Bin counting framework

Abstract

The invention provides a data warehouse architecture. The data warehouse architecture comprises a source pasting layer, a dimension layer, a detail layer, a middle layer, a summary layer, an application layer and a data distribution layer, required original complete data are stored in the source pasting layer; the data of the dimension layer is generated by carrying out ETL task cleaning processing on the data of the source pasting layer; the data of the detail layer is generated by combining the data of the source pasting layer with the data of the dimension layer through ETL task cleaning processing; the data of the middle layer is generated by the data of the detail layer and the data of the dimension layer through ETL task processing; data of the summarization layer is generated by processing and summarizing data of at least two layers of the dimension layer, the detail layer and the middle layer through ETL tasks, and an overall data view needed by enterprise decision making is provided; data of the application layer is generated by processing and summarizing data of at least two layers of the dimension layer, the detail layer, the middle layer and the summarization layer through ETL tasks, and final application data is provided; and the data distribution layer is used for distributing the result data of the application layer to each application party database for a data application system to use.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and particularly to a data warehouse architecture. Background Art

[0002] The implementation of cross-border logistics business goes through many different business modules, including airports, customs clearance, tax payment, in-warehouse operations, inbound and outbound, trunk lines, last-mile delivery, etc. Each link is responsible for different business personnel and will generate corresponding business data. Different business personnel focus on different business data. From the perspective of protecting the company's information security, the data of the business department should not be made public to colleagues in all business lines. At the same time, the R & D technical team should not have access to real business data.

[0003] Currently, there are the following problems in the processing of logistics data:

[0004] Data islands are formed among the business data of each department. For example, for the business department responsible for tax payment, it is necessary to understand the business progress of the customs clearance department in real time to reasonably plan the tax payment process. However, due to the ineffective integration of data, it is extremely difficult to interact with data between different departments. The customs clearance department focuses on the customs clearance rate, while the tax payment department focuses on the tax payment situation of the recipient. The data dimensions of the two are different, resulting in low collaboration efficiency.

[0005] The data storage, cleaning and service system cannot guarantee the security of the company's data. Business information is concentrated on a data dashboard, resulting in different business departments being able to view each other's business data, which is not only unnecessary but also increases the risk of data leakage. In addition, R & D personnel can directly access business data during data analysis, posing a serious security risk. Worse still, if the company relies on a third-party platform for data analysis and display, the risk of data leakage is further expanded.

[0006] The data service system is inefficient, resulting in slow development speed and poor service experience. Existing data application services often occupy the database resources of the business system, causing the business system to run slowly and even affecting normal logistics operations.

[0007] Data updates are lagging and rely on manual intervention. The current system only supports data updates once a day, which is completed manually. It not only takes time and effort but also cannot meet the needs of real-time business analysis.

[0008] There is a lack of a quick response mechanism for abnormal situations. Emergencies in cross-border logistics (such as customs clearance delays or abnormal charges) need to be resolved through data analysis in a timely manner. However, the existing system lacks efficient data monitoring and feedback capabilities, resulting in untimely problem-solving and having a negative impact on the overall business.

[0009] Therefore, how to solve the above problems is the focus of attention of those skilled in the art. Summary of the Invention

[0010] The object of the present invention is to propose a data warehouse architecture, which can at least solve one of the problems in the background art.

[0011] To achieve the above object, the present invention provides a data warehouse architecture, including: a source layer, a dimension layer, a detail layer, an intermediate layer, a summary layer, an application layer, and a data distribution layer;

[0012] The source layer stores the required original complete data;

[0013] The data in the dimension layer is generated by cleaning and processing the data in the source layer through ETL tasks;

[0014] The data in the detail layer is generated by cleaning and processing the data in the source layer in combination with the data in the dimension layer through ETL tasks;

[0015] The data in the intermediate layer is generated by processing the data in the detail layer and the data in the dimension layer through ETL tasks;

[0016] The data in the summary layer is generated by processing and summarizing at least two layers of data among the dimension layer, the detail layer, and the intermediate layer through ETL tasks, providing an overall data view required for enterprise decision-making;

[0017] The data in the application layer is generated by processing and summarizing at least two layers of data among the dimension layer, the detail layer, the intermediate layer, and the summary layer through ETL tasks, providing the final application data;

[0018] The data distribution layer is used to distribute the result data of the application layer to each application party database for use by the data application system.

[0019] In an alternative solution, the ETL tasks for the data to flow from the source layer to the dimension layer include: extracting business dimensions through dimension modeling methods and standardizing the business dimensions.

[0020] In an alternative solution, the ETL tasks for the data to flow to the detail layer include: based on the data in the source layer and the data in the dimension layer, refining and standardizing the data according to business themes.

[0021] In an alternative solution, the ETL tasks for the data to flow to the intermediate layer include: based on the data in the dimension layer and the data in the detail layer, realizing the splitting, processing, and recombining of complex business requirements.

[0022] In an alternative solution, refining and standardizing the data according to business themes includes: dividing the company's business into business themes, extracting the core data models of each theme, and constructing the data of each theme in the detail layer through dimension modeling.

[0023] In an alternative solution, the data warehouse architecture further includes: a business system library, a business data off-site disaster recovery library, and an incremental staging area;

[0024] The business system library is used to collect business data in real time and synchronize the business data to the business data off-site disaster recovery library;

[0025] The incremental staging area stores historical data, and extracts incremental data from the business data off-site disaster recovery library in real time to distinguish historical data from incremental data;

[0026] The source layer obtains the data in the incremental staging area in two ways: incremental and full volume.

[0027] In an alternative solution, the source layer is distinguished according to the data type, and the dimensional data flows into the dimension layer, and the detailed data flows into the detail layer.

[0028] In an alternative solution, when the source layer obtains data from the incremental staging area, the incremental data is extracted in real time by using a change capture mechanism; the full volume data is extracted regularly.

[0029] The beneficial effects of the present invention are as follows:

[0030] By centrally storing, processing, calculating, and managing various types of structured and semi-structured data, the present invention provides enterprises with unified reporting, data query, and data analysis capabilities, significantly improving the overall efficiency and business value of data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] By describing the exemplary embodiments of the present invention in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present invention will become more apparent. In the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0032] Figure 1 It is a schematic diagram of a data warehouse architecture in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to the drawings and specific embodiments. According to the following description and drawings, the advantages and features of the present invention will be clearer. However, it should be noted that the concept of the technical solution of the present invention can be implemented in many different forms and is not limited to the specific embodiments described herein. The drawings are all in a very simplified form and use non-precise scales, only for the purpose of facilitating and clearly assisting in explaining the purpose of the embodiments of the present invention.

[0034] It should be understood that when an element or layer is referred to as being "on", "adjacent to", "connected to" or "coupled to" another element or layer, it can be directly on, adjacent to, connected or coupled to the other element or layer, or intervening elements or layers may be present. In contrast, when an element is referred to as being "directly on", "directly adjacent to", "directly connected to" or "directly coupled to" another element or layer, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc. may be used to describe various elements, components, regions, layers and / or portions, these elements, components, regions, layers and / or portions should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or portion from another. Thus, a first element, component, region, layer or portion discussed below may be denoted as a second element, component, region, layer or portion without departing from the teachings of the present invention.

[0035] Spatial relationship terms such as "under", "below", "lower", "beneath", "above", "upper", etc. are used herein for convenience in describing the relationship of one element or feature to another element or feature shown in the figures. It should be understood that, in addition to the orientation shown in the figures, spatial relationship terms are intended to include different orientations of the device in use and operation. For example, if the device in the figures is flipped, then an element or feature described as "under" or "beneath" or "below" another element or feature will be oriented "on" the other element or feature. Thus, the exemplary terms "under" and "below" can include both an upper and a lower orientation. The device may be otherwise oriented (rotated 90 degrees or other orientations) and the spatial descriptors used herein are to be interpreted accordingly.

[0036] The purpose of the terms used herein is only to describe specific embodiments and is not a limitation of the present invention. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, specify the presence of the stated features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups. As used herein, the term "and / or" includes any and all combinations of the associated listed items.

[0037] In cross-border logistics operations, data management and visualization are of great significance. The logistics process is complex and spans multiple regions and countries. Accurately and in real-time grasping the data of each link is the key to optimizing logistics operations. Through effective data management, it is possible to integrate and clean the massive amounts of data generated in each link, eliminate information silos, and ensure the flow and collaboration of data among various business modules. At the same time, data visualization can present key information such as logistics trajectories, customs clearance status, and warehouse usage in an intuitive form, helping business personnel monitor logistics operations in real-time, identify potential problems, and make quick decisions.

[0038] In addition, data management is also crucial for enhancing the customer experience. By analyzing and mining logistics data, enterprises can optimize delivery routes, shorten delivery times, and provide customers with accurate package status updates, thereby enhancing customer satisfaction. In the global competition, the ability to make data-driven decisions has become an important means for cross-border logistics companies to build their core competitiveness.

[0039] Example 1

[0040] Referring to Figure 1 , this example provides a data warehouse architecture, including: a source layer (ODS layer), a dimension layer (DIM layer), a detail layer (DWD layer), an intermediate layer (DWM layer), a summary layer (DWS layer), an application layer (ADS layer), and a data distribution layer (DDS layer);

[0041] The source layer (ODS layer) stores the required original and complete data;

[0042] The data in the dimension layer (DIM layer) is generated by cleaning and processing the data in the source layer (ODS layer) through ETL tasks;

[0043] The data in the detail layer (DWD layer) is generated by cleaning and processing the data in the source layer (ODS layer) in combination with the data in the dimension layer (DIM layer) through ETL tasks;

[0044] The data in the intermediate layer (DWM layer) is generated by processing the data in the detail layer (DWD layer) and the data in the dimension layer (DIM layer) through ETL tasks;

[0045] The data in the summary layer (DWS layer) is generated by processing and summarizing at least two layers of data among the dimension layer (DIM layer), the detail layer (DWD layer), and the intermediate layer (DWM layer) through ETL tasks, providing an overall data view required for enterprise decision-making;

[0046] The data of the application layer (ADS layer) is generated by processing and summarizing the data of at least two of the dimension layer (DIM layer), the detail layer (DWD layer), the intermediate layer (DWM layer), and the summary layer (DWS layer) through ETL tasks, providing the final application data;

[0047] The data distribution layer (DDS layer) is used to distribute the result data of the application layer (ADS layer) to each application party database for use by the data application system.

[0048] ① It refers to the data in the ODS layer being generated by extracting data from the source database (such as the incremental staging area) through ETL.

[0049] ② It refers to the DIM layer data being generated by cleaning and processing the ODS layer data through ETL tasks.

[0050] ③ It refers to the DWD layer data being generated by cleaning and processing the ODS layer data in combination with the DIM layer data through ETL tasks.

[0051] ④ It refers to the DWM layer data being generated by processing the DWD layer and DIM layer data through ETL tasks.

[0052] ⑤ It refers to the DWS layer data that can be generated by processing and summarizing the DWD layer, DIM layer, and DWM layer data through ETL tasks.

[0053] ⑥ It refers to the ADS application layer data that can be generated by processing and summarizing the DWD layer, DIM layer, DWM layer, and DWS layer data through ETL tasks.

[0054] Specifically, in this embodiment, the data warehouse architecture further includes: a business system library, a business data off-site disaster recovery library, and an incremental staging area; the business system library is used to collect business data in real time and synchronize the business data to the business data off-site disaster recovery library; the incremental staging area stores historical data and extracts incremental data from the business data off-site disaster recovery library in real time to distinguish historical data from incremental data; the source-attached layer (ODS layer) obtains the data in the incremental staging area in two ways: incrementally and fully.

[0055] The off-site disaster recovery library for business data synchronizes data to Hong Kong through DRS (Huawei Cloud Data Replication Service) for off-site disaster recovery, improving the risk resistance ability of business data. The incremental staging area stores historical data. When data is loaded into the warehouse, the incremental data in the off-site disaster recovery library for business data is synchronously replicated in real time (which means data is extracted by the minute / second, while the existing technology updates data once a day) to the incremental staging area of the data warehouse (offline synchronization staging STG layer, real-time synchronization DELTA layer) through the data integration service of DataWorks (Alibaba Cloud Big Data Development and Governance Platform). The incremental staging area can retrieve data from the off-site disaster recovery library for business data in an offline state. When data is loaded into the warehouse, data collection is performed through the off-site disaster recovery environment, reducing the load on the online business system library, decoupling the data warehouse from the business system library, and reducing the risk of downtime of the online primary database.

[0056] Through the combination of Alibaba Cloud DataWorks and the cloud-native big data computing service MaxCompute with an incremental merge algorithm, the data in the incremental staging area is merged into the source layer (full snapshot table or historical table) for subsequent data processing and cleaning.

[0057] The data in the source layer (ODS layer) is generated by extracting the data in the incremental staging area through ETL. ETL is a core concept in data processing, representing three steps: Extract, Transform, and Load. It is a key process for building a data warehouse, aiming to integrate scattered and heterogeneous data into a unified target system to support subsequent analysis and applications. When the source layer (ODS layer) retrieves data from the incremental staging area, a high-performance ETL tool (such as Alibaba Cloud DataWorks) is used to connect to the incremental staging area, and business data is obtained in two ways: incremental and full volume. Incremental data is extracted in real time using a change capture mechanism to ensure real-time performance; full volume data is extracted periodically to maintain integrity. Key technologies during data extraction (Extract): Data synchronization optimization: Parallel extraction and partition scanning strategies are adopted to improve the efficiency of large-scale data extraction. Data consistency guarantee: Transaction consistency control (such as distributed transactions or snapshot isolation) is implemented during the extraction process. After the extracted data is loaded into the source layer (ODS layer), it is initially archived and classified according to data topics, while retaining the original characteristics of the data to ensure data traceability.

[0058] After the data enters the ODS layer, it is distinguished according to the data type to determine whether the data flows into the DIM layer or the DWD layer. The dimensional data flows into the dimension layer (DIM layer), and the detailed data flows into the detail layer (DWD layer). Dimensional data refers to data whose data type belongs to entities, such as information like the names, phone numbers, and addresses of customers, suppliers, or individuals. Detailed data refers to data generated according to business scenario requirements. It refers to data that records specific business activities or transactions. For example, order numbers and the order placement times of orders.

[0059] The ETL tasks for the data to flow from the source-attached layer (ODS layer) to the dimension layer (DIM layer) include: 1. Dimension extraction and modeling: Extract business dimensions (such as products, customers, time, regions) through dimension modeling methods and standardize the business dimensions. The design of dimension tables follows the combined principle of normalization and denormalization, ensuring both the rationality of the structure and optimizing query performance. Key technologies: The combination of star model and snowflake model to improve query efficiency and flexibility. Historical change records: Use SCD (Slowly Changing Dimension) technology to track the historical changes of dimensional data. 2. Data standardization and cleaning: Clean the data through data quality rules (such as uniqueness, integrity, format verification) and perform encoding normalization processing (such as unified time format, regional coding). For example, unify the time dimension to the ISO 8601 standard and connect the region dimension to the GB / T 2260 standard.

[0060] The dimension layer (DIM layer) stores master data that has a greater relationship with the business, such as products, customers, time, regions, etc. Dimensional data is mainly used for data classification and induction, making data access and analysis more convenient and fast, and can record historical change information. The dimension layer (DIM layer) is an indispensable part of the data warehouse, providing important support for data analysis and report generation. Through the analysis and induction of the company's business, abstractly extract the dimensional information of the company's business, establish a dimensional model closely related to the company's business development, and perform data processing on the dimensional model through the relevant raw data of the source-attached layer (ODS layer) to provide dimensional support for subsequent data cleaning and processing. Cleaning different-dimensional data into the same dimension is the concept of data upscaling or downscaling. It can be understood as to what extent the data needs to be cleaned and with what intensity to clean the data. Detailed data has dimensions, and it is necessary to determine how detailed the data needs to be cleaned. For example, the data required by Department A needs to be cleaned to the order dimension, and the data required by Department B is more detailed and needs to be specific to the dimension of order detail items. At this time, the data warehouse will clean out a table at the order level and a table at the order detail level. The data warehouse will combine the required data intensity according to the data intensity required by the business department. It is not to merge the data of different business departments, but to store different models for the data intensity requirements of different business needs and create tables for different business requirements based on the associated models.

[0061] The ETL tasks for the data flow to the detailed layer (DWD layer) include: 1. Data cleaning and transformation: Based on the data in the source-attached layer (ODS layer) and the dimension layer (DIM layer), the data is refined and standardized according to business themes. Specifically, the company's business is divided into business themes, and the core data models of each theme are refined. The data of each theme in the detailed layer is constructed through dimensional modeling. It is mainly responsible for further cleaning, transforming, and loading the data to form a data format that conforms to the data warehouse specification, providing detailed data support for subsequent data analysis and mining. Data cleaning: Remove redundant fields, repair missing values and error values to ensure the integrity and consistency of the data. Data transformation: Implement data type conversion, unit conversion, and business semantic mapping (such as unifying the order amount to the local currency) through a rule engine. 2. Fine-grained model construction: Guided by the theme modeling method, the core data models related to business themes are designed (such as user behavior analysis model, sales transaction model). The granularity of the detailed data is accurate to the event level, meeting the high-precision analysis requirements. The detailed layer (DWD layer) is the detailed data layer in the data warehouse, also known as the isolation layer between the ODS layer or the business layer and the data warehouse.

[0062] The ETL tasks for the data flow to the intermediate layer (DWM layer) include: Based on the data in the dimension layer (DIM layer) and the data in the detailed layer (DWD layer), the splitting, processing, and recombining of complex business requirements are realized. Specifically: Based on the data in the DWD layer and the DIM layer, use ETL tools to complete cross-theme data integration and complex calculations. For example, correlation analysis: Realize the correlation of multiple data tables (such as the multi-table join of the order table, customer table, and product table), adapting to the needs of multi-department collaboration and high-level decision-making. Business logic operations: Perform operations such as cumulative statistics, year-on-year and month-on-month calculations, and hierarchical summarization to provide support for multi-dimensional analysis. For example, when receiving a very complex business scenario or requirement, an upper-layer requirement requires the support of an entire detailed table in the lower layer and also needs to be combined with a dimension table, and it is very troublesome to directly process. In this case, a large requirement needs to be decomposed into small modules, and then the small modules are combined. Some intermediate dimension tables or statistical tables are established in the DWM layer, and some complex tasks are decomposed through the DWM layer.

[0063] The ETL tasks for the data flow to the summary layer (DWS layer) include: Data grouping and summarization: Generate multi-dimensional summary tables according to business dimensions (such as region, time, product). Trend analysis: Perform predictive analysis based on time series data (such as sales trend prediction, customer churn rate analysis). Cross-system data integration: Support the integration of multi-source data across departments (such as the integration of financial data and operation data), enhancing the data value.

[0064] The application layer (ADS layer) is mainly used to support the data storage and processing of application systems, and contains data that has been cleaned, transformed, and integrated. The application layer is designed based on requirements, and according to the requirements of data application business parties, it aggregates relevant data and provides the final application data. Such as data dashboards, data reports, financial middle platforms, etc. The ETL tasks for the data flowing to the application layer (ADS layer) include: Requirement-oriented data optimization: According to business application requirements (such as large screen display, real-time monitoring), further optimize and output at least two layers of data. Use streaming processing technology (such as Flink) to update data in real time (real-time requirement). Based on API design, support flexible query and call (interactivity requirement). Data productization: Data is processed into specific application products, including visual reports, decision support systems (DSS), and business dashboards. The data product output methods are diverse (such as API interfaces, file exports, real-time charts). For example, only the ID of the customer service is stored in the summary layer, but in the application layer, in addition to the value of this statistical indicator, the name of the customer service is also required. At this time, it will be associated with the dimension layer to retrieve the name of the customer service and the indicator and place them in the application layer. At this time, the application layer stores a piece of data that fully meets the requirements.

[0065] The data distribution layer (DDS layer) is a data distribution service layer that pushes the result data of the ADS layer to the databases of each application party for use by data application systems, and uses various distribution methods (such as batch distribution, real-time push, streaming distribution). Data encryption and permission control: Ensure the security of data during the distribution process. Data transmission optimization: Use incremental synchronization and compression technology to improve the distribution efficiency. Provide a unified data interface externally, using RESTful API design to enable rapid access and flexible call by data consumers.

[0066] This embodiment uses a data hierarchical governance system to solve the data island problem between different business departments. Specifically: By adopting a hierarchical architecture design (ODS layer, DIM layer, DWD layer, ADS layer, etc.), standardization, structuring, and flexibility are achieved during the data processing process. For example, the ODS layer focuses on the original collection and storage of data to ensure data integrity, which lays a foundation for solving the data island problem. The DIM layer standardizes business dimensions, and through a unified data perspective, breaks through the data barriers across departments, effectively improving the collaboration efficiency. The DWD layer further optimizes the data query performance through theme modeling and fine-grained processing to meet multi-dimensional analysis requirements. The ADS layer focuses on theme-based and application-based distribution, and through flexible configuration, supports rapid response to different business scenarios, thus significantly improving the development efficiency and system performance, and providing high-quality data services for business departments.

[0067] This embodiment introduces a hybrid strategy of real-time collection and offline calculation, which combines stream processing technologies (such as Flink) with batch processing technologies (such as MaxCompute) to fundamentally solve the problem of data update lag. Specifically: The real-time collection mechanism captures incremental data (real-time extraction of incremental data from the off-site disaster recovery database of business data), ensuring the real-time update of key business data (such as order status, customs clearance progress), thus supporting dynamic business requirements such as order monitoring and anomaly detection; at the same time, offline (offline means that the incremental staging area stores historical data) calculation focuses on the in-depth analysis of large-scale historical data, providing strong support for trend prediction and strategic planning. For example, real-time data streams can be used to identify anomalies in the logistics chain and respond quickly, while offline data calculation can support multi-dimensional evaluation of logistics performance and optimization decisions. This real-time and offline combination mode greatly improves the dynamic monitoring ability of cross-border logistics data, ensuring the timeliness and comprehensiveness of data.

[0068] This embodiment ensures the safe and seamless data flow between different roles and departments by designing a fine-grained data isolation strategy. For example, the development team can only access data that has been anonymized or desensitized, fundamentally eliminating the possibility of direct contact with real business data and effectively preventing the risk of data leakage. At the same time, the business team can only access specific data slices related to their responsibilities according to clear permission settings, ensuring the standardization and accuracy of cross-departmental data use. In addition, the system also tracks data access behaviors through logging and auditing functions, further strengthening the controllability and compliance of data security. Fine-grained data processing means dividing data into sufficiently fine levels, which can be accurate to the event level or specific business dimensions (such as orders, customers, products).

[0069] After the data is processed in a fine-grained manner, specific fine-grained data can be allocated to different departments or roles through permission control. For example: The customs clearance department only needs to access data on the customs clearance status of orders. The customer service department may only need the delivery details of orders. This fine-grained data division ensures that each department can only access data related to its business, preventing unnecessary data exposure and thus achieving data isolation.

[0070] This embodiment extracts data from the disaster recovery database of the business system into the warehouse, and combines high-performance ETL technology for comprehensive cleaning, transformation and processing, and finally synchronizes the high-quality, structured result data to the application databases of each business department. Specifically:

[0071] 1. Data extraction realizes the unified acquisition of multi-source heterogeneous data, ensuring the comprehensive coverage of business system data.

[0072] 2. During the data cleaning process, missing values and outliers are repaired, improving the accuracy and consistency of the data.

[0073] 3. Data processing includes dimensional modeling and theme division, providing targeted theme analysis support for business departments.

[0074] 4. Data synchronization adopts a combination of real-time and offline methods, meeting the requirements of different departments for data real-time and stability.

[0075] 5. Data output supports diverse forms, such as reports, large-screen displays, API calls, etc., enhancing the flexibility and usability of business scenarios.

[0076] Through the above processes, the utilization efficiency and decision-making value of data have been significantly improved, providing reliable data support and insight capabilities for business departments.

[0077] This embodiment is based on the Alibaba Cloud big data development and governance platform DataWorks, building an enterprise-level data warehouse (DataWareHouse, i.e., DW) and a department-level data mart (Data Mart, i.e., DM), solving the following problems:

[0078] Data island problem: By constructing an enterprise-level data warehouse and data mart, centralized management and integration of data are achieved, breaking down data barriers between business modules, and enhancing cross-departmental data sharing and collaboration capabilities.

[0079] Data security problem: Through data hierarchical governance, it is ensured that each business department can only access necessary data, while preventing the R & D team from accessing real business data, fundamentally improving data security.

[0080] Development efficiency and system performance problems: By standardizing data storage and cleaning processes, the development efficiency of data services is improved, direct dependence on the business system database is avoided, and system lags caused by database resource contention are reduced.

[0081] Data update lag problem: Introducing a combination of real-time and offline data processing methods realizes dynamic data updates, meets real-time query and analysis requirements, and supports flexible decision-making for business.

[0082] Insufficient exception handling ability: Through comprehensive monitoring and real-time analysis of data, a rapid response mechanism can be established to improve the handling efficiency of emergencies (such as customs clearance delays, abnormal fees) in cross-border logistics, ensuring business continuity and customer satisfaction.

[0083] This embodiment has the following beneficial effects:

[0084] 1. Data integration and sharing

[0085] By means of a unified data warehouse architecture, data silos are broken, and cross-departmental data integration and sharing are achieved. For example, the customs clearance and tax payment departments can view data related to their own operations in real time through the same data platform, significantly improving collaboration efficiency and avoiding the impact of information lag on the overall process.

[0086] 2. Enhanced security

[0087] Through hierarchical data governance, the security of sensitive data is ensured. For example, R & D personnel can only access anonymized or desensitized data, completely isolating real business data and avoiding the leakage of sensitive information.

[0088] 3. Improved data utilization efficiency

[0089] Through standardized data processing procedures, the efficiency of data query, analysis, and report generation is significantly improved. For example, the warehousing department can optimize goods storage based on automatically generated inventory reports, while the operations department can use data analysis to more accurately plan transportation routes, reduce costs, and improve customer satisfaction.

[0090] 4. Reduced system risks

[0091] By reducing the direct dependence on the business system database, the stability of the system is enhanced. For example, during peak periods, even if the volume of order data queries surges, the system performance can remain stable, avoiding affecting the normal operation of core business.

[0092] 5. Timely data updates

[0093] Combining real-time and offline data processing technologies to meet data requirements in different scenarios. For example, real-time data processing can help quickly identify order anomalies such as customs clearance delays or logistics hold-ups and notify relevant departments for handling in real time; while offline data provides a basis for in-depth analysis to support long-term strategic decision-making by management.

[0094] 6. Quick response ability to abnormal events

[0095] Through real-time monitoring, abnormal events can be quickly located in cross-border logistics. For example, when the system detects a customs clearance delay in a certain region, it can immediately generate an alarm and notify the relevant team to take countermeasures, minimizing the impact on customer delivery timeliness to the greatest extent.

[0096] 7. Improved customer satisfaction

[0097] By integrating and visualizing data, accurate and real-time logistics tracking information is provided to customers. For example, customers can view the latest status of packages in real time through the platform, such as the estimated arrival time, customs clearance progress, etc., enhancing the customer experience and trust.

[0098] 8. Support for business expansion

[0099] With flexible data processing and analysis capabilities, it supports enterprises to quickly adapt to new market demands. For example, when a company expands new logistics routes or cross-border e-commerce services, the data system can quickly integrate the newly added business data to ensure the smooth development of new businesses.

[0100] This embodiment comprehensively optimizes the data management process in the field of cross-border logistics through multi-level data processing and distribution, not only significantly improving the operational efficiency of enterprises, but also providing a solid technical foundation for future development.

[0101] The above description is only a description of the preferred embodiments of the present invention, and does not limit the scope of the present invention in any way. Any changes and modifications made by those of ordinary skill in the art of the present invention based on the above disclosure shall fall within the scope of protection of the claims.

Claims

1. A data warehouse architecture, characterized in that, Including: Source-attached layer, dimension layer, detail layer, intermediate layer, summary layer, application layer, and data distribution layer; The source-attached layer stores the required original complete data; The data in the dimension layer is generated by cleaning and processing the data in the source-attached layer through an ETL task; The data in the detail layer is generated by cleaning and processing the data in the source-attached layer in combination with the data in the dimension layer through an ETL task; The data in the intermediate layer is generated by processing the data in the detail layer and the data in the dimension layer through an ETL task; The data in the summary layer is generated by processing and summarizing at least two layers of data among the dimension layer, the detail layer, and the intermediate layer through an ETL task, providing an overall data view required for enterprise decision-making; The data in the application layer is generated by processing and summarizing at least two layers of data among the dimension layer, the detail layer, the intermediate layer, and the summary layer through an ETL task, providing the final application data; The data distribution layer is used to distribute the result data of the application layer to each application party database for use by the data application system.

2. The data warehouse architecture according to claim 1, wherein The ETL tasks for the data to flow from the source-attached layer to the dimension layer include: extracting business dimensions through the dimension modeling method and standardizing the business dimensions.

3. The data warehouse architecture according to claim 1, wherein The ETL tasks for the data to flow to the detail layer include: based on the data in the source-attached layer and the data in the dimension layer, refining and standardizing the data according to the business theme.

4. The data warehouse architecture according to claim 1, characterized in that The ETL tasks for the data to flow to the intermediate layer include: based on the data in the dimension layer and the data in the detail layer, splitting, processing, and recombining complex business requirements.

5. The data warehouse architecture according to claim 3, wherein Refining and standardizing the data according to the business theme includes: dividing the company's business into business themes, refining the core data models of each theme, and constructing the data of each theme in the detail layer through dimension modeling.

6. The data warehouse architecture according to claim 1, wherein The data warehouse architecture further includes: Business system library, off-site disaster recovery library for business data, and incremental staging area; The business system library is used to collect business data in real time and synchronize the business data to the off-site disaster recovery library for business data; The incremental staging area stores historical data, extracts incremental data from the off-site disaster recovery library for business data in real time, and distinguishes historical data from incremental data; The source-attached layer obtains the data in the incremental staging area in two ways: incrementally and fully.

7. The data warehouse architecture according to claim 6, characterized in that, The source-attached layer is distinguished according to the data type, and the dimension data flows into the dimension layer, and the detail data flows into the detail layer.

8. The data warehouse architecture according to claim 6, wherein When the source-attached layer obtains data from the incremental staging area, the incremental data is extracted in real time by the change capture mechanism; the full data is extracted regularly.

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