Data management and collaborative sharing processing method and system based on data warehouse

Abstract

The invention discloses a data management and collaborative sharing processing method and system based on a data warehouse, and the method comprises the steps: obtaining original business data of all business systems related to business of each enterprise, and carrying out the data preprocessing, and obtaining multi-source business data in a unified data format, performing business logic analysis on the multi-source business data according to the business process, performing data association on the multi-source business data, constructing a data association relationship of each enterprise business, and sequentially associating data processing rules of the multi-source business data according to the business process to obtain a data governance rule; and constructing a data association architecture according to the data association relationship to generate a data governance model, obtaining a service analysis request, calling multi-source service data of the same service through the data governance model to perform a collaborative service decision, and feeding back the service decision to all data sources related to the service to obtain service shared data of the current service. The application has the effects of realizing intercommunication and interconnection of data and improving the efficiency of service decision.

Description

Technical Field

[0001] This invention relates to the technical field of data governance, and in particular to a data governance and collaborative sharing processing method and system based on a data warehouse. Background Technology

[0002] Currently, during the process of enterprise informatization and digitalization, the volume of business undertaken by enterprises and the data generated during enterprise operation and management are increasing exponentially. Therefore, it is necessary to manage the massive amounts of data stored by enterprises in a unified manner, which are in complex and diverse formats.

[0003] The current method for managing massive amounts of enterprise data is usually to set up a data warehouse to store and classify the data. However, different business systems have different data formats and data processing rules, which often leads to problems such as data silos, data inconsistencies, and poor data quality. This makes it difficult to manage the complex and diverse data in the data warehouse in a unified and effective manner, which seriously affects the enterprise's data management capabilities and poses a significant challenge to business operations, decision analysis, and future development. Therefore, there is room for further optimization of the data governance of data warehouses in the aforementioned technologies. Summary of the Invention

[0004] To address the problems of data silos, data inconsistencies, and poor data quality caused by different data formats and data processing rules in existing data warehouses, this invention provides a data governance and collaborative sharing processing method and system based on a data warehouse. It can achieve data interconnection and improve the efficiency of business decision-making through intelligent integration of massive amounts of data and data sharing and collaborative calling among multiple applications.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A data governance and collaborative sharing processing method based on a data warehouse, the method comprising: Obtain the original business data from all business systems related to each enterprise's business, perform data preprocessing on the original business data, and obtain multi-source business data in a unified data format; The multi-source business data is analyzed according to the business process, and the multi-source business data is associated according to the business logic to build the data association relationship of each enterprise business. The data processing rules of the multi-source business data are sequentially associated according to the business process to obtain data governance rules, and a data association architecture is constructed according to the data association relationship to generate a data governance model. The system obtains business analysis requests, calls multi-source business data of the same business through the data governance model to conduct collaborative business decisions, and feeds back the business decisions to all data sources related to the business to obtain the business-shared data of the current business.

[0006] In a preferred embodiment, this application can be further configured as follows: obtaining the original business data of all business systems related to each enterprise's business, and performing data preprocessing on the original business data to obtain multi-source business data in a unified data format, specifically includes: Based on the enterprise's business, obtain the original business data of all business systems related to the same enterprise business, and clean and store the data according to the original data processing rules of the business systems. By using a preset data conversion format, the raw business data after preliminary processing is converted to obtain standard business data with a unified data format. The standard business data is integrated according to business needs to obtain multi-source business data with a unified data structure for the current enterprise business.

[0007] In a preferred embodiment, this application can be further configured as follows: the step of performing business logic analysis on the multi-source business data according to the business process, and performing data association on the multi-source business data according to the business logic to construct the data association relationship for each enterprise business specifically includes: According to the business process, obtain the node multi-source business data of each business process node, compare it with the node multi-source business data of adjacent business process nodes, and obtain the flow change data of adjacent business process nodes. Analyze the data flow patterns of the changed data, and perform business logic analysis based on the data flow patterns and the business processes to obtain the business logic of each enterprise's business. The multi-source business data are associated according to the business logic, and the data association relationship of each enterprise business is constructed in the order of business process.

[0008] In a preferred embodiment, this application can be further configured as follows: the analysis of the data flow patterns of the changing data, and the business logic analysis based on the data flow patterns and the business process, to obtain the business logic of each enterprise's business, specifically includes: Based on the analysis of the data flow changes, the data flow change trend of each enterprise's business is obtained, and the data flow pattern is obtained based on the data flow change trend and the corresponding data process nodes. Obtain data processing permissions for each data flow node, and adjust the business processing logic of the current data flow node according to the data processing permissions and the data flow rules; Following the order of business processes, the adjusted business processing logic is sequentially linked to obtain the business logic for each enterprise business.

[0009] In a preferred embodiment, this application can be further configured as follows: the step of sequentially associating the data processing rules of the multi-source business data according to the business process to obtain data governance rules, and constructing a data association architecture according to the data association relationship to generate a data governance model, specifically includes: According to the business process, obtain the node multi-source business data of each business process node, and associate the data processing rules of each node multi-source business data with the current business process node to obtain the node data processing rules; According to the business process, the node data processing rules of each business process node are sequentially associated to obtain the data governance rules of the current business. By combining the data relationships and the data governance rules, a data relationship framework for each enterprise's business is constructed, resulting in a data governance model for the enterprise data warehouse.

[0010] In a preferred embodiment, this application can be further configured as follows: the process of obtaining a business analysis request involves calling multi-source business data of the same business through the data governance model to perform collaborative business decisions, feeding back the business decisions to all data sources related to the business, and obtaining the business-shared data of the current business, specifically including: Obtain the business analysis request for each enterprise business, and use the data governance model to call the multi-source business data of the same business corresponding to the business analysis request to obtain the target multi-source business data; Based on the target multi-source business data, the business processing objectives of each business process node are analyzed, and combined with the preset node business processing objectives, the business processing decision of the current business process node is generated. The business processing decisions are synchronously fed back to all business-related data sources, and the data compliance of each data source is evaluated and adjusted to obtain target data source data that conforms to the business processing decisions. According to the preset data sharing decision, the target data source data is shared to all business process nodes of the current business analysis to obtain the business shared data of the current business.

[0011] In a preferred embodiment, this application can be further configured as follows: sharing the target data source data to all business process nodes of the current business analysis according to a preset data sharing decision, to obtain the business-shared data of the current business, specifically includes: According to the preset data sharing decision, the target multi-source business data is hot-backed up, and according to the business processing decision, the target data source data is used to replace and update the hot-backed target multi-source business data. The updated hot backup data is synchronously backed up to all business process nodes of the current business analysis through a distributed framework, thus obtaining the business-shared data of the current business.

[0012] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A data warehouse-based data governance and collaborative sharing processing system, characterized in that the system applies the aforementioned data warehouse-based data governance and collaborative sharing processing method, and the system includes: The data acquisition module is used to acquire the raw business data of all business systems related to each enterprise's business, and to preprocess the raw business data to obtain multi-source business data in a unified data format. The data association module is used to perform business logic analysis on the multi-source business data according to the business process, and to associate the multi-source business data according to the business logic to build the data association relationship of each enterprise business. The model building module is used to sequentially associate the data processing rules of the multi-source business data according to the business process to obtain data governance rules, and to build a data association architecture according to the data association relationship to generate a data governance model. The data sharing module is used to obtain business analysis requests, call multi-source business data of the same business through the data governance model to make collaborative business decisions, and feed back the business decisions to all data sources related to the business to obtain the business-shared data of the current business.

[0013] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described data governance and collaborative sharing processing method based on a data warehouse.

[0014] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described data governance and collaborative sharing processing method based on a data warehouse.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application preprocesses data between multiple business systems based on business operations, unifies data rules and formats, establishes data associations between multiple business systems through business logic analysis, constructs data association relationships between multiple business systems within the same business, improves data relevance between multiple business systems, and refines business data processing to each business process node. A data governance model is constructed through data governance rules and data association relationships at business process nodes, which helps to unify, standardize, and integrate all heterogeneous data in the data warehouse, facilitates data access between multiple business systems, calls multi-source business data of the same business for collaborative business decision-making according to business analysis requests, shares data with relevant data sources through decision feedback, and transfers shared business data to all data sources, achieving data interoperability and interconnection among multiple application systems, thereby improving the efficiency of business decision-making. 2. This application performs preliminary preprocessing on the original business data of each business system and stores it in local memory, thereby reducing the storage space occupied by redundant and repetitive interference data, improving data quality, and converting the preprocessed data into a unified data format to maintain data consistency for the same business. In addition, standard business data is integrated according to business needs to break down data silos and facilitate the sharing and calling of heterogeneous data between multiple application systems during business analysis. 3. This application analyzes the trends and summarizes the data flow patterns by analyzing the changes in data flow between adjacent business process nodes. It then summarizes the business logic of each enterprise's business and, combined with the data governance rules derived from the association of data processing rules of multiple nodes, constructs a data association framework. This facilitates the transformation of multi-source heterogeneous data from each enterprise's business into structured and related data. Consequently, it builds a data governance model for managing the enterprise's data warehouse, which helps to intelligently manage the data warehouse. Furthermore, it enables data collaboration and sharing through hot backup of business analysis data, providing effective data support for the business analysis of the data governance model. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart illustrating the implementation of the data governance and collaborative sharing processing method based on a data warehouse in this embodiment.

[0018] Figure 2 This is a flowchart illustrating the implementation of step S10 of the data governance and collaborative sharing processing method in this embodiment.

[0019] Figure 3 This is a flowchart illustrating the implementation of step S20 of the data governance and collaborative sharing processing method in this embodiment.

[0020] Figure 4 This is a flowchart illustrating the implementation of step S202 of the data governance and collaborative sharing processing method in this embodiment.

[0021] Figure 5 This is a flowchart illustrating the implementation of step S30 of the data governance and collaborative sharing processing method in this embodiment.

[0022] Figure 6 This is a flowchart illustrating the implementation of step S40 of the data governance and collaborative sharing processing method in this embodiment.

[0023] Figure 7 This is a flowchart illustrating the implementation of step S404 of the data governance and collaborative sharing processing method in this embodiment.

[0024] Figure 8 This is a structural block diagram of the data governance and collaborative sharing processing system in this embodiment.

[0025] Figure 9 This is a schematic diagram of the internal structure of a computer device used to implement data governance and collaborative sharing processing methods. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0028] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0029] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0030] In one embodiment, such as Figure 1 As shown, this application discloses a data governance and collaborative sharing processing method based on a data warehouse, which specifically includes the following steps: S10: Obtain the original business data of all business systems related to each enterprise's business, perform data preprocessing on the original business data, and obtain multi-source business data in a unified data format.

[0031] Specifically, such as Figure 2 As shown, step S10 includes: S101: Based on enterprise business, obtain the original business data of all business systems related to the same enterprise business, and clean and store the data according to the original data processing rules of the business systems.

[0032] Specifically, the data in the enterprise data warehouse is organized according to the enterprise's business operations. Raw business data from all business systems within the same business scope is extracted, and the data undergoes initial cleaning according to the original data processing rules of each business system before being stored locally. Data cleaning includes removing invalid, duplicate, missing, and incorrectly formatted data. For example, in a procurement transaction system, if the project procurement budget is 0 or an excessively large number significantly exceeding the normal budget value, then this data needs to be cleaned. Raw business data may include data from procurement transaction systems, bidding and evaluation systems, portal systems, ERP systems, and financial systems.

[0033] S102: By using a preset data conversion format, the raw business data after preliminary processing is converted to obtain standard business data with a unified data format.

[0034] Specifically, according to a preset data conversion format, the original business data is converted into standard business data in the same format. The original data may have various different formats and structures, such as CSV, JSON, and XML. For example, date data formats include "yyyy / mm / dd" and "mm-dd-yyyy", which are uniformly converted to "mm-dd-yyyy". Furthermore, various monetary units in the procurement transaction system are standardized to yuan. A hash value calculated using the business name is used as the data header and associated with each standard business data, facilitating the mutual access of multi-source heterogeneous data between multiple business systems. In this embodiment, data transmission is performed through an API interface.

[0035] S103: Integrate standard business data according to business needs to obtain multi-source business data with a unified data structure for the current enterprise business.

[0036] Specifically, standard business data is integrated according to business needs, and key standard business data that meets business needs is extracted from all business systems to obtain multi-source business data with a unified data structure for the current enterprise business.

[0037] S20: Perform business logic analysis on multi-source business data according to business processes, and associate multi-source business data according to business logic to build data association relationships for each enterprise business.

[0038] Specifically, such as Figure 3 As shown, step S20 includes: S201: Obtain the node multi-source business data of each business process node according to the business process, compare it with the node multi-source business data of adjacent business process nodes, and obtain the flow change data of adjacent business process nodes.

[0039] Specifically, according to the business process, obtain the node multi-source business data of each business process node. For example, in the procurement node, obtain the procurement budget amount, procurement quantity, unit price of goods, and procurement personnel, etc., and compare the node multi-source business data of adjacent business process nodes, including changes in key data items of multi-source business and changes in the values ​​or text of the same key data items, to obtain the flow change data of adjacent business process nodes.

[0040] S202: Analyze the data flow patterns of the changing data, and conduct business logic analysis according to the data flow patterns and business processes to obtain the business logic of each enterprise's business.

[0041] Specifically, such as Figure 4 As shown, step S202 includes: S2021: Analyze the data flow change trend of each enterprise's business based on the data flow change data, and obtain the data flow pattern based on the data flow change trend and the corresponding data process nodes.

[0042] Specifically, based on the data flow changes, we analyze the data flow change trends of each enterprise's business, including the change trends of key data items and the numerical change trends or textual changes of the same key data items, such as changes in procurement funds or procurement personnel. In accordance with the flow sequence of data flow nodes, we conduct correlation analysis on the data flow change trends of the same or similar businesses of the same enterprise, and summarize the commonalities of data flow changes to form data flow patterns.

[0043] S2022: Obtain data processing permissions for each data flow node, and adjust the business processing logic of the current data flow node according to the data processing permissions and data flow rules.

[0044] Specifically, obtain data processing permissions for each data flow node, such as access and call permissions to retrieve data required for business analysis from the database. Based on data processing permissions and data flow rules, optimize and adjust the business processing logic of the current data flow node so that the adjusted business processing logic fits the current business of the enterprise.

[0045] S2023: Following the order of business processes, sequentially associate the adjusted business processing logic to obtain the business logic of each enterprise business.

[0046] Specifically, following the order of business processes, the adjusted business processing logic of each enterprise's business is sequentially linked to obtain the business logic of each enterprise's business.

[0047] S203: Perform data association on multi-source business data according to business logic, and build the data association relationship of each enterprise business in the order of business process.

[0048] Specifically, key business data is extracted from multi-source business data according to business logic to associate data between adjacent business nodes, and the data association relationship of each enterprise business is constructed in order of business process.

[0049] S30: Sequentially associate the data processing rules of multi-source business data according to the business process to obtain data governance rules, and build a data association architecture according to the data association relationship to generate a data governance model.

[0050] Specifically, such as Figure 5 As shown, step S30 includes: S301: Obtain the multi-source business data of each business process node according to the business process, and associate the data processing rules of each node's multi-source business data with the current business process node to obtain the node data processing rules.

[0051] Specifically, the system acquires multi-source business data for each business process node according to the business process flow. Based on the data source, it obtains the data processing rules for each node's multi-source business data. These data processing rules are then associated with the corresponding data items and the current business process node. The associated data items and data processing rules are stored in the node's hot backup storage to obtain the node data processing rules. In this embodiment, data source types include relational databases, non-relational databases, file systems, log systems, cloud storage, and API interfaces.

[0052] S302: According to the business process, the node data processing rules of each business process node are sequentially associated to obtain the data governance rules of the current business.

[0053] Specifically, the node data processing rules of each business process node are sequentially associated according to the business process. That is, each node data processing rule is synchronized to all data process nodes of the current business through a hot backup storage node to obtain the data governance rules of the current business.

[0054] S303: Combining data relationships and data governance rules, construct a data relationship framework for each enterprise's business to obtain the data governance model for the enterprise data warehouse.

[0055] Specifically, by combining data relationships and data governance rules, a data relationship framework for each enterprise business is constructed according to business process nodes, resulting in a data governance framework for managing multi-source heterogeneous data from multiple enterprise businesses in the enterprise data warehouse.

[0056] S40: Obtain business analysis requests, call multi-source business data of the same business through the data governance model to make collaborative business decisions, and feed back the business decisions to all data sources related to the business to obtain the business-shared data of the current business.

[0057] Specifically, such as Figure 6 As shown, step S40 includes: S401: Obtain the business analysis request for each enterprise business, and use the data governance model to call the multi-source business data of the same business corresponding to the business analysis request to obtain the target multi-source business data.

[0058] Specifically, the system obtains business analysis requests from each enterprise's business, performs business requirement analysis through a data governance model, and calls multi-source business data corresponding to the business to obtain target multi-source business data for responding to the business analysis requests.

[0059] S402: Analyze the business processing objectives of each business process node based on the target multi-source business data, and generate the business processing decision for the current business process node by combining the preset node business processing objectives.

[0060] Specifically, based on the target multi-source business data, the business processing target, i.e. the target asset, of each business process node is analyzed. Combined with the preset node business processing target, it is analyzed whether the current business processing target meets the expected data processing requirements. If it does not meet the requirements, the key data items that do not meet the requirements are marked and compared with the preset standard values ​​for improvement, thereby forming the business processing decision for the current business process node.

[0061] S403: Synchronously feed business processing decisions back to all business-related data sources, assess the data compliance of each data source and adjust the data accordingly to obtain target data source data that conforms to the business processing decisions.

[0062] Specifically, through hot backup storage nodes, business processing decisions are synchronously fed back to all business-related data sources. The data compliance of each data source is evaluated, and non-compliant data items are adjusted in data format or replaced based on the evaluation results to obtain target data source data that meets the data processing decisions.

[0063] S404: According to the preset data sharing decision, the target data source data is shared to all business process nodes of the current business analysis to obtain the business shared data of the current business.

[0064] Specifically, such as Figure 7 As shown, step S404 includes: S4041: According to the preset data sharing decision, perform hot backup of the target multi-source business data, and replace and update the hot backup target multi-source business data with the target data source data according to the business processing decision.

[0065] Specifically, according to the preset data sharing decision, the target multi-source data business is hot-backed up, and the target data source data is stored in the hot backup storage node according to the business processing decision, and the existing target multi-source data of the corresponding data item is replaced.

[0066] S4042: The updated hot backup data is synchronously backed up to all business process nodes of the current business analysis through a distributed framework to obtain the business shared data of the current business.

[0067] Specifically, the updated hot backup data is synchronously backed up to all business process nodes involved in the current business analysis through a distributed framework, thus obtaining the business-shared data for the current business.

[0068] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0069] In one embodiment, a data warehouse-based data governance and collaborative sharing processing system is provided, which corresponds one-to-one with the data warehouse-based data governance and collaborative sharing processing methods described in the above embodiments. For example... Figure 8 As shown, this data warehouse-based data governance and collaborative sharing system includes a data acquisition module, a data association module, a model building module, and a data sharing module. Detailed descriptions of each functional module are as follows: The data acquisition module is used to acquire raw business data from all business systems related to each enterprise's business, perform data preprocessing on the raw business data, and obtain multi-source business data in a unified data format.

[0070] The data association module is used to perform business logic analysis on the multi-source business data according to the business process, and to associate the multi-source business data according to the business logic to build the data association relationship of each enterprise business.

[0071] The model building module is used to sequentially associate the data processing rules of the multi-source business data according to the business process to obtain data governance rules, and to build a data association architecture according to the data association relationship to generate a data governance model.

[0072] The data sharing module is used to obtain business analysis requests, call multi-source business data of the same business through the data governance model to make collaborative business decisions, and feed back the business decisions to all data sources related to the business to obtain the business-shared data of the current business.

[0073] Preferably, the data acquisition module includes: The data acquisition submodule is used to acquire the original business data of all business systems related to the same business based on the enterprise's business, and to clean and store the data according to the original data processing rules of the business systems.

[0074] The format conversion submodule is used to convert the raw business data after preliminary processing into standard business data with a unified data format by using a preset data conversion format.

[0075] The data integration submodule is used to integrate the standard business data according to business needs to obtain multi-source business data with a unified data structure for the current enterprise business.

[0076] Preferably, the data association module specifically includes: The data analysis submodule is used to obtain the node multi-source business data of each business process node according to the business process, compare the node multi-source business data of adjacent business process nodes, and obtain the flow change data of adjacent business process nodes.

[0077] The logic analysis submodule is used to analyze the data flow patterns of the changing data and perform business logic analysis based on the data flow patterns and the business process to obtain the business logic of each enterprise business.

[0078] The data association submodule is used to associate the multi-source business data according to the business logic and to build the data association relationship of each enterprise business in the order of business process.

[0079] Preferably, the logic analysis submodule specifically includes: The pattern analysis unit is used to analyze the data flow change trend of each enterprise's business based on the flow change data, and to obtain the data flow pattern based on the data flow change trend and the corresponding data process nodes.

[0080] The logic adjustment unit is used to obtain the data processing permissions of each data flow node and adjust the business processing logic of the current data flow node according to the data processing permissions and the data flow rules.

[0081] The logical association unit is used to sequentially associate the adjusted business processing logic according to the business process sequence to obtain the business logic of each enterprise business.

[0082] Preferably, the model building module specifically includes: The rule analysis submodule is used to obtain the multi-source business data of each business process node according to the business process, and associate the data processing rules of each node's multi-source business data with the current business process node to obtain the node data processing rules.

[0083] The rule association submodule is used to sequentially associate the node data processing rules of each business process node according to the business process to obtain the data governance rules of the current business.

[0084] The model building submodule is used to combine the data association relationships and the data governance rules to build a data association framework for each enterprise business, thereby obtaining the data governance model of the enterprise data warehouse.

[0085] Preferably, the data sharing module specifically includes: The data retrieval submodule is used to obtain the business analysis request of each enterprise business, and to retrieve the multi-source business data of the same business corresponding to the business analysis request through the data governance model to obtain the target multi-source business data.

[0086] The decision analysis submodule is used to analyze the business processing objectives of each business process node based on the target multi-source business data, and generate the business processing decision for the current business process node by combining the preset node business processing objectives.

[0087] The data adjustment submodule is used to synchronously feed back the business processing decision to all business-related data sources, evaluate the data compliance of each data source and adjust the data to obtain target data source data that conforms to the business processing decision; The data sharing submodule is used to share the target data source data to all business process nodes of the current business analysis according to the preset data sharing decision, so as to obtain the business shared data of the current business.

[0088] Preferably, the data sharing submodule specifically includes: The data update unit is used to perform hot backup of target multi-source business data according to preset data sharing decisions, and to replace and update the hot-backed target multi-source business data with the target data source data according to business processing decisions.

[0089] The data sharing unit is used to synchronously back up the updated hot backup data to all business process nodes of the current business analysis through a distributed framework, thereby obtaining the business shared data of the current business.

[0090] For specific limitations regarding the data governance and collaborative sharing processing system based on data warehouses, please refer to the limitations of the data governance and collaborative sharing processing methods based on data warehouses mentioned above, which will not be repeated here. Each module in the aforementioned data governance and collaborative sharing processing system based on data warehouses can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0091] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores intermediate data for data governance and collaborative sharing in the data warehouse. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a data warehouse-based data governance and collaborative sharing processing method.

[0092] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of a data warehouse-based data governance and collaborative sharing processing method.

[0093] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0094] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0095] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A data governance and collaborative sharing processing method based on a data warehouse, characterized in that, The method includes: Obtain the original business data from all business systems related to each enterprise's business, perform data preprocessing on the original business data, and obtain multi-source business data in a unified data format; The multi-source business data is analyzed according to the business process, and the multi-source business data is associated according to the business logic to build the data association relationship of each enterprise business. The data processing rules of the multi-source business data are sequentially associated according to the business process to obtain data governance rules, and a data association architecture is constructed according to the data association relationship to generate a data governance model. The system obtains business analysis requests, calls multi-source business data of the same business through the data governance model to conduct collaborative business decisions, and feeds back the business decisions to all data sources related to the business to obtain the business-shared data of the current business.

2. The data governance and collaborative sharing processing method based on a data warehouse according to claim 1, characterized in that, The process of acquiring raw business data from all business systems related to each enterprise's business, and preprocessing the raw business data to obtain multi-source business data in a unified data format, specifically includes: Based on the enterprise's business, obtain the original business data of all business systems related to the same enterprise business, and clean and store the data according to the original data processing rules of the business systems. By using a preset data conversion format, the raw business data after preliminary processing is converted to obtain standard business data with a unified data format. The standard business data is integrated according to business needs to obtain multi-source business data with a unified data structure for the current enterprise business.

3. The data governance and collaborative sharing processing method based on a data warehouse according to claim 1, characterized in that, The step of performing business logic analysis on the multi-source business data according to the business process, and then performing data association on the multi-source business data according to the business logic to construct the data association relationship for each enterprise business, specifically includes: According to the business process, obtain the node multi-source business data of each business process node, compare it with the node multi-source business data of adjacent business process nodes, and obtain the flow change data of adjacent business process nodes. Analyze the data flow patterns of the changed data, and perform business logic analysis based on the data flow patterns and the business processes to obtain the business logic of each enterprise's business. The multi-source business data are associated according to the business logic, and the data association relationship of each enterprise business is constructed in the order of business process.

4. The data governance and collaborative sharing processing method based on a data warehouse according to claim 3, characterized in that, The analysis of the data flow patterns of the changing data, and the subsequent business logic analysis based on these patterns and the business processes, yields the business logic for each enterprise's business, specifically including: Based on the analysis of the data flow changes, the data flow change trend of each enterprise's business is obtained, and the data flow pattern is obtained based on the data flow change trend and the corresponding data process nodes. Obtain data processing permissions for each data flow node, and adjust the business processing logic of the current data flow node according to the data processing permissions and the data flow rules; Following the order of business processes, the adjusted business processing logic is sequentially linked to obtain the business logic for each enterprise business.

5. The data governance and collaborative sharing processing method based on a data warehouse according to claim 1, characterized in that, The step of sequentially associating the data processing rules of the multi-source business data according to the business process to obtain data governance rules, and constructing a data association architecture according to the data association relationship to generate a data governance model, specifically includes: According to the business process, obtain the node multi-source business data of each business process node, and associate the data processing rules of each node multi-source business data with the current business process node to obtain the node data processing rules; According to the business process, the node data processing rules of each business process node are sequentially associated to obtain the data governance rules of the current business. By combining the data relationships and the data governance rules, a data relationship framework for each enterprise's business is constructed, resulting in a data governance model for the enterprise data warehouse.

6. The data governance and collaborative sharing processing method based on a data warehouse according to claim 1, characterized in that, The process of obtaining a business analysis request involves using the data governance model to call multi-source business data from the same business for collaborative business decision-making, feeding back the business decisions to all relevant data sources, and obtaining the current business's shared data. Specifically, this includes: Obtain the business analysis request for each enterprise business, and use the data governance model to call the multi-source business data of the same business corresponding to the business analysis request to obtain the target multi-source business data; Based on the target multi-source business data, the business processing objectives of each business process node are analyzed, and combined with the preset node business processing objectives, the business processing decision of the current business process node is generated. The business processing decisions are synchronously fed back to all business-related data sources, and the data compliance of each data source is evaluated and adjusted to obtain target data source data that conforms to the business processing decisions. According to the preset data sharing decision, the target data source data is shared to all business process nodes of the current business analysis to obtain the business shared data of the current business.

7. The data governance and collaborative sharing processing method based on a data warehouse according to claim 6, characterized in that, The step of sharing the target data source data to all business process nodes of the current business analysis according to the preset data sharing decision, to obtain the business shared data of the current business, specifically includes: According to the preset data sharing decision, the target multi-source business data is hot-backed up, and according to the business processing decision, the target data source data is used to replace and update the hot-backed target multi-source business data. The updated hot backup data is synchronously backed up to all business process nodes of the current business analysis through a distributed framework, thus obtaining the business-shared data of the current business.

8. A data governance and collaborative sharing processing system based on a data warehouse, characterized in that, The system is applied to the data governance and collaborative sharing processing method based on a data warehouse as described in any one of claims 1-7, and the system includes: The data acquisition module is used to acquire the raw business data of all business systems related to each enterprise's business, and to preprocess the raw business data to obtain multi-source business data in a unified data format. The data association module is used to perform business logic analysis on the multi-source business data according to the business process, and to associate the multi-source business data according to the business logic to build the data association relationship of each enterprise business. The model building module is used to sequentially associate the data processing rules of the multi-source business data according to the business process to obtain data governance rules, and to build a data association architecture according to the data association relationship to generate a data governance model. The data sharing module is used to obtain business analysis requests, call multi-source business data of the same business through the data governance model to make collaborative business decisions, and feed back the business decisions to all data sources related to the business to obtain the business-shared data of the current business.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the data governance and collaborative sharing processing method based on a data warehouse as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the data governance and collaborative sharing processing method based on a data warehouse as described in any one of claims 1 to 7.

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