A multi-platform-based order synchronization processing method, device and medium

Abstract

This invention discloses a method, device, and medium for multi-platform order synchronization processing, belonging to the field of enterprise information management technology. It addresses the technical problems of low order processing efficiency, high error rates, and asynchronous information between orders, as well as high labor costs in existing multi-platform e-commerce management. The method includes: acquiring raw heterogeneous order data from different e-commerce platforms through an API interface adapter; standardizing and converting the raw heterogeneous order data to obtain standard order data; integrating the standard order data with various business tasks under a relevant business rule engine to obtain customized order data; synchronizing the customized order data with relevant enterprise systems and obtaining order process feedback information based on order update information; and visually configuring both the customized order data and the order process feedback information through a visual management backend to obtain order process monitoring information.

Description

Technical Field

[0001] This application relates to the field of enterprise information management technology, and in particular to a method, device and medium for order synchronization processing based on multiple platforms. Background Technology

[0002] With the booming development of e-commerce, businesses commonly open stores on multiple third-party platforms to expand their sales channels. However, this multi-platform business model also brings severe management challenges: the order data formats and interface protocols of each platform are vastly different, resulting in information silos. Currently, businesses typically use manual or semi-manual methods to switch back and forth between the back-end systems of each platform, verify, and enter data. This method suffers from extremely low efficiency, high error rates (such as overselling, incorrect shipments, and missing shipments), information asynchrony, and high labor costs. Existing single tools or basic interfaces cannot achieve efficient centralized and intelligent collaborative processing, and cannot meet the urgent needs of modern e-commerce enterprises for real-time, accurate, and automated order processing.

[0003] Therefore, there is an urgent need for a solution that can seamlessly integrate multiple platforms and systems to achieve automated synchronous processing of the entire order process. Summary of the Invention

[0004] This application provides a method, device, and medium for order synchronization processing based on multiple platforms to solve the following technical problems: In existing multi-platform e-commerce management, order processing efficiency is low, error rate is high, and information between orders is not synchronized, resulting in high labor costs.

[0005] The embodiments of this application adopt the following technical solutions: On one hand, this application provides a multi-platform order synchronization processing method, including: obtaining raw heterogeneous order data from different e-commerce platforms through an API interface adapter; standardizing and converting the raw heterogeneous order data to obtain standard order data; integrating the standard order data with various business tasks under a relevant business rule engine to obtain customized order data; synchronizing the customized order data with relevant enterprise systems and obtaining order process feedback information based on order update information; and visually configuring both the customized order data and the order process feedback information through a visual management backend to obtain order process monitoring information.

[0006] This application's embodiments achieve end-to-end automation from order retrieval to warehouse shipment, completely changing the traditional manual data entry model. Processing speed is exponentially improved, easily handling massive orders during peak sales periods. It effectively avoids problems prone to errors such as missed orders, incorrect orders, and overselling that occur with manual operations, significantly improving business accuracy. Furthermore, the built-in rule engine allows enterprises to customize automated processes according to their own business needs, enabling intelligent decision-making and refined operations in order processing. Simultaneously, it breaks down information silos between various e-commerce platforms and internal systems, achieving real-time data unification and two-way synchronization, providing enterprises with an accurate and comprehensive data view, and supporting more scientific business decisions.

[0007] In one feasible implementation, the original heterogeneous order data from different e-commerce platforms is obtained through an API interface adapter. Specifically, this includes: listening to push messages from different e-commerce platforms through a long-polling message queue; performing distributed task-based crawling processing on the order data in the push messages using a timed crawling scheduler to obtain initial order data; incrementally crawling the initial order data according to the incremental identifiers of the order data from different e-commerce platforms, and performing full data synchronization to obtain the original order data for each e-commerce platform; and combining the original order data from each e-commerce platform with multi-source data to obtain the original heterogeneous order data.

[0008] In one feasible implementation, the original heterogeneous order data is standardized to obtain standard order data. Specifically, this includes: generating predefined data cleaning rules based on the central processing unit; performing abnormal data cleaning on the original heterogeneous order data using the predefined data cleaning rules, and verifying the integrity and validity of the cleaned order data to obtain preprocessed order data; generating data format conversion rules based on the central processing unit; and performing data standardization conversion on the preprocessed order data under a unified data format using the data format conversion rules to obtain standard order data.

[0009] In one feasible implementation, the standard order data is integrated with various business tasks under a relevant business rule engine to obtain customized order data. Specifically, this includes: based on the business requirement information to be configured, encapsulating the corresponding batch processing framework, rule network and inference engine, and distributed execution mechanism into a rule engine to obtain the business rule engine; using the anomaly detection and self-learning optimization module in the business rule engine, identifying and processing abnormal order data in the standard order data to obtain abnormal order identification information; and using the multi-factor weighted scoring model in the business rule engine to automatically review and process business actions and processes in the standard order data, and verifying them based on a financial interface. The system obtains automatic review results from the standard order data. Using the multi-factor weighted scoring model, it merges new orders and splits old orders from the order attribute items in the standard order data to obtain order splitting and merging information. The order attribute items include at least: recipient, warehouse, and product attributes. Using the multi-factor weighted scoring model and based on priority tags, it dynamically calculates the priority processing order of the standard order data to obtain order processing priority information. Based on the abnormal order identification information, the automatic review results, the order splitting and merging information, and the order processing priority information, it integrates various business tasks to obtain the customized order data.

[0010] In one feasible implementation, before synchronizing the customized order data with relevant enterprise systems and obtaining order process feedback information based on order update information, the method further includes: configuring the enterprise system with a multi-protocol adaptation and conversion engine based on the customized order data to construct an external interface gateway conforming to the specific format of each downstream system; integrating and managing the interface metadata of all downstream systems and associating the interface metadata configuration data with the external interface gateway to obtain an integrated external interface gateway; wherein, the interface metadata configuration data includes: address, authentication method, data format, and call frequency limit; and performing unified permission management on the integrated external interface gateway through the permission management strategy corresponding to the authentication credential data between downstream systems to obtain a unified integrated external interface gateway.

[0011] In one feasible implementation, the customized order data is synchronized with relevant enterprise systems, and order process feedback information is obtained based on order update information. Specifically, this includes: constructing an order fulfillment process template based on the unified integration external interface gateway; wherein the order fulfillment process template includes at least: order confirmation, inventory deduction, order printing, shipment, and accounting; using the order fulfillment process template, callbacks and monitoring are performed on each execution action in the customized order data to obtain order execution result information; based on the order execution result information, the customized order data undergoes bidirectional status monitoring and subscription processing between the relevant push platform and downstream enterprise systems, and based on order update information, key update events of the customized order data are bidirectionally pushed to obtain order data synchronization feedback information between the push platform and downstream enterprise systems; each order execution process action in the order data synchronization feedback information is recorded and synchronized to obtain the order process feedback information.

[0012] In one feasible implementation, the customized order data and the order process feedback information are both visualized and configured through a visual management backend to obtain order process monitoring information. Specifically, this includes: configuring the customized order data and the order process feedback information into visual charts to obtain full-process order visualization information and corresponding node links; displaying the full-process order visualization information and corresponding node links through the visual management backend, and monitoring the node data of each node link to obtain the order process monitoring information.

[0013] In one feasible implementation, the order process monitoring information is updated and corrected using order process correction information from manual nodes, and then synchronously updated to the corresponding order process nodes in the push platform and downstream enterprise systems.

[0014] Secondly, embodiments of this application also provide a multi-platform order synchronization processing device, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to execute a multi-platform order synchronization processing method as described in any of the above embodiments.

[0015] Thirdly, embodiments of this application also provide a non-volatile computer storage medium, which is a non-volatile computer-readable storage medium storing at least one program. Each program includes instructions, which, when executed by a terminal, cause the terminal to perform a multi-platform-based order synchronization processing method as described in any of the above embodiments.

[0016] This application provides a method, device, and medium for order synchronization processing based on multiple platforms. Compared with the prior art, the embodiments of this application have the following beneficial technical effects: 1. Significantly improve operational efficiency: It has achieved full automation from order capture to warehouse delivery, completely changing the traditional manual data entry mode, and the processing speed has been increased exponentially, making it easy to handle the massive number of orders during promotional periods.

[0017] 2. Significantly reduce error rate and risk: Through automated data cleaning, verification and rule processing, problems such as missed orders, incorrect orders and overselling (caused by inventory asynchrony) that are prone to occur in manual operations are effectively avoided, greatly improving the accuracy of business operations.

[0018] 3. Achieve refined and intelligent management: The built-in rule engine enables enterprises to customize automated processes according to their own business needs (such as by region, SKU, customer level), realizing intelligent decision-making and refined operation in order processing.

[0019] 4. Effectively reduce labor and management costs: Automated systems replace a large amount of repetitive manual labor, enabling companies to efficiently manage orders from multiple platforms without investing too much manpower, directly saving labor costs and management expenses.

[0020] 5. Enhance data collaboration and decision support: Break down information silos between various e-commerce platforms and internal systems, achieve real-time data unification and two-way synchronization, provide enterprises with accurate and comprehensive data views, and help make more scientific business decisions. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating a multi-platform order synchronization processing method provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of a multi-platform order synchronization processing device provided in an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0023] This application provides a method for order synchronization processing based on multiple platforms, such as... Figure 1 As shown, the order synchronization processing method based on multiple platforms specifically includes steps S101-S105: S101. Obtain raw heterogeneous order data from different e-commerce platforms through the API interface adapter.

[0024] Specifically, it is necessary to first listen for push messages from different e-commerce platforms through a long polling message queue.

[0025] Furthermore, the order data in the push message is further processed by a timed capture scheduler under a distributed task to obtain the initial order data.

[0026] Furthermore, based on the incremental identifiers of order data from different e-commerce platforms, the initial order data needs to be incrementally captured, and full data synchronization needs to be performed to obtain the original order data for each e-commerce platform.

[0027] Furthermore, the original order data from each e-commerce platform is combined from multiple sources to obtain original heterogeneous order data.

[0028] In one embodiment, to monitor push notifications from different e-commerce platforms, the corresponding adapter can be triggered in real time to fetch data using platform push notification monitoring (such as Webhooks) or long polling and message queue monitoring methods, ensuring the timeliness of high-priority data (such as new orders and order status changes). Then, based on configurable Cron expressions or a visual scheduling interface, the fetching frequency and strategy for different data categories (such as orders, products, and logistics) can be set. Distributed task scheduling is supported to avoid single points of failure and task backlog. Furthermore, by intelligently identifying and recording incremental identifiers (such as order update timestamps and incremental IDs) of data from each platform, incremental fetching is used by default to improve efficiency; full data synchronization is automatically performed periodically or when data inconsistency is detected to ensure data integrity; finally, the original order data from each e-commerce platform is combined from multiple sources to obtain the original heterogeneous order data.

[0029] S102. Standardize and transform the original heterogeneous order data to obtain standard order data.

[0030] Specifically, predefined data cleaning rules need to be generated based on the central processing unit first.

[0031] Furthermore, by using predefined data cleaning rules, the original heterogeneous order data is cleaned to remove abnormal data, and the cleaned order data is then verified for data integrity and validity to obtain preprocessed order data.

[0032] Furthermore, based on the central processing unit, data format conversion rules are generated. Then, using these rules, the preprocessed order data is standardized and converted to a unified data format to obtain standard order data.

[0033] In one embodiment, a protocol parser (such as HTTP / HTTPS, WebSocket) and a data format mapping template (such as JSON-to-XML, JSON-to-standard JSON with a specific structure) can be built into the central processing unit. This submodule converts the raw, heterogeneous data (with different structures, naming conventions, and units) returned by various platforms into an internally unified standard data intermediate format in real time. Then, the cleaned preprocessed order data undergoes data standardization transformation under the unified data format to finally obtain standard order data.

[0034] S103. Perform integrated processing of standard order data under various business tasks of the relevant business rules engine to obtain customized order data.

[0035] Specifically, based on the business requirements information to be configured, the corresponding stream and batch processing framework, rule network and inference engine, and distributed execution mechanism are encapsulated into the rule engine to obtain the business rule engine.

[0036] As a feasible implementation approach, the construction of a business rule engine requires first building a unified stream and batch processing framework. This involves integrating a stream processing engine (such as Flink or Storm) to handle rules requiring real-time responses (e.g., real-time fraud interception); and integrating a batch processing engine (such as Spark) to handle rules that are timed or involve large amounts of data (e.g., nighttime batch order merging). A unified scheduler allocates execution modes based on rule attributes and data characteristics. Then, relevant rules are organized into a rule network (a variant of the Rete network), and pattern matching algorithms are used to optimize rule evaluation efficiency and avoid unnecessary computation. Furthermore, it supports forward and reverse chain reasoning to handle complex multi-rule dependencies and triggering scenarios.

[0037] Furthermore, through the anomaly detection and self-learning optimization module in the business rules engine, abnormal order data in the standard order data is identified and processed to obtain abnormal order identification information.

[0038] In one embodiment, the anomaly detection and self-learning optimization module can enable the rule center to perform rule execution effect monitoring and feedback loop: real-time monitoring of the trigger frequency, matching rate, and business results after triggering each rule (such as automatic approval rate and subsequent order cancellation rate). A feedback data pipeline is also established to feed back the processing results (including correction results from manual review) to the analysis module; finally, after the identification is completed, abnormal order identification information is obtained.

[0039] Furthermore, the business rules engine uses a multi-factor weighted scoring model to automatically review and process business actions and processes in standard order data, and obtains automatic review results based on financial interface verification information.

[0040] In one embodiment, for complex decisions (such as identifying abnormal orders), a multi-factor weighted scoring model or a decision tree model is employed. Based on the rule evaluation results and scores, the system automatically routes orders to different processing pipelines (such as "automatic release," "transfer to manual review," and "high-risk freeze"). It also utilizes automated business action executors to collaborate with downstream systems, automatically executing rule-triggered business actions. That is, it can call the financial interface to verify payment, and after calling the risk control interface to confirm security, automatically set the order status to "pending shipment."

[0041] Furthermore, a multi-factor weighted scoring model is needed to process the order attribute items in the standard order data by merging new orders and splitting old orders to obtain order splitting and merging information. The order attribute items must include at least: recipient, warehouse, and product attributes.

[0042] In one embodiment, new merge orders or split instructions can be generated based on rules such as consignee, warehouse, and product attributes, and logistics costs can be recalculated.

[0043] Furthermore, by using a multi-factor weighted scoring model and based on priority labels, the priority processing order of standard order data is dynamically calculated to obtain order processing priority information.

[0044] In one embodiment, order processing priority queues can also be dynamically calculated and assigned based on rules such as VIP customers, expedited labels, and promised delivery times.

[0045] Furthermore, based on abnormal order identification information, automatic review result information, order splitting and merging information, and order processing priority information, standard order data is integrated and processed by various business tasks to obtain customized order data.

[0046] As a feasible implementation method, the processed orders (with metadata such as processing tags, priorities, and routing instructions) and the specific task instructions to be executed (such as calling the logistics interface to create a waybill) are accurately distributed to downstream warehousing systems (WMS), logistics systems, financial systems, or manual review workbenches through message queues or service calls, so as to achieve integrated processing of various business tasks on standard order data.

[0047] S104. Synchronize the customized order data with the relevant enterprise systems and obtain order process feedback information based on the order update information.

[0048] Specifically, based on customized order data, the enterprise system needs to be configured with a multi-protocol adaptation and conversion engine to build an external interface gateway that conforms to the specific format of each downstream system.

[0049] Furthermore, the interface metadata of all downstream systems is integrated and managed, and the interface metadata configuration data is associated with the external interface gateway to obtain the integrated external interface gateway. The interface metadata configuration data includes: address, authentication method, data format, and call frequency limit.

[0050] Furthermore, by using the permission management strategy corresponding to the authentication credential data between downstream systems, unified permission management is implemented for the integrated external interface gateway, resulting in a unified integrated external interface gateway.

[0051] In one embodiment, a unified external interface gateway serves as the sole interface point for all internal systems. Utilizing a multi-protocol adaptation and conversion engine, it supports various integration protocols such as RESTful API, SOAP WebService, direct database connection, message queues (Kafka / RabbitMQ), and file exchange (SFTP). It provides protocol converters and data format mapping configurations to convert the platform's internal standard data model into specific formats (such as SAP IDoc format, Kingdee K / 3 credential format, and custom XML) that meet the requirements of each downstream system. An interface metadata management and registration center centrally manages the interface metadata of all downstream systems, including addresses, authentication methods (API Key / OAuth / account password), data format schemas, and call frequency limits; it also supports rapid registration and configuration of new systems. Finally, it uniformly manages authentication credentials between downstream systems, implements role-based interface access control, and logs all external calls for auditing; and it performs unified permission management on the integrated external interface gateway, ultimately resulting in a unified integrated external interface gateway.

[0052] S105. Through the visual management backend, customized order data and order process feedback information can be configured visually to obtain order process monitoring information.

[0053] Specifically, an order fulfillment process template must be built based on a unified integrated external interface gateway. This template should include at least the following steps: order confirmation, inventory deduction, order processing, shipment, and accounting.

[0054] Furthermore, by using the order fulfillment process template, callbacks and monitoring are performed on each execution action in the customized order data to obtain order execution result information.

[0055] In one embodiment, a pre-defined or configurable order fulfillment process template (e.g., "Confirmation - Deduct Inventory - Print Order - Ship - Accounting") is first set up. When an order enters a specific state (e.g., "Approved"), the engine automatically calls the interfaces of different systems sequentially according to the process, driving the business flow forward. Then, after executing an action (e.g., calling the WMS interface to create an outbound order), the system can synchronously wait for or asynchronously listen for callback notifications from the system to obtain the execution result (e.g., outbound order number) and trigger the next process (e.g., calling the TMS interface to schedule logistics). This completes the callback and listening for each execution action in the customized order data, thereby obtaining order execution result information.

[0056] Furthermore, based on the order execution result information, the customized order data undergoes bidirectional status monitoring and subscription processing between the relevant push platform and downstream enterprise systems. Based on the order update information, key update events of the customized order data are bidirectionally pushed to obtain synchronized feedback information on order data between the push platform and downstream enterprise systems.

[0057] Furthermore, it is necessary to synchronize and record each order execution process action in the order data feedback information to obtain order process feedback information. Then, both the customized order data and the order process feedback information are configured with visualization charts to obtain full-process order visualization information and corresponding node links.

[0058] Furthermore, through the visual management backend, the entire order process information and corresponding node links are visualized and displayed, and node data is monitored for each node link to ultimately obtain order process monitoring information.

[0059] As a feasible implementation method, order process monitoring information can also be updated and corrected through order process correction information from manual nodes, and synchronously updated to the corresponding order process nodes in the push platform and downstream enterprise systems. In other words, approval points are preset in the automated process. When the rule engine marks it as "requiring manual confirmation" or the downstream system returns a specific error, the process automatically pauses, generates a pending task, and pushes it to the manual workbench, resuming only after manual processing.

[0060] In addition, embodiments of this application also provide a multi-platform-based order synchronization processing device, such as... Figure 2 As shown, the multi-platform-based order synchronization processing device 200 specifically includes: At least one processor 201; and a memory 202 communicatively connected to the at least one processor 201; wherein the memory 202 stores instructions executable by the at least one processor 201 to enable the at least one processor 201 to execute: Obtain raw, heterogeneous order data from different e-commerce platforms through API interface adapters; The original heterogeneous order data is standardized and transformed to obtain standard order data; Standard order data is processed by integrating various business tasks under the relevant business rules engine to obtain customized order data; The customized order data is synchronized with the relevant enterprise systems, and the order process feedback information is obtained based on the order update information. Through the visual management backend, customized order data and order process feedback information can be configured visually to obtain order process monitoring information.

[0061] This application automates the entire process from order retrieval to warehouse shipment, completely transforming the traditional manual data entry model. Processing speed is exponentially improved, easily handling massive order volumes during peak sales periods. It effectively avoids issues prone to errors such as missed orders, incorrect orders, and overselling that often occur with manual operations, significantly improving business accuracy. Furthermore, the built-in rules engine allows businesses to customize automated processes according to their specific business needs, enabling intelligent decision-making and refined operations in order processing. Simultaneously, it breaks down information silos between various e-commerce platforms and internal systems, achieving real-time data unification and two-way synchronization, providing businesses with accurate and comprehensive data views to support more informed business decisions.

[0062] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0063] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0064] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0069] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0070] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0071] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0072] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of this specification.

Claims

1. A method for order synchronization processing based on multiple platforms, characterized in that, The method includes: Obtain raw, heterogeneous order data from different e-commerce platforms through API interface adapters; The original heterogeneous order data is standardized and transformed to obtain standard order data; The standard order data is integrated and processed by various business tasks under the relevant business rule engine to obtain customized order data; The customized order data is synchronized with the relevant enterprise systems, and order process feedback information is obtained based on the order update information. The customized order data and order process feedback information can be visualized and configured through the visual management backend to obtain order process monitoring information.

2. The order synchronization processing method based on multiple platforms according to claim 1, characterized in that, Through API interface adapters, raw heterogeneous order data from different e-commerce platforms is obtained, specifically including: Listen for push messages from different e-commerce platforms through a long-polling message queue; The order data in the push message is processed in a distributed task by a timed capture scheduler to obtain the initial order data; Based on the incremental identifiers of order data from different e-commerce platforms, the initial order data is incrementally captured, and full data synchronization is performed to obtain the original order data for each e-commerce platform. The original heterogeneous order data is obtained by combining the original order data from each e-commerce platform from multiple sources.

3. The order synchronization processing method based on multiple platforms according to claim 1, characterized in that, The original heterogeneous order data is standardized and transformed to obtain standard order data, specifically including: Based on the central processing unit, predefined data cleaning rules are generated; The original heterogeneous order data is cleaned by the predefined data cleaning rules, and the cleaned order data is then verified for data integrity and validity to obtain preprocessed order data. Data format conversion rules are generated based on the central processing unit; The preprocessed order data is standardized and converted to a unified data format using the data format conversion rules to obtain standard order data.

4. The order synchronization processing method based on multiple platforms according to claim 1, characterized in that, The standard order data is integrated and processed by various business tasks under the relevant business rules engine to obtain customized order data, specifically including: Based on the business requirements information to be configured, the corresponding stream and batch processing framework, rule network and inference engine, and distributed execution mechanism are encapsulated into the rule engine to obtain the business rule engine; The abnormal order data in the standard order data is identified and processed by the anomaly detection and self-learning optimization module in the business rule engine to obtain abnormal order identification information. The business rules engine uses a multi-factor weighted scoring model to automatically review and process business actions and processes in standard order data, and obtains automatic review results based on financial interface verification information. The multi-factor weighted scoring model is used to merge new orders and split old orders in the standard order data to obtain order splitting and merging information; wherein the order attribute items include at least: consignee, warehouse and product attributes; The order processing priority information is obtained by dynamically calculating the priority processing order of standard order data using the multi-factor weighted scoring model and based on priority labels. Based on the abnormal order identification information, the automatic review result information, the order splitting and merging information, and the order processing priority information, the standard order data is integrated and processed by multiple business tasks to obtain the customized order data.

5. The order synchronization processing method based on multiple platforms according to claim 1, characterized in that, Before synchronizing the customized order data with relevant enterprise systems and obtaining order process feedback information based on order update information, the method further includes: Based on the customized order data, the enterprise system is configured with a multi-protocol adaptation and conversion engine to build an external interface gateway that conforms to the specific format of each downstream system. All downstream system interface metadata is integrated and managed, and the interface metadata configuration data is associated with the external interface gateway to obtain the integrated external interface gateway; wherein, the interface metadata configuration data includes: address, authentication method, data format, and call frequency limit; By using the permission management strategy corresponding to the authentication credential data between downstream systems, unified permission management is performed on the integrated external interface gateway to obtain a unified integrated external interface gateway.

6. The order synchronization processing method based on multiple platforms according to claim 5, characterized in that, The customized order data is synchronized with relevant enterprise systems, and order process feedback information is obtained based on order update information, specifically including: Based on the unified integrated external interface gateway, an order fulfillment process template is constructed; wherein, the order fulfillment process template includes at least: order confirmation, deducting inventory, printing the order, shipping, and accounting; Using the order fulfillment process template, callbacks and listeners are performed on each execution action in the customized order data to obtain order execution result information; Based on the order execution result information, the customized order data is subjected to bidirectional status monitoring and subscription processing between the relevant push platform and the downstream enterprise system. Based on the order update information, the key update events of the customized order data are bidirectionally pushed to obtain the order data synchronization feedback information between the push platform and the downstream enterprise system. Each order execution process action in the order data synchronization feedback information is recorded and synchronized to obtain the order process feedback information.

7. The order synchronization processing method based on multiple platforms according to claim 1, characterized in that, Through a visual management backend, the customized order data and the order process feedback information can be configured visually to obtain order process monitoring information, specifically including: The customized order data and the order process feedback information are both configured with visualization charts to obtain full-process visualization information of the order and corresponding node links; The visual management backend displays the entire order process information and corresponding node links in a visual data format, and monitors the node data of each node link to obtain the order process monitoring information.

8. The order synchronization processing method based on multiple platforms according to claim 1, characterized in that, The order process monitoring information is updated and corrected by manually verifying the order process information at each node, and then synchronously updated to the corresponding order process nodes in the push platform and downstream enterprise systems.

9. A multi-platform-based order synchronization processing device, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, enabling the at least one processor to perform a multi-platform-based order synchronization processing method according to any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that, The storage medium is a non-volatile computer-readable storage medium that stores at least one program, each program including instructions that, when executed by a terminal, cause the terminal to perform a multi-platform order synchronization processing method according to any one of claims 1-8.

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