Automatic urging warehousing method based on RPA robot

Abstract

In order to solve the problem of low efficiency of online order processing flow in the prior art, the invention provides an automatic urging warehousing method based on an RPA robot. According to the method, an RPA robot automatically exports a return logistics order number and an order number through an Excel assembly to generate an order number corresponding table, obtains express information according to the order number corresponding table and generates a task progress table; and the RPA robot traverses the task progress table according to the priority, matches the information of the task units and the order number corresponding table in the task progress table, filters out the task units in a termination state according to a preset timestamp, regularly reminds the task units in an unprocessed state, and modifies the states of the task units after processing the task units. An exception handling mechanism is built in the RPA technology, integration of various systems and services is supported, and APIs of different platforms do not need to be called, so that the response speed is increased, human errors and cost are reduced, and the efficiency of an online order processing flow is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an automatic expediting goods entry method based on an RPA robot. Background Art

[0002] With the rapid development of the e-commerce industry, order processing has become an important part of its daily operations. Among them, in terms of return management, the traditional manual operation method requires manual export of orders and urging the return order number to enter the warehouse. This manual operation lacks standardized processes, has high communication costs and is prone to information errors, resulting in low order processing efficiency. Although the existing technology can improve order processing efficiency to a certain extent through automated scripts, when the scripts operate across platforms, the data layer and business logic layer of different e-commerce platforms cannot be effectively connected, so a simple automated script cannot solve the above problems. Therefore, a more efficient solution is urgently needed to optimize the order processing process.

[0003] The following examples describe the solutions of the prior art to the above problems. For example, the "Cross-platform Logistics Order Filling Method, Device, Terminal Equipment and Storage Medium" disclosed in Chinese patent documents, with announcement number CN112036791A, automatically identifies order information and automatically matches the optimal target logistics platform according to preset rules and third-party logistics matching lists, automatically generates logistics information and sends it to the e-commerce platform for display, and at the same time passes multi-level security detection and sends the logistics information to the blockchain to improve security. In summary, although this method can automatically match the optimal logistics platform, it may still be affected by the complexity and compatibility issues of cross-platform integration in actual applications, so that it must adopt manual intervention measures and bear the maintenance costs of the platform. Summary of the invention

[0004] The present invention mainly solves the problem of low efficiency of order processing in the prior art; in this regard, the present invention provides an automatic urging warehousing method based on RPA robots. The technical improvement lies in that the technical solution simplifies the urging warehousing process through RPA tools and improves the efficiency and accuracy of order processing. Compared with traditional automation scripts, RPA provides a more intuitive and flexible operation method, and non-technical personnel can easily create and maintain processes without writing complex codes. In addition, RPA has a built-in exception handling mechanism and supports the integration of multiple systems and services without calling APIs of different platforms, thereby improving response speed and reducing human errors and costs.

[0005] The above technical problems of the present invention are mainly solved by the following technical solutions: An automatic urging goods entry method based on an RPA robot of the present invention includes: the RPA robot automatically exports the return logistics order number and the order number through the Excel component to generate an order number correspondence table, obtains the express information according to the order number correspondence table and generates a task schedule; the RPA robot traverses the task schedule according to the priority, matches the task unit in the task schedule with the information in the order number correspondence table, filters out the task unit in the terminal state according to the preset timestamp, performs timed reminders on the task unit in the unprocessed state, and modifies the status of the task unit after processing. This automatic expediting method based on RPA (Robotic Process Automation) significantly improves warehouse management efficiency and reduces manual errors. By automatically generating a table of order numbers and obtaining express information, it not only speeds up data processing, but also ensures the accuracy of information. The function of traversing the task schedule according to priority makes resource allocation more reasonable and can respond to high-priority tasks in a timely manner. The preset timestamp mechanism can effectively manage task status and avoid invalid operations. At the same time, it implements regular reminders for unprocessed tasks to ensure the continuity and timeliness of work.

[0006] Preferably, the process of generating the order number correspondence table includes: the RPA robot calls the Excel component in the RPA tool to generate an order number capture module, the order number capture module automatically obtains the return logistics order number and order number and exports them; using the order number as a unique identifier, and generating an order number correspondence table based on the correspondence between the return logistics order number and the order number. The above content uses RPA robots to automatically obtain return logistics order numbers and order numbers, which not only greatly simplifies the data collection process, but also effectively avoids human errors that may be caused by manual input. Using the order number as a unique identifier ensures the uniqueness of each record, which is easy to track and manage. The order number correspondence table is automatically generated based on the correspondence between the return logistics order number and the order number, which further improves the accuracy and efficiency of data processing, making subsequent logistics tracking and goods warehousing operations smoother, reducing problems caused by information mismatch, and thus optimizing the entire supply chain management process.

[0007] Preferably, the process of generating the order number correspondence table also includes: identifying the order source through the keywords of the return logistics order number, storing the order number correspondence table using a relational database, and establishing a foreign key relationship; associating the order source with the order number correspondence table through a foreign key relationship to form a task unit, and the structure of a single task unit is [order number, order source, task status]. The above content enhances the flexibility and reliability of data processing. The keyword recognition function can automatically distinguish returns from different channels, simplify classification work, and make operations more efficient. The use of relational database for storage not only ensures the consistency and integrity of the data, but also facilitates complex queries and data analysis. By establishing a foreign key relationship to associate the order source with the order number corresponding table, a structured task unit is formed, so that each task unit can clearly display the core information of the order, such as the order number, order source, and task status, which helps to accurately manage and monitor the progress of each task, and also facilitates subsequent data maintenance and business expansion.

[0008] Preferably, the process of generating the order number correspondence table also includes: the order source includes the courier name and the order processing time, and the task status includes the task processing status and the task processing time; for the task unit whose order source cannot be identified, the order source is automatically queried on the Internet of Things platform based on the return logistics order number and supplemented in the structure of the task unit. The above content makes task management more refined by including detailed information such as courier name and order processing time in the task unit, which helps to quickly locate and process various orders. Adding task processing time to the task status can monitor the task progress in real time to ensure timely response. For task units that cannot directly obtain the order source, the system will automatically query and supplement on the IoT platform through the return logistics order number, which greatly reduces the situation of missing information and improves data integrity and processing efficiency. The various information matches each other and is associated through foreign keys to ensure data consistency.

[0009] Preferably, the process of obtaining express information according to the order number correspondence table and generating a task schedule includes adding priorities to individual task units to form a task item, arranging all task items according to priorities to generate a task table, and the priority items are jointly determined by the order processing time and the task processing time; identifying the task processing status in the task item, filtering out the task items with a terminal status, and generating a task schedule. The above content realizes intelligent scheduling of tasks by adding priority to each task unit and generating a task table by priority, ensuring that urgent or important tasks are processed first. The priority is determined by the order processing time and the task processing time, making resource allocation more reasonable and improving processing efficiency. By identifying and filtering out task items with a terminal processing status, repeated processing of completed tasks is avoided and unnecessary computing resource consumption is reduced. The generated task schedule clearly shows the order and status of pending tasks, which helps to monitor and adjust the workflow in real time, thereby improving overall work efficiency and service levels.

[0010] Preferably, the process of obtaining express information according to the order number correspondence table and generating a task schedule also includes: traversing the task schedule according to the priority, querying the logistics status corresponding to the task item on the Internet of Things platform according to the return logistics order number in the task item, marking the task items with a logistics status of collection and signed for as terminated, and regenerating the task schedule after removing the task items in the terminated state.

[0011] The above content can dynamically update the status of task items by traversing the task schedule according to priority and querying the logistics status in conjunction with the IoT platform to ensure that the latest and most urgent tasks are processed. Task items with a logistics status of collection and receipt are marked as terminal and removed from the task schedule, avoiding redundant processing of tasks in completed logistics links and saving processing time and computing resources. This method not only improves the timeliness and accuracy of task processing, but also optimizes the task management process, making resource allocation more efficient, and overall enhancing the operational efficiency and reliability of the system.

[0012] Preferably, the process of executing timed reminders for unprocessed task units includes: querying the task processing time in the task item according to the task schedule; if the task processing time is more than 48 hours from the current time, the RPA robot automatically initiates the order reminder process; if it is less than 48 hours, the next task item is executed until each task item is checked one by one; during the checking process, the task items that lack logistics information are retained and all their information is recorded and delivered to the set mailbox. The above content automatically initiates the order reminder process by setting a 48-hour time threshold, effectively ensuring timely follow-up of tasks that have not been processed for a long time and avoiding delays due to negligence. For tasks that do not reach the time threshold, the system continues to monitor to ensure that resources are used to handle more urgent tasks. In addition, during the investigation process, task items that lack logistics information are retained and recorded, and then sent to the set mailbox for processing, which can prevent the omission of important information and ensure that all tasks are properly managed. This method not only improves the timeliness and accuracy of task processing, but also enhances the reliability of the system and improves customer satisfaction.

[0013] Preferably, the process of executing timed reminders for unprocessed task units also includes: screening TK orders for the regenerated task schedule: the RPA robot uses the RPA component to build a query module, matches and finds the order numbers in each task item in the regenerated task schedule, obtains order information in the OMS system according to the order number, analyzes whether the task item corresponding to the order information contains a TK order, and processes the TK order. The above content optimizes task management through TK order screening, ensuring that all task items containing TK orders can be identified and processed in a timely manner. The query module is built using RPA components to automatically match order numbers and obtain order information in the OMS system, improving the speed and accuracy of data processing. The process of analyzing and processing TK orders enables special orders to be responded to quickly and avoid omissions.

[0014] Preferably, the process of processing the TK order includes: increasing the priority of the task item containing the TK order by 70% based on the original priority; the priority of the task item is determined by the order processing time and the task processing time: calculating the difference between the order processing time and the current time as data A, calculating the difference between the task processing time and the current time as data B, and taking the difference between 60% of data A and 40% of data B as the priority of the corresponding task item. The above content ensures that such special tasks can be processed faster and improves service response speed by increasing the priority of TK single task items. Priority calculation takes into account the order processing time and task processing time. Through scientific weight allocation, task scheduling is more reasonable, which not only ensures the timely processing of emergency tasks, but also takes into account the orderly progress of the overall tasks, thus improving overall operational efficiency.

[0015] Preferably, it also includes: the RPA robot communicates with different platforms through standardized protocols, abstractly encapsulates the different functions of different platforms through the middle layer, sets up RPA components in the form of plug-ins to support special connections with various platforms, simulates user operations, and executes tasks in the cloud.

[0016] The above content communicates with different platforms through standardized protocols, and uses the middle layer to abstract and encapsulate the different functions of each platform, so that the RPA robot can seamlessly connect to multiple systems, enhancing the compatibility and scalability of the system. Supporting specific functions in the form of plug-ins and connecting with various platforms can flexibly simulate user operations, reducing integration costs and complexity. Executing tasks in the cloud not only reduces the pressure on local computing resources, but also improves the stability and security of task execution, ensuring efficient and reliable service delivery.

[0017] The beneficial effects of the present invention are: 1. The present invention provides an automatic expediting warehousing method based on RPA robots, which realizes information exchange through cross-platform operations and can realize data flow without opening platform interfaces, thereby reducing the chances of human contact with data and significantly reducing the risk of data leakage. The RPA tool has built-in strict access control and identity authentication mechanisms, further enhancing data security. It automatically generates reports and notifies relevant persons in charge in a timely manner, ensuring that enterprises can immediately understand the order processing status and improving management efficiency.

[0018] 2. The present invention provides an automatic urging warehousing method based on RPA robots, which realizes the automated processing of urging orders for warehousing. It not only establishes a standardized process, but also overcomes the shortcomings of manual operations that are prone to errors and delays. More importantly, the process has high flexibility and scalability, and can easily expand functions and scale as business grows to adapt to higher order volumes. In the face of changes in business processes or rules, RPA tools can quickly adjust configurations to meet new needs, reduce maintenance costs, and improve the ability to respond to market changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of an automatic urging goods entry method based on an RPA robot according to the first embodiment of the present invention.

[0020] Figure 2 This is a flow chart of an automatic urging goods entry method based on an RPA robot according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0022] Embodiment 1: Please refer to Figure 1 In order to solve the problem of low efficiency of order processing in the prior art, this embodiment provides an automatic expediting warehousing method based on an RPA robot, and the specific steps are as follows.

[0023] The RPA robot automates the refund and after-sales management process on the Taobao platform, significantly improving work efficiency and accuracy. First, the RPA robot automatically logs in to the Taobao platform, and completes the tedious steps of entering the account password, processing the verification code, and slider verification without human intervention, ensuring quick and accurate access to the refund and after-sales management interface of the customer service. Subsequently, the robot selects the number of orders to be refunded, clicks the button for the merchant to receive the goods after entering the page, and exports the Excel document. This series of operations is efficient and reduces the risk of errors caused by manual operations.

[0024] Next, in the process of analyzing the express delivery receipt information, the Excel component in the RPA tool automatically obtains the return task item number (express delivery number) and order number information in the exported Excel document, and identifies the corresponding express delivery company through the keywords in the express delivery number (such as: YTO: YT, JT: JT, JDX: JDX). For those pure digital numbers that cannot be identified by keywords, the RPA robot will enter them into a special search website to query the express delivery company to ensure that all task items can be processed correctly.

[0025] In the step of creating a query table and importing it to the website for query, the robot fills in the information of the courier company, courier number and order number into the template, generates a task schedule, and imports it into the website for courier analysis. The task schedule generated in this process is not only convenient for tracking the status of each task, but also allows you to check the completed tasks and merge and export them after the tasks reach the final state, and save them to a fixed path to facilitate subsequent auditing and management.

[0026] When analyzing and exporting results, the system will filter out task items with a logistics status of "collected on behalf of" and "signed for". For task items without logistics information and their corresponding order information, the system will automatically record them and provide feedback to relevant personnel via email after the task is completed. In this way, the system can determine whether the latest time of the task item is more than 48 hours from the current time. Task items that are more than 48 hours away will be marked as requiring urging processing, while task items that are less than 48 hours away will be ignored, thus avoiding unnecessary urging operations and improving work efficiency.

[0027] When logging into the OMS system and analyzing whether to create a TK order, the RPA robot first logs into the OMS system, then filters out eligible task items by time, and uses the RPA tool to match and find the order numbers corresponding to these task items. Next, enter the order number in the after-sales center search box of the OMS system to determine whether there is a TK order in the search results. If there is a TK order, the next step is required. Otherwise, the relevant information is recorded and returned to the designated person by email after the task is completed.

[0028] Finally, in the process of executing TK order judgment, the system will filter out the tasks that have been terminated and put into storage, and process the remaining TK orders. If there is no order for TK orders in the search results, it will be recorded and returned to the specified email address after the task is completed. This series of operations ensures that all tasks can be properly processed and reduces the possibility of omissions.

[0029] In the step of automatically initiating communication, the RPA robot will operate the task items that need to be processed on the order query page of the OMS system, click the "Communicate" button and select the corresponding warehouse, fill in the express number and specific requirements. For example, if a task item has been signed for more than 48 hours, the robot will issue a request: Express number: YT7423782738283828; Express company: YTO; Signing time: 2022-05-26 12:12:12; Please ask the warehouse to verify the warehousing situation. Such an automated communication mechanism not only speeds up the problem solving, but also ensures the accuracy and consistency of the communication content.

[0030] Embodiment 2: Please refer to Figure 2In this embodiment, a method for processing an address library of an e-commerce platform is further improved on the basis of embodiment 1, and described in more detail. Among them, the RPA robot communicates with different platforms through standardized protocols, abstracts and encapsulates the different functions of different platforms through the middle layer, sets the RPA component in the form of a plug-in to support special communication with each platform, simulates user operations, and executes tasks in the cloud.

[0031] The RPA robot automatically exports the return logistics order number and order number through the Excel component to generate an order number correspondence table, obtains express information based on the order number correspondence table, and generates a task schedule. By adopting robotic process automation technology, this solution not only improves processing efficiency, but also significantly reduces human errors and ensures the accuracy and timeliness of data processing.

[0032] The process of generating the order number correspondence table is as follows: The RPA robot calls the Excel component in the RPA tool to generate a number capture module, which automatically obtains and exports the return logistics number and order number; uses the order number as a unique identifier, and generates a number correspondence table based on the correspondence between the return logistics number and the order number. This process automatically collects and organizes key information, greatly reducing the need for manual intervention, thereby reducing the error rate and improving work efficiency.

[0033] The order source is identified through the keywords of the return logistics order number, the order number corresponding table is stored in the relational database, and a foreign key relationship is established; the order source is associated with the order number corresponding table through the foreign key relationship to form a task unit, and the structure of a single task unit is [order number, order source, task status]. This not only facilitates information retrieval and management, but also ensures the consistency and integrity of the data, which helps to improve the accuracy of data analysis.

[0034] The order source includes the courier name and order processing time, and the task status includes the task processing status and task processing time. For task units whose order sources cannot be identified, the order source is automatically queried on the IoT platform based on the return logistics order number and added to the structure of the task unit. The various items in the structure of a single task unit match each other and are associated through foreign keys.

[0035] This approach ensures the information integrity of each task unit and can automatically complete the information even if the initial data is missing, reducing the problems caused by data inconsistency and improving the accuracy of data processing and the degree of automation of the process.

[0036] The process of obtaining express information and generating a task schedule based on the order number correspondence table is as follows: After adding priorities to individual task units, a task item is formed. All task items are arranged according to priorities to generate a task list. The priority item is determined by the order processing time and the task processing time. The task processing status in the task item is identified, and the task items with a terminal processing status are filtered out to generate a task schedule. This method ensures that urgent and important tasks are processed first, improves the efficiency and response speed of task processing, and at the same time avoids resource waste by filtering out completed tasks, maintaining the simplicity and effectiveness of the task list.

[0037] The task schedule is traversed according to the priority, and the logistics status of the task item is queried on the IoT platform according to the return logistics order number in the task item. The task items with the logistics status of collection and signed for are marked as terminated, and the task schedule is regenerated after the task items with the terminated status are removed. This process enables the system to dynamically update the task status and ensure that only tasks that require attention are left in the schedule, thereby improving management efficiency, reducing invalid operations, and ensuring that resources are reasonably allocated to the tasks to be processed.

[0038] The RPA robot traverses the task schedule according to the priority, matches the task unit in the task schedule with the order number information in the order number correspondence table, filters out the task units in the terminal state according to the preset timestamp, performs scheduled reminders on the unprocessed task units, and modifies the status of the task units after processing. In this way, the system can automatically track the status changes of tasks and promptly remind the processing of unfinished tasks, ensuring the timeliness and accuracy of task processing, and also providing transparency and controllability of task management through the status update mechanism.

[0039] During this process, the process of executing a timed reminder for the task units in the unprocessed state is as follows: According to the task schedule, query the task processing time in the task item. If the task processing time is more than 48 hours from the current time, the RPA robot automatically initiates the order reminder process. If it is less than 48 hours, execute the next task item until the task items are checked one by one. During the troubleshooting process, retain the task items that lack logistics information and record all their information and send it to the set mailbox. This mechanism ensures timely follow-up of timed tasks, avoids the impact caused by delayed processing, and retains and records the task items that lack logistics information, ensuring the visibility of the problem and the consistency of subsequent processing, and improving the reliability of the overall process.

[0040] For the regenerated task schedule, perform TK order screening: The RPA robot uses the RPA component to build a query module, match and find the order number in each task item in the regenerated task schedule, obtain the order information in the OMS system according to the order number, analyze whether the task item corresponding to the order information contains a TK order, and process the TK order. This process ensures that special orders (such as TK orders) can be identified and processed in a timely manner, enhances the ability to respond to special situations, improves service quality and processing efficiency, and also simplifies the workload of managers and improves overall operational efficiency.

[0041] The process of processing a TK order is as follows: For task items that include TK orders, the priority is increased by 70% based on the original priority; the priority of the task item is determined by the order processing time and the task processing time: the difference between the order processing time and the current time is calculated as data A, and the difference between the task processing time and the current time is calculated as data B. The difference between 60% of data A and 40% of data B is used as the priority of the corresponding task item.

[0042] What this means is that for those task items that contain special marks (TK singles), the system will increase their priority by 70% based on the original priority, which means that these special task items will be given a higher priority so that they can be processed faster.

[0043] The priority of a task item is determined by both the order processing time and the task processing time: first, the difference between the order processing time and the current time is calculated, i.e., data A; second, the difference between the task processing time and the current time is calculated, i.e., data B; finally, the priority of the task item is determined based on the weighted average of 60% of data A and 40% of data B. The purpose of this is to ensure that tasks that need to be processed as quickly as possible (such as tasks with longer order processing time or longer task processing time) can be processed first, thereby improving overall processing efficiency and service quality.

[0044] For example, if the order processing time of a task is 5 days and the task processing time is 2 days, then data A = 5 days, data B = 2 days, and the priority calculation formula is priority = (data A * 0.6) + (data B * 0.4), that is, priority = (5 * 0.6) + (2 * 0.4) = 3 + 0.8 = 3.8. Therefore, the priority of this task item is 3.8; if it is a task item that includes a TK order, its priority will be increased by 70% on the original basis, that is, the priority becomes 3.8 *1.7 = 6.46, so that the task item containing the TK order will be processed first.

[0045] For example, if the order processing time of a task is 3 hours and the task processing time is 1 hour, then data A = 3 hours, data B = 1 hour, and the priority calculation formula is priority = (data A * 0.6) + (data B * 0.4), that is, priority = (3 * 0.6) + (1 * 0.4) = 1.8 + 0.4 = 2.2. If this task includes a TK order, its priority will be increased by 70%, that is, the priority becomes 2.2 * 1.7 = 3.74, thereby ensuring that such tasks are processed first.

[0046] The benefit of this design is that by increasing the priority of task items containing TK orders by 70%, it ensures that these urgent tasks can be processed more quickly, reduces problems caused by delays, and improves service response speed; at the same time, by calculating the weighted average of order processing time and task processing time to determine priority, it ensures that resources are reasonably allocated to the tasks that need to be processed most, improves overall processing efficiency and resource utilization, and thus improves the smoothness of the overall business process and customer satisfaction.

[0047] It should be understood that the embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope limited by the appended claims of the application.

Claims

1. An automatic expediting goods entry method based on RPA robot, characterized in that: include: The RPA robot automatically exports the return logistics order number and order number through the Excel component to generate an order number correspondence table, obtains the express information based on the order number correspondence table, and generates a task schedule; The RPA robot traverses the task schedule according to priority, matches the information in the task unit and order number correspondence table in the task schedule, filters out the task units in the terminal state according to the preset timestamp, performs scheduled reminders for the task units in the unprocessed state, and modifies the status of the task unit after processing.

2. According to claim 1, the method for automatically urging goods to enter the warehouse based on RPA robot is characterized in that: The process of generating a tracking number correspondence table includes: The RPA robot calls the Excel component in the RPA tool to generate a tracking number capture module, which automatically obtains the return logistics tracking number and order number and exports them; Use the order number as the unique identifier and generate an order number correspondence table based on the correspondence between the return logistics order number and the order number.

3. According to claim 2, the method for automatically urging goods to enter the warehouse based on RPA robot is characterized in that: The process of generating the order number correspondence table also includes: Identify the order source through the keywords of the return logistics order number, use the relational database to store the order number corresponding table, and establish a foreign key relationship for the order number corresponding table; The order source is associated with the order number corresponding table through a foreign key relationship to form a task unit. The structure of each task unit is [order number, order source, task status].

4. According to claim 3, the method for automatically urging goods to enter the warehouse based on RPA robot is characterized in that: The process of generating the order number correspondence table also includes: The order source includes the courier name and order processing time, and the task status includes the task processing status and task processing time. For task units whose order sources cannot be identified, the order source is automatically queried on the IoT platform based on the return logistics order number and supplemented in the structure of the task unit.

5. The method for automatically urging goods to enter warehouse based on RPA robot according to claim 1 or 4, characterized in that: The process of obtaining express information and generating a task schedule based on the order number correspondence table includes: After adding a priority to a single task unit, a task item is formed. All task items are arranged according to the priority to generate a task table. The priority item is determined by the order processing time and the task processing time. Identify the task processing status in the task items, filter out the task items whose task processing status is a terminal state, and generate a task schedule.

6. According to claim 5, the method for automatically urging goods to enter the warehouse based on RPA robot is characterized in that: The process of obtaining express information and generating a task schedule based on the order number correspondence table also includes: Traverse the task items in the task schedule according to priority, query the logistics status of the task item on the Internet of Things platform according to the return logistics order number in the task item, mark the task items with logistics status of collection and signed for as terminated, remove the task items with terminated status and regenerate the task schedule.

7. The method for automatically urging goods to enter warehouse based on RPA robot according to claim 1 or 6, characterized in that: The process of performing a timed reminder for an unprocessed task unit includes: Query the task processing time in the task item according to the task schedule. If the task processing time is more than 48 hours from the current time, the RPA robot automatically initiates the order reminder process. If it is less than 48 hours, the next task item is executed until all task items are checked. During the troubleshooting process, keep the task items that lack logistics information and record all their information, and send it to the set mailbox.

8. According to claim 7, the method for automatically urging goods to enter the warehouse based on RPA robot is characterized in that: The process of performing a timed reminder for an unprocessed task unit also includes: For the regenerated task schedule, perform TK order screening: The RPA robot uses the RPA component to build a query module, match and find the order number corresponding to each task item in the regenerated task schedule, obtain the order information in the OMS system according to the order number, analyze whether the task item corresponding to the order information contains a TK order, and process the TK order.

9. The method for automatically urging goods to enter warehouse based on RPA robot according to claim 8, characterized in that: The process of processing a TK order includes: For tasks that include TK orders, the priority will be increased by 70% based on the original priority; The priority of a task item is determined by the order processing time and the task processing time: the difference between the order processing time and the current time is calculated as data A, and the difference between the task processing time and the current time is calculated as data B. The difference between 60% of data A and 40% of data B is used as the priority of the corresponding task item.

10. The method for automatically urging goods to enter warehouse based on RPA robot according to claim 1, characterized in that: Also includes: RPA robots communicate with different platforms through standardized protocols, abstract and encapsulate the different functions of different platforms through the middle layer, set up RPA components in the form of plug-ins to support special connections with various platforms, simulate user operations, and execute tasks in the cloud.

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