monitoring processing; otherwise, the monitoring management method for the change monitoring target is determined to be the second basic monitoring method.

[0109] If a "monitoring deviation business system" (i.e., a type with a low monitoring ratio) is found among the real-time fusion business types of the system, it indicates that the system is required to assume real-time responsibility in business scenarios where the monitoring foundation is weak and the timeliness of data may not be guaranteed. In this case, it is necessary to assess whether this "weak link" is widespread. If the number of "monitoring deviation business systems" is large (exceeding the threshold), it means that the system is frequently required to respond in real time in scenarios with inadequate monitoring. In this case, in order to compensate for the deficiencies in the overall monitoring environment and ensure that accurate data from the system can be obtained at critical moments, it is necessary to enhance the monitoring intensity of the system itself and adopt "real-time monitoring and processing" to make it the most reliable data source in this weak business chain.

[0110] If the number of "deviation monitoring systems" is small, the weak links are localized. In this case, the "second basic monitoring method" is adopted, which is a compromise solution with a monitoring intensity between "real-time" and "basic".

[0111] It should be noted that the basic monitoring method is as follows: when the change monitoring target belongs to a business type that does not have a real-time monitoring target in different business types, the monitoring management method for the change monitoring target is determined to be real-time monitoring processing. If the change monitoring target does not belong to a business type that does not have a real-time monitoring target in different business types, then when there is a change monitoring target whose business data has changed in any of the business systems of the change monitoring target, the monitoring processing of the change monitoring target is performed.

[0112] Basic monitoring method (corresponding to S431): Real-time monitoring is only upgraded to real-time monitoring when there are no change monitoring targets in all business types involved in the target system. Otherwise, as long as a data change of another change monitoring target is detected in any of its business systems (which can be understood as any instance of the business process it participates in), a check (polling) of that target is triggered. This is suitable for systems with a good monitoring environment and clearly defined responsibilities.

[0113] It should be noted that the second basic monitoring method is to determine the monitoring management method of the change monitoring target as real-time monitoring processing when there is a business type in different business types where there is no real-time monitoring target. If there is no business type in different business types where there is no real-time monitoring target, then when there is a change monitoring target in any business system of the change monitoring target where the business data has changed, the monitoring processing of the change monitoring target is performed.

[0114] The second basic monitoring method (for cases with low S432 deviations) has more lenient conditions. It upgrades to real-time monitoring as long as the target system has (i.e., exists) a business type that does not have a change monitoring target for real-time monitoring. If all its business types have high requirements (i.e., all have change monitoring targets for real-time monitoring), the strategy of "triggering checks when other monitoring targets change" is still used. This is suitable for systems that bear some responsibility in scenarios with weak monitoring, but are not core systems.

[0115] Step S41: Analysis revealed that the company's "Unified Payment Gateway" system only requires real-time integration in the "Cross-border E-commerce Order Fulfillment" business type (the number of real-time integrated business types = 1, not exceeding the threshold).

[0116] Step S42: Obtain the change monitoring ratio for the "Cross-border E-commerce Order Fulfillment" type, which is only 30% (less than the preset monitoring ratio threshold of 50%). This type is marked as "Monitoring Deviation Business System".

[0117] Step S43: Enter refined judgment.

[0118] S431: There are real-time fusion service types with low monitoring rates, and the condition is met.

[0119] S432: The number of monitoring deviation business systems is 1. Assuming the preset deviation system threshold is 2, 1 is not greater than 2. Therefore, the "second basic monitoring method" is adopted.

[0120] Interpretation: The "logistics tracking system" is required to respond in real time in cross-border fulfillment scenarios where monitoring is inadequate. However, since there are only one such "weak link" scenario, it is not subject to full-time real-time monitoring. A second basic monitoring method is adopted: if it is found that the system also participates in other non-real-time business types (such as "domestic logistics reports"), and there are no real-time monitoring targets in these other non-real-time business types, then real-time monitoring is initiated; otherwise, data checks on the logistics system are only triggered when a change occurs in any of the business systems it participates in (another monitoring target).

[0121] In this application, the cost-effectiveness of monitoring resources is maximized by deploying expensive real-time monitoring capabilities only on systems that are truly involved in multi-critical real-time processes or play a pivotal role in monitoring weak links, while configuring lower-cost monitoring solutions for a large number of other systems.

[0122] Establish a dynamic correlation between monitoring intensity and business criticality: The monitoring level of the system is not static, but dynamically adjusted according to the quantity and quality of the real-time converged services it supports, so that IT resource investment closely aligns with the business value stream.

[0123] Enhance the resilience and observability of the system architecture: Through a tiered monitoring strategy, we ensure that critical nodes of the enterprise's core data flow can still be prioritized and protected under resource constraints, while the observability of the entire system group is reasonably covered, thereby enhancing the ability to cope with complex business changes and technical failures.

[0124] Example 2 Secondly, such as Figure 4 As shown, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for comprehensive management of enterprise data when running the computer program.

[0125] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0126] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0127] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A comprehensive management method for enterprise data, characterized in that, Specifically, it includes: A method for merging and processing business data between different business systems in a business type is obtained, and based on the merging and processing method, the merging optimization processing target in the business type is determined; The business system is determined to belong to the business type of the fusion optimization processing target, and the change monitoring target in the business system is determined by combining the fusion optimization processing target data in the business type. The changes in business data are monitored and processed using the change monitoring targets. A fusion processing strategy for business data in the business type is determined using the change monitoring target data and the fusion processing method of the change monitoring target business data. Based on the strategy for fusion processing of business data of the change monitoring target in different business types, the monitoring and management method for the change monitoring target is determined.

2. The comprehensive management method for enterprise data as described in claim 1, characterized in that, The method for merging business data between business systems is determined based on the business data that needs to be merged between the business system and other business systems in the business type.

3. The comprehensive management method for enterprise data as described in claim 1, characterized in that, The method for determining the fusion optimization processing target in the aforementioned business type is as follows: Using the fusion processing method of the business system and other business systems in the business type, the business data type that needs to be fused with the business data of other business systems in the business system is determined, and the business data type that needs to be fused with the business data of other business systems is taken as the fusion data type. Based on the data type of the business systems being merged, determine the business systems that need to undergo merge processing in the merged business data processing. Based on the data types of fusion across different business systems, the fusion optimization processing objectives for the aforementioned business type are determined.

4. The comprehensive management method for enterprise data as described in claim 3, characterized in that, If the number of fused data types in the business type is less than a preset data type number threshold, then it is determined that there is no fusion optimization processing target in the business type.

5. The comprehensive management method for enterprise data as described in claim 1, characterized in that, The method for determining the change monitoring targets in the aforementioned business system is as follows: The business types of the aforementioned business systems that belong to the fusion optimization processing target are defined as the fusion matching business types. Using the fusion optimization processing target data in the fusion matching service type, determine the proportion of the fusion optimization processing target in the fusion service type in the business system of the fusion service type, and use it as the optimization proportion; Based on the fusion matching business types of the business system and the optimization ratios in different fusion matching business types, it is determined whether the business system belongs to the change monitoring target.

6. The comprehensive management method for enterprise data as described in claim 5, characterized in that, If the number of business types that the business system matches is greater than the preset threshold for the number of matching types, then the business system is determined to be a change monitoring target.

7. The comprehensive management method for enterprise data as described in claim 1, characterized in that, The method for determining the monitoring and management method for the aforementioned change monitoring targets is as follows: Based on the fusion processing strategy of business data of the change monitoring target in different business types, determine the business type of real-time fusion processing of the change monitoring target, and take the business type of real-time fusion processing of the change monitoring target as the real-time fusion business type of the change monitoring target; Based on the change monitoring targets in the aforementioned real-time converged service types, the change monitoring ratio in the real-time converged service types is determined; The monitoring and management method for the change monitoring target is determined based on the real-time fusion service type of the change monitoring target and the change monitoring ratio in the real-time fusion service type.

8. The comprehensive management method for enterprise data as described in claim 7, characterized in that, If the number of real-time integrated service types of the change monitoring target is greater than the preset threshold for the number of integrated service types, then the monitoring and management method for the change monitoring target is determined to be real-time monitoring processing.

9. A computer system, comprising: A memory and processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a comprehensive management method for enterprise data as described in any one of claims 1-8.

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