Multi-system order management system and management method

Abstract

The embodiment of the invention provides a multi-system order management system and management method, and belongs to the technical field of order management. A multi-system order management system is applied to multi-system order integrated management in the logistics industry, and is characterized by comprising a data acquisition layer used for docking each data source and acquiring logistics order data based on each data source; the data exchange layer is used for performing data preprocessing on the logistics order data of each data source; the data processing layer is used for obtaining processing data of the business requirements corresponding to the data types; the data service layer is used for matching the corresponding target systems based on the processing data and correspondingly transmitting the processing data to the target systems in the same data format based on the transmission protocols of the target systems; and the target system layer executes an order management scheme corresponding to the target system based on the received processing data. According to the scheme, the efficiency, the reliability and the expandability of the logistics management system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of order management solutions, and in particular to a multi-system order management system and a multi-system order management method. Background Art

[0002] The logistics industry has experienced rapid growth in recent years, particularly driven by e-commerce and global trade. The complexity and scale of logistics operations have increased significantly. This has placed higher demands on logistics management systems, particularly order and waybill management systems. As the core data carrier in the logistics process, the efficiency of its processing and management directly impacts the operational efficiency and service quality of the entire logistics system. However, existing traditional logistics systems suffer from numerous issues, making them unable to meet the growing demands of the modern logistics industry.

[0003] Traditional logistics systems are inefficient in data integration. Because the logistics industry involves multiple processes and systems (such as warehousing, transportation, and finance), data sources are diverse and formatted in non-standard ways. Data transmission and sharing between these systems often requires manual operations or complex interface configuration. This not only increases system complexity but also leads to delays in data synchronization and updates, compromising the real-time and accuracy of logistics information. Existing order management systems have limited data processing capabilities. When it comes to large-scale data processing and analysis, traditional systems are unable to rapidly adapt to the massive volume of orders and changes in logistics information. Especially during peak periods or sudden demand surges, the systems can experience slow responses and data processing errors, directly impacting logistics efficiency and service reliability. Furthermore, pre-processing steps such as data cleansing, format conversion, and data validation in existing systems rely heavily on manual labor and lack automated and intelligent support, further reducing data processing efficiency. Traditional logistics systems also have a low level of process automation. While some systems have basic automation capabilities, most still rely on manual intervention, particularly in order generation, review, and scheduling, where manual operations are frequently required. This not only increases operating costs but also easily leads to data inconsistencies and process interruptions due to human errors, impacting the overall reliability of logistics services.

[0004] As can be seen, existing order management systems have many shortcomings in data integration, data processing capabilities, and process automation, making them difficult to adapt to the modern logistics industry's demand for efficient, reliable, and scalable systems. Therefore, a new solution is urgently needed to improve the performance of logistics systems and meet the requirements of industry development. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a multi-system order management system and management method to at least solve the many deficiencies in existing order and shipping order management systems in terms of data integration, data processing capabilities and process automation.

[0006] In order to achieve the above-mentioned objectives, the first aspect of the present invention provides a multi-system order management system, which is applied to the multi-system order integration management in the logistics industry, and the system includes: a data acquisition layer, which is used to connect to various data sources and collect logistics order data based on each data source; a data exchange layer, which is connected to the data acquisition layer, is used to receive logistics order data from each data source, and perform data preprocessing on the logistics order data of each data source; a data processing layer, which is connected to the data exchange layer, is used to perform adaptive data processing based on the preprocessed logistics order data type of each data source to obtain processed data corresponding to business requirements of each data type; a data service layer, which is connected to the data processing layer, is used to match the corresponding target system based on each processed data, and transmit each processed data to each target system in the same data format based on the transmission protocol of each target system; a target system layer, which is connected to the data service layer, is used to receive the corresponding processed data, and execute the order management solution of the corresponding target system based on the received processed data.

[0007] Optionally, the data collection layer is configured to: identify the data sources that need to be connected according to different business systems in the logistics industry, and configure corresponding collection interfaces and collection methods for each data source; based on the configured collection interfaces and collection methods, execute data collection of each data source in parallel to obtain the data sets corresponding to each data source.

[0008] Optionally, the data exchange layer is configured to: based on the data source protocol corresponding to the logistics order data of each data source, uniformly convert the logistics order data of each data source into a preset standard format; perform data cleansing and data verification on the logistics order data of each data source in the preset standard format to obtain basic data that has passed integrity verification and consistency verification; perform data conversion and / or data mapping on the basic data to obtain converted data; and forward the converted data to the data processing layer based on preset standards.

[0009] Optionally, performing data conversion and / or data mapping on the basic data to obtain converted data includes: parsing each basic data to identify fields and data content that need to be converted and / or mapped; matching corresponding data conversion rules based on the fields and data content that need to be converted and / or mapped of each basic data; wherein the data conversion rules include any one or more of data extraction, format conversion and data dictionary relationship mapping; based on the matched data conversion rules, extracting and executing corresponding data processing functions and / or scripts, performing corresponding basic data conversion and / or data mapping, and obtaining converted data.

[0010] Optionally, the data processing layer includes: a data cleaning component for cleaning the preprocessed data; a data conversion component for performing format conversion on the preprocessed data; a data processing component for performing adaptive data processing on the preprocessed data; a data mapping component for mapping the preprocessed data from the original structure to the target structure; a data filtering component for performing preset data filtering based on the functional requirements corresponding to the logistics order data type of each data source after preprocessing; and a data verification component for verifying the preprocessed data.

[0011] Optionally, the adaptive data processing includes any one or more of data aggregation processing, data screening processing, and data calculation processing.

[0012] Optionally, the data processing component is configured to: match the corresponding business needs based on the pre-processed logistics order data type of each data source; based on the matched business needs, match the corresponding adaptive data processing rules in the pre-built data processing rule library; based on the matched adaptive data processing rules, execute the corresponding pre-processed logistics order data processing of each data source to obtain the processed data corresponding to the business needs of each data type.

[0013] Optionally, the data service layer is configured to: match the corresponding target system based on each processed data, and identify the data transmission protocol of each target system; establish a connection relationship with the database of each target system based on the corresponding data transmission protocol; and transmit the corresponding processed data in a unified format to the target system layer based on the established connection relationship, so that each target system can execute the corresponding order management plan based on the received processed data.

[0014] The second aspect of the present invention provides a multi-system order management method, which is applied to the multi-system order integration management in the logistics industry. The method is executed based on the above-mentioned multi-system order management system, and the method includes: connecting to each data source, and collecting logistics order data based on each data source; performing data preprocessing on the logistics order data of each data source; performing adaptive data processing based on the preprocessed logistics order data type of each data source to obtain processing data corresponding to business needs of each data type; matching the corresponding target system based on each processing data, and transmitting each processing data to each target system in the same data format based on the transmission protocol of each target system; receiving the corresponding processing data, and executing the order management plan of the corresponding target system based on the received processing data.

[0015] On the other hand, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, enables the computer to execute the multi-system order management method described above.

[0016] Through the above-mentioned technical solution, the solution of the present invention realizes efficient data collection, processing and transmission through multiple layers, with significant technical effects. First, the data collection layer can connect to multiple data sources and automatically collect logistics order data, thereby improving the efficiency of data integration. Secondly, the data exchange layer pre-processes the received logistics order data to ensure the consistency of the data in format and structure, thereby improving the accuracy and stability of data processing. The data processing layer further performs adaptive processing on the pre-processed data to generate processed data that meets different business needs, thereby enhancing the system's responsiveness to diverse order management needs. The data service layer ensures that the processed data can be transmitted in a standardized format according to the protocol requirements of different target systems, thereby achieving efficient data transmission between multiple systems. Finally, the target system layer performs corresponding order management operations based on the transmitted data, ensuring the automation and intelligence of the logistics order management process. This system significantly improves the efficiency, reliability and scalability of the logistics management system.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0019] Figure 1 This is a system structure diagram of a multi-system order management system provided by one embodiment of the present invention;

[0020] Figure 2 It is a flowchart of the steps of a multi-system order management method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0022] Figure 1 This is a system structure diagram of a multi-system order management system provided by an embodiment of the present invention. Figure 1As shown, an embodiment of the present invention provides a multi-system order management system, which includes: a data acquisition layer, which is used to connect to various data sources and collect logistics order data based on various data sources; a data exchange layer, which is connected to the data acquisition layer, which is used to receive logistics order data from various data sources and perform data preprocessing on the logistics order data from various data sources; a data processing layer, which is connected to the data exchange layer, which is used to perform adaptive data processing based on the preprocessed logistics order data type of each data source to obtain processed data corresponding to business needs of each data type; a data service layer, which is connected to the data processing layer, which is used to match the corresponding target system based on each processed data, and transmit each processed data to each target system in the same data format based on the transmission protocol of each target system; a target system layer, which is connected to the data service layer, which is used to receive the corresponding processed data and execute the order management solution of the corresponding target system based on the received processed data.

[0023] In an embodiment of the present invention, the solution of the present invention achieves efficient data collection, processing, and transmission through multiple layers, with significant technical effects. First, the data collection layer can connect to multiple data sources and automatically collect logistics order data, thereby improving the efficiency of data integration. Second, the data exchange layer pre-processes the received logistics order data to ensure the consistency of the data in format and structure, thereby improving the accuracy and stability of data processing. The data processing layer further performs adaptive processing on the pre-processed data to generate processed data that meets different business needs, thereby enhancing the system's responsiveness to diverse order management needs. The data service layer ensures that the processed data can be transmitted in a standardized format based on the protocol requirements of different target systems, thereby achieving efficient data transmission between multiple systems. Finally, the target system layer performs corresponding order management operations based on the transmitted data, ensuring the automation and intelligence of the logistics order management process. This system significantly improves the efficiency, reliability, and scalability of the logistics management system.

[0024] Preferably, the data collection layer is configured to: identify the data sources that need to be connected according to different business systems in the logistics industry, and configure the corresponding collection interface and collection method for each data source; based on the configured collection interface and collection method, execute data collection of each data source in parallel to obtain the data set corresponding to each data source.

[0025] In an embodiment of the present invention, the data collection layer is configured to: identify the data sources required for docking based on different business systems in the logistics industry, and configure a corresponding acquisition interface and acquisition method for each data source; based on the configured acquisition interface and acquisition method, execute data acquisition from each data source in parallel to obtain the corresponding data set for each data source. The logistics industry has a variety of business systems, including enterprise resource planning systems (ERP), enterprise management systems (OA), financial systems, transportation management systems (TMS), warehouse management systems (WMS), etc. These systems play a vital role in different business links. However, all of these systems use different communication protocols and data formats. For example, SAP systems typically use the SOAP protocol for data transmission, enterprise management systems (OA) may rely on the FTP protocol, and other systems may use protocols such as EDI, JDBC, and REST. Therefore, the data collection layer must have flexible docking capabilities to adapt to the requirements of different data sources, data formats, and transmission protocols.

[0026] To achieve efficient data collection, the data collection layer must first identify and categorize each data source. Based on the different business systems, the protocols and data formats used are identified, and the corresponding collection interfaces and methods are configured for each data source. For example, in an SAP system, order information is obtained through a SOAP interface, while in an OA system, data files may be regularly retrieved from an FTP server. Furthermore, for different data formats, such as JSON, XML, and CSV, the data collection layer must possess the corresponding data parsing capabilities to parse the received data and prepare it for subsequent processing.

[0027] During the data collection process, the data collection layer employs a parallel collection strategy, allowing data to be collected simultaneously from multiple sources. This parallel processing significantly improves data collection efficiency, particularly when processing large volumes of order information, significantly reducing wait times and system latency. By executing data collection tasks in parallel, the system ensures real-time updates of logistics order information, reducing decision-making delays caused by data lags.

[0028] Based on the solution of the present invention, by identifying different data sources and configuring corresponding interfaces and collection methods for them, the data collection layer can achieve efficient integration of multiple business systems, breaking the data silos between systems. The system can automatically adapt to different data formats and protocols without extensive manual intervention, thereby accelerating data integration. Different business systems use different data formats and transmission protocols. The data collection layer can flexibly configure collection interfaces to enable it to simultaneously process multiple data sources in different formats. This enables the system to maintain efficient data processing capabilities even in complex and changing business scenarios.

[0029] Furthermore, the parallel data collection method ensures that data from various business systems can be quickly aggregated into the system, reducing data latency. In the logistics industry, real-time data is particularly important, especially when processing key business scenarios such as order distribution, warehouse scheduling, and transportation route optimization. The system can make quick decisions based on the latest data. The data collection layer supports multiple protocols (such as SOAP, FTP, REST, EDI, etc.), making it easy to integrate with different types of business systems such as ERP, OA, and financial systems. Whether it is the collection of logistics order information or the transmission of financial reconciliation data, efficient data interaction can be achieved through standardized interfaces.

[0030] Preferably, the data exchange layer is configured to: based on the data source protocol corresponding to the logistics order data of each data source, uniformly convert the logistics order data of each data source into a preset standard format; perform data cleaning and data verification on the logistics order data of each data source in the preset standard format to obtain basic data that has passed integrity verification and consistency verification; perform data conversion and / or data mapping on the basic data to obtain converted data; and forward the converted data to the data processing layer based on preset standards.

[0031] Furthermore, performing data conversion and / or data mapping on the basic data to obtain converted data includes: parsing each basic data to identify the fields and data content that need to be converted and / or mapped; matching corresponding data conversion rules based on the fields and data content that need to be converted and / or mapped of each basic data; wherein the data conversion rules include any one or more of data extraction, format conversion and data dictionary relationship mapping; based on the matched data conversion rules, extracting and executing corresponding data processing functions and / or scripts, performing corresponding basic data conversion and / or data mapping, and obtaining converted data.

[0032] In this embodiment of the present invention, the core function of the data exchange layer is to uniformly convert, clean, and validate logistics order data from multiple different data sources, thereby ensuring data integrity and consistency. Specifically, the data exchange layer first receives logistics order data through various supported data source protocols (such as SOAP, EDI, REST, FTP, JDBC, etc.). These protocols and data sources are often different, so when receiving data, the system first needs to identify the protocol used by each data source and match the corresponding data interface and parsing method.

[0033] Furthermore, upon receiving data in various formats, the data exchange layer converts this data into a unified, pre-defined standard format, such as JSON or XML, using built-in protocol adapters. This process not only enables the system to handle heterogeneous data from various sources but also ensures data compatibility and standardization in subsequent processing steps. For the logistics industry, unified data formatting is crucial, as it avoids data processing errors and delays caused by format differences. After the data format conversion is complete, the system performs data cleansing and validation to ensure data integrity and consistency. The data cleansing process includes removing invalid data, correcting incorrect field values, and filling in missing data to ensure data quality. Simultaneously, data validation ensures data consistency through rule checks, ensuring that data content complies with business logic and regulatory requirements. This cleansed and validated base data ensures high-quality order information, providing a solid foundation for subsequent data processing.

[0034] Furthermore, the system performs data conversion and / or data mapping on the base data. Data conversion involves modifying the data format or structure to meet the requirements of the target system. For example, this involves standardizing date formats or data in different units. Data mapping involves matching fields in the base data with fields in the target system. For example, mapping the "order number" of a logistics order to the "order ID" in the target system. These conversion and mapping rules can be dynamically adjusted based on business logic and automated through built-in functions and scripts. This process supports the combined application of multiple conversion rules, such as data extraction, format conversion, and data dictionary mapping. After conversion is complete, the data exchange layer forwards the converted data to the data processing layer according to a pre-set standard. This standard may be a unified format such as JSON or XML, allowing the data processing layer to further execute business logic.

[0035] Based on the solution of the present invention, the solution of the present invention can bring the following advantages:

[0036] 1) Improve data processing efficiency: The data exchange layer greatly reduces manual intervention by supporting automatic adaptation and conversion of multiple protocols. It can process information from multiple data sources in parallel, thereby improving the system's data processing speed and efficiency.

[0037] 2) Ensure data uniformity and accuracy: Through pre-set standard formats and strict data cleaning and verification processes, the system ensures the consistency of data format and content across all data sources, avoiding processing errors caused by non-standard data.

[0038] 3) Support multi-system integration: Data conversion and mapping functions enable the system to adapt to the needs of different business systems. Whether it is SAP, OA, financial system, etc., their order data can be processed by the data exchange layer and standardized into a unified data structure to adapt to the needs of their respective target systems.

[0039] 4) Enhanced system scalability and flexibility: The data exchange layer not only supports existing protocols and data formats but also dynamically adjusts conversion rules and mapping relationships based on business changes, offering high scalability. Regardless of the number of new systems and data sources added, the system can flexibly adapt by expanding its protocol support and rule processing capabilities.

[0040] 5) Simplified system operations: Through automated data processing and pre-set rule execution, the system reduces reliance on manual operations and simplifies system maintenance and troubleshooting. Automated data cleaning, conversion, and validation not only improves system stability but also reduces operational costs.

[0041] Preferably, the data processing layer includes: a data cleaning component for cleaning the preprocessed data; a data conversion component for performing format conversion on the preprocessed data; a data processing component for performing adaptive data processing on the preprocessed data; a data mapping component for mapping the preprocessed data from the original structure to the target structure; a data filtering component for performing preset data filtering based on the functional requirements corresponding to the logistics order data type of each data source after preprocessing; and a data verification component for verifying the preprocessed data.

[0042] Furthermore, the adaptive data processing includes any one or more of data aggregation processing, data screening processing, and data calculation processing.

[0043] Furthermore, the data processing component is configured to: match the corresponding business needs based on the pre-processed logistics order data types of each data source; match the corresponding adaptive data processing rules in the pre-built data processing rule library based on the matched business needs; and execute the corresponding pre-processed logistics order data processing of each data source based on the matched adaptive data processing rules to obtain the processed data corresponding to the business needs of each data type.

[0044] In the embodiment of the present invention, each component is described in detail:

[0045] 1) Data cleaning component: The data cleaning component is responsible for cleaning the pre-processed data to ensure the accuracy and completeness of the data. Logistics order data may have duplication, errors, missing data and other problems during the collection process. Through the cleaning operation of this component, invalid data and redundant data can be effectively removed, and erroneous fields can be corrected. The cleaning process may include data deduplication, format correction (such as date, amount, etc.), missing value filling and abnormal data detection. For example, in logistics order processing, if an order lacks a shipping date or the product price is abnormal, the data cleaning component will process these abnormal data according to preset rules to ensure that subsequent business operations are based on accurate data.

[0046] 2) Data conversion component: The data conversion component is used to convert the pre-processed data format into a standard format acceptable to the target system. In the logistics industry, different business systems may use different data formats, such as JSON, XML, etc. The data conversion component ensures that order data can be adapted to different systems through format conversion. For example, a certain order data may initially be in XML format, while the target system requires JSON format. The data conversion component will automatically complete this type of conversion. In addition, the component also supports conversion operations for specific data formats, such as converting the date format from "YYYY / MM / DD" to a timestamp format, or converting numerical data in text form into computable digital data. This not only simplifies the interaction between data, but also improves the processability of the data.

[0047] 3) Data Processing Component: The Data Processing Component is a core component of the data processing layer, responsible for performing in-depth business processing on pre-processed data. Logistics order data often requires complex operations such as data aggregation, filtering, and calculations to meet different business needs. For example, the Data Processing Component can aggregate large quantities of order data to generate the total order volume for a specific period, or calculate shipping costs, taxes, and other fees for specific orders. During this process, the component automatically matches the corresponding processing rules based on business needs by calling on the built-in expression engine and rule library, ensuring flexible and scalable data processing.

[0048] 4) Data Mapping Component: The Data Mapping Component is used to map the source data structure to the target data structure. Logistics order data originates from different business systems, and the data structures of each system may differ. The Data Mapping Component aligns fields from different data sources with fields in the target system, thus unifying the data structure and facilitating subsequent processing. For example, the "Shipping Time" field in order data may be named "dispatch_time" in the source system, while the target system requires "shipping_date." The Data Mapping Component automatically completes the mapping conversion of such fields, ensuring seamless integration between systems.

[0049] 5) Data Filtering Component: The data filtering component filters data based on pre-set conditions for different business needs. The logistics industry handles a vast amount of order data, but different business scenarios may only require processing specific types of order data. The data filtering component selects eligible data by matching business rules. For example, a business scenario may only require processing orders generated that day or orders exceeding a certain weight. The data filtering component automatically filters out irrelevant data, improving processing efficiency and ensuring that the business system receives only the required data.

[0050] 6) Data Validation Component: The data validation component is the last line of defense in the data processing layer, verifying processed data and ensuring its consistency and integrity throughout the entire processing flow. Validation rules can include formatting and business logic validation. For example, the system might verify that the amount field in a logistics order is positive, that the date field conforms to the expected format, and that the order status complies with business logic. This final check by the data validation component ensures that data is error-free when it is passed to subsequent business systems, preventing business interruptions or processing failures caused by data issues.

[0051] Based on the solution of the present invention, the data processing layer adopts a componentized structural design, and each functional component can be flexibly configured according to different business needs. Through the componentized processing method, the system can perform customized processing for different data sources, data types and business rules to ensure that the data processing needs under different business scenarios can be met. Through a series of processing operations such as data cleaning, conversion, processing, mapping, filtering and verification, the system can efficiently and accurately process large amounts of logistics order data. Especially when processing multi-system and multi-source data, through automated data processing processes, the system greatly reduces manual operations and improves the overall data processing efficiency. The data processing layer ensures that every link of data from collection, preprocessing to business processing is strictly verified and corrected, and the data finally generated has a high degree of accuracy and consistency. Whether it is data format conversion, field mapping or business logic verification, the processed data meets the system preset standards, providing reliable data support for downstream business systems.

[0052] Preferably, the data service layer is configured to: match the corresponding target system based on each processed data and identify the data transmission protocol of each target system; establish a connection relationship with the database of each target system based on the corresponding data transmission protocol; and transmit the corresponding processed data in a unified format to the target system layer based on the established connection relationship, so that each target system can execute the corresponding order management plan based on the received processed data.

[0053] In an embodiment of the present invention, the data service layer first needs to identify the data transmission protocol of each target system and establish a connection with each system based on this. For example, OMS may interact with data through REST API, while TMS may use message queues (MQ) to process order data, BMS may need to directly access the database through JDBC, and WMS may upload files through FTP. After identifying the transmission protocol of the target system, the data service layer establishes a connection relationship with these systems through a protocol adapter to ensure that the data can be correctly transmitted to the target system. This automation of protocol adaptation makes the system highly flexible and can support the docking needs of multiple business systems.

[0054] Furthermore, although the data processing layer has performed preliminary standardization on the order data, the data service layer may still need to further adapt and convert the data format according to the specific requirements of the target system. For example, some systems may only accept data in JSON format, while other systems may require data to be uploaded in the form of XML or files. In this case, the data service layer will convert the data format according to the needs of the target system to ensure that the transmitted data conforms to the acceptable format of the target system. In addition, in some business scenarios, order data may need to be split or reorganized to facilitate processing by the target system. For example, the WMS system may need to classify order information according to the geographical location of the warehouse. The data service layer will be able to provide processed data to the corresponding system based on these business rules.

[0055] Furthermore, the data service layer supports multiple protocols, such as REST, MQ, JDBC, FTP, etc., to ensure that it can interact with different types of systems.

[0056] RESTful API: The system uses a REST API built with the Spring Boot framework to deliver processed order data to the OMS or other systems in JSON or XML format. This approach is highly scalable, easily adapting to changing business needs, and supports both synchronous and asynchronous data exchange.

[0057] Message Queues (MQ): For systems requiring asynchronous data transmission, the data service layer integrates with MQ services through the Spring AMQP framework, publishing processed order data to a message queue. This is particularly useful for systems that require high throughput but less real-time performance, such as TMS. This allows the target system to pull data based on its own processing capabilities, avoiding data congestion during the data transmission process.

[0058] JDBC: For systems that need to interact directly with a database, such as a BMS, the data service layer establishes a connection with the database via the JDBC protocol, directly writing order data to the target database. This method enables efficient and accurate data updates and is particularly suitable for systems with high data real-time requirements.

[0059] FTP: In scenarios requiring file transfer, such as WMS, the data service layer uses the FTP protocol to package order data into files and upload them to an FTP server. The target system can then download and process these files based on a pre-set schedule or event trigger. This approach is suitable for business scenarios requiring high-volume or batch processing.

[0060] Furthermore, a key function of the data service layer is to ensure real-time synchronization and updating of order data. By leveraging protocols such as the REST API, message queues, and JDBC, the system enables instant delivery and updating of order information, ensuring that target systems have timely access to the latest order status. Whether creating, modifying, splitting, or canceling an order, data from these operations is delivered to the target system in real time through the data service layer, ensuring that each system processes the latest order data. This synchronization mechanism is crucial for improving system responsiveness and automating business processes.

[0061] Based on the solution of the present invention, by supporting multiple data transmission protocols such as REST, MQ, JDBC, FTP, etc., the data service layer can adapt to the needs of multiple business systems, breaking the bottleneck of difficult data transmission between different systems in traditional logistics systems. Regardless of the protocol used by the target system, the data service layer can seamlessly connect with it through the protocol adapter, thereby improving the flexibility and scalability of the system. The data service layer ensures that the processed order data can be transmitted in a format accepted by the target system through the format conversion function. At the same time, it supports multiple transmission methods (such as REST, message queues, JDBC, FTP) to ensure that data can reach the target system in the most efficient way. The standardized transmission of data improves the efficiency of data processing and reduces the error rate of data during transmission.

[0062] Figure 2 This is a flowchart of a multi-system order management method provided by one embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides a multi-system order management method, the method comprising:

[0063] S10: Connect to various data sources and collect logistics order data based on each data source.

[0064] S20: Perform data preprocessing on the logistics order data of each data source.

[0065] S30: Adaptively process the data based on the pre-processed logistics order data types of each data source to obtain processed data corresponding to business requirements of each data type.

[0066] S40: Matching corresponding target systems based on each processed data, and transmitting each processed data to each target system in the same data format based on the transmission protocol of each target system.

[0067] S50: Receive corresponding processing data, and execute the order management solution of the corresponding target system based on the received processing data.

[0068] An embodiment of the present invention further provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, enables the computer to execute the multi-system order management method described above.

[0069] Those skilled in the art will appreciate that all or part of the steps in the methods of the aforementioned embodiments can be accomplished by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a single-chip microcomputer, chip, or processor 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 a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0070] The above describes in detail the optional embodiments of the present invention in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the technical concept of the embodiments of the present invention, a variety of simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the scope of protection of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner unless there is any contradiction. In order to avoid unnecessary repetition, the embodiments of the present invention will no longer describe the various possible combinations separately.

[0071] In addition, the various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A multi-system order management system, applied to the multi-system order integrated management in the logistics industry, characterized by: The system comprises: The data collection layer is used to connect to various data sources and collect logistics order data based on these data sources; A data exchange layer, connected to the data acquisition layer, for receiving logistics order data from various data sources and performing data preprocessing on the logistics order data from various data sources; The data processing layer is connected to the data exchange layer and is used to perform adaptive data processing based on the pre-processed logistics order data types of each data source to obtain processed data corresponding to business requirements of each data type; A data service layer, connected to the data processing layer, is used to match the corresponding target system based on each processed data, and transmit the processed data to each target system in the same data format based on the transmission protocol of each target system; The target system layer is connected to the data service layer, and is used to receive corresponding processing data and execute the order management solution of the corresponding target system based on the received processing data.

2. The system according to claim 1, wherein: The data acquisition layer is configured to: Identify the data sources that need to be connected based on the different business systems in the logistics industry, and configure the corresponding collection interface and collection method for each data source; Based on the configured collection interface and collection method, data collection from each data source is executed in parallel to obtain the data sets corresponding to each data source.

3. The system according to claim 1, wherein: The data exchange layer is configured to: Based on the data source protocol corresponding to the logistics order data of each data source, the logistics order data of each data source is uniformly converted into a preset standard format; Perform data cleansing and data verification on logistics order data from various data sources in a preset standard format to obtain basic data that has passed integrity and consistency verification; Performing data conversion and / or data mapping on the basic data to obtain converted data; The converted data is forwarded to the data processing layer based on preset standards.

4. The system according to claim 3, characterized in that The performing data conversion and / or data mapping on the basic data to obtain converted data includes: Analyze each basic data to identify the fields and data content that need to be converted and / or mapped; Based on the fields and data contents that need to be converted and / or mapped for each basic data, the corresponding data conversion rules are matched; wherein, The data conversion rules include any one or more of data extraction, format conversion and data dictionary relationship mapping; Based on the matched data conversion rules, the corresponding data processing functions and / or scripts are extracted and executed, the corresponding basic data conversion and / or data mapping are performed, and the converted data is obtained.

5. The system according to claim 1, wherein: The data processing layer includes: Data cleaning component, used to clean the pre-processed data; Data conversion component, used to perform format conversion on preprocessed data; A data processing component is used to perform adaptive data processing on the pre-processed data; Data mapping component, used to map the pre-processed data from the original structure to the target structure; The data filtering component is used to perform preset data filtering based on the functional requirements corresponding to the logistics order data type of each data source after pre-processing; Data verification component, used to verify the preprocessed data.

6. The system according to claim 5, characterized in that The adaptive data processing includes: Any one or more of data aggregation processing, data screening processing, and data calculation processing.

7. The system according to claim 6, characterized in that The data processing component is configured to: Match the corresponding business needs based on the pre-processed logistics order data types of each data source; Based on the matching business needs, the corresponding adaptive data processing rules are matched in the pre-built data processing rule library; Based on the matching adaptive data processing rules, the logistics order data processing of each data source after corresponding preprocessing is executed to obtain the processing data corresponding to the business needs of each data type.

8. The system according to claim 1, wherein: The data service layer is configured as follows: Match the corresponding target system based on each processed data and identify the data transmission protocol of each target system; Establish connections with the databases of each target system based on the corresponding data transmission protocols; Based on the established connection relationship, the corresponding processing data in a unified format is transmitted to the target system layer, so that each target system executes the corresponding order management solution based on the received processing data.

9. A multi-system order management method, applied to the multi-system order integrated management in the logistics industry, characterized by: The method is executed based on the multi-system order management system according to any one of claims 1 to 8, and the method includes: Connect to various data sources and collect logistics order data based on them; Perform data preprocessing on logistics order data from various data sources; Adaptive data processing is performed based on the pre-processed logistics order data types of each data source to obtain processed data corresponding to business needs of each data type; Matching the corresponding target system based on each processed data, and transmitting the processed data to each target system in the same data format based on the transmission protocol of each target system; Receive corresponding processing data, and execute an order management solution corresponding to the target system based on the received processing data.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the multi-system order management method according to claim 9.

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