Order information analysis method and system

Abstract

The invention relates to an order information analysis method and system. The method comprises the following steps: in response to an input instruction of a user, obtaining a service request instruction corresponding to the input instruction; according to the business request instruction, analyzing and processing order data of a target shop in an order system, and outputting an analysis and processing result; wherein the order system stores order data of shops authorized by different e-commerce platforms, and the integration of the order data of multiple platforms is combined with a user instruction processing technology, so that the analysis of order information is realized, the user operation process is simplified, and a data analysis result is provided; the problems of data islands and low analysis efficiency in multi-platform e-commerce management are effectively solved.

Description

Technical Field

[0001] This application relates to the field of e-commerce order management, and in particular to a method and system for analyzing order information. Background Technology

[0002] In the e-commerce sector, to expand market reach, businesses commonly adopt a multi-platform operation strategy, opening stores on multiple domestic and cross-border e-commerce platforms simultaneously. However, this model also brings significant challenges to order information management: each e-commerce platform operates independently, resulting in order data being scattered across multiple independent back-end systems.

[0003] Several solutions have been developed to address the issue of multi-platform order management. However, these solutions lack the ability to effectively integrate and process heterogeneous order data from multiple sources, thus reducing the user experience. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and system for analyzing order information.

[0005] A method for analyzing order information, comprising:

[0006] In response to the user's input command, obtain the business request command corresponding to the input command;

[0007] Based on business request instructions, process the order data of the target store in the order system and output the analysis and processing results;

[0008] The order system contains order data from authorized stores on different e-commerce platforms.

[0009] In another embodiment, prior to the step of processing order data in the order system according to the business request instruction, the method further includes:

[0010] Connect the data interfaces of different e-commerce platforms to the order system;

[0011] The data interface receives order data from various stores on different e-commerce platforms.

[0012] In another embodiment, receiving order data from various stores on different platforms through the data interface includes:

[0013] A pre-defined, unified data template;

[0014] The data interfaces of different e-commerce platforms are mapped to the data template to obtain standardized order data and then integrated.

[0015] In another embodiment, prior to the step of processing order data in the order system according to the business request instruction, the method further includes:

[0016] Upon receiving order data from different e-commerce platforms, the order data is verified.

[0017] The verified order data is synchronized to the order system, and the inventory data of the order system is updated.

[0018] In another embodiment, prior to the step of processing order data in the order system according to the business request instruction, the method further includes:

[0019] If the available inventory in the order system exceeds the inventory required for the order, the best shipping warehouse and logistics service are allocated according to warehouse distance / inventory age priority / delivery timeliness.

[0020] In another embodiment, order data in the order system is processed according to a business request instruction, and analysis results are output, including:

[0021] According to the business request instruction, the order data of the target store within a preset time period is obtained from the order system;

[0022] Based on the large language model, the order data is analyzed and processed to obtain the corresponding statistical results;

[0023] Trigger the data visualization engine to display the statistical results.

[0024] In another embodiment, the business request instruction includes at least one of the following: statistics on the total order sales data of the target store within a preset time period, statistics on the difference in order sales of the target store within the preset time period compared to the previous time period, and statistics on the difference in order sales of the target store on different e-commerce platforms within the preset time period.

[0025] In another embodiment, the method according to any of the foregoing claims further includes:

[0026] Real-time acquisition and updates of the entire logistics status of an order from shipment to receipt;

[0027] Based on preset anomaly detection rules, the logistics status is monitored, and an early warning is issued when an abnormal event is triggered.

[0028] An order information analysis system, comprising:

[0029] The order management module is used to access the data interfaces of different e-commerce platforms and receive order data from the store on different e-commerce platforms through the data interfaces; it is also used to respond to user input instructions, obtain business request instructions corresponding to the input instructions; and analyze and process the order data of the store on different e-commerce platforms according to the business request instructions, and output the analysis and processing results.

[0030] In another embodiment, the order information analysis system further includes:

[0031] The inventory management module is used to synchronize the verified order data to the inventory management module and update the inventory data in the inventory management module when the order management module receives order data from different e-commerce platforms.

[0032] The aforementioned order information analysis method and system, by combining multi-platform order data integration with user instruction processing technology, enables the analysis of order information, simplifies user operation processes, and effectively solves the problems of data silos and low analysis efficiency in multi-platform e-commerce management. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating an order information analysis method according to one embodiment;

[0035] Figure 2 A flowchart illustrating an order information analysis method according to another embodiment;

[0036] Figure 3 A flowchart illustrating an order information analysis method according to another embodiment;

[0037] Figure 4 A flowchart illustrating an order information analysis method according to another embodiment;

[0038] Figure 5 This is a connection diagram of an order information analysis system module according to one embodiment;

[0039] Figure 6 This is an internal structural diagram of a computer device according to one embodiment.

[0040] Explanation of reference numerals in the attached diagram: 501, Order Management Module; 502, Inventory Management Module; 503, Cross-border E-commerce Platform; 504, Domestic E-commerce Platform; 505, Independent Website E-commerce Platform. Detailed Implementation

[0041] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0043] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of this application, a first dataset may be referred to as a second dataset, and similarly, a second dataset may be referred to as a first dataset. Both the first dataset and the second dataset are resistors, but they are not the same dataset.

[0044] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having,” etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0045] With the development of e-commerce, the same store may operate on different e-commerce platforms, resulting in orders from different platforms. Existing order integration technologies have two main shortcomings: firstly, they lack the ability to effectively integrate and uniformly analyze multi-source heterogeneous order data.

[0046] Based on this, this application provides a method and system for analyzing order information.

[0047] In one embodiment, a method for analyzing order information, such as Figure 1 As shown, this includes steps 101 and 102. The analysis method can be applied to order systems that contain order data from authorized stores on different e-commerce platforms.

[0048] 101. Responding to user input instructions, obtain the corresponding business request instructions. In this step, the user inputs instructions through the interactive interface provided by the order system. These instructions can be structured instructions or natural language commands. The interactive interface input instructions may include fields such as sales order number, stock keeping unit (SKU) and category, order source platform, order sales amount, order address, order status, whether the order is abnormal, and order remarks. The order system analyzes and processes the user's input instructions (e.g., extracting keywords) to obtain the corresponding business request instructions.

[0049] 102. Based on business request instructions, process the order data of the target store in the order system and output the analysis results. The system parses the business request instructions, locates the target store and the specified time period, and then queries the corresponding order data from the order system's database. The order system pre-stores and integrates order data from different platforms. Based on the business request instructions, the system performs corresponding calculations and analyses on the acquired order data. After the analysis is completed, the system generates processing results and outputs them through the user interface. The output can be in the form of text or images, and the content can include order information from all platforms or display order information from each platform individually.

[0050] This embodiment combines multi-platform order data integration with intelligent natural language processing technology to achieve order information analysis, simplify user operation processes, provide intuitive data analysis results, and effectively solve the problems of data silos and low analysis efficiency in multi-platform e-commerce management.

[0051] In another embodiment, such as Figure 2 As shown, before the step of processing the order data in the order system according to the business request instruction, the steps include: step 201 and step 202.

[0052] 201. Integrate the data interfaces of different e-commerce platforms into the order system; complete the authorization and configuration with the target e-commerce platforms, and establish an independent and secure data access channel for each authorized store.

[0053] 202. Receive order data from various stores on different e-commerce platforms via data interfaces. After completing step 201, initiate an automated data synchronization process to continuously aggregate order data scattered across multiple platforms into the central order system.

[0054] By connecting to the data interfaces opened by various e-commerce platforms, orders can be automatically captured into a unified system. This allows the system to receive order information from sellers on cross-border e-commerce platforms, as well as from their independent websites and domestic e-commerce platforms.

[0055] This embodiment solves the problem of fragmented cross-platform data management for merchants by accessing data from multiple platforms, and achieves one-stop centralized management of order data.

[0056] In another embodiment, order data from various stores on different platforms is received via a data interface, including:

[0057] A unified data template is preset; the unified data template is a data structure preset within the system that defines standardized fields and formats, serving as a mapping target to convert heterogeneous order data from different e-commerce platforms into a unified format.

[0058] The data interfaces of different e-commerce platforms are mapped to the data template to obtain standardized order data and then integrated.

[0059] This embodiment constructs an efficient multi-source order data integration solution by defining a unified template and mapping it to the data interfaces of various platforms. It solves the problem of heterogeneous data structures across e-commerce platforms, achieving standardization and automatic synchronization of order information. This not only improves the efficiency of data access and processing but also provides an accurate and consistent data foundation for subsequent intelligent analysis and business processing.

[0060] In another embodiment, such as Figure 3 As shown, before the step of processing the order data in the order system according to the business request instruction, the steps include: step 301 and step 302.

[0061] 301. Upon receiving order data from different e-commerce platforms, validate the order data. The validation process is real-time and automatically triggered. Once the data receiving module successfully parses an order information, it immediately generates a validation task and sends it to the validation service queue.

[0062] Validating order data may, for example, include at least one of the following steps:

[0063] This step verifies whether the association between the product inventory code on the e-commerce platform and the inventory unit in the order system matches. This is the mapping verification process. Specifically, after receiving the product inventory code, the system uses a pre-set mapping table to determine whether the code is associated with an inventory unit in the order system. If the mapping relationship is successfully established, the verification passes; if the mapping relationship fails, the verification fails.

[0064] Verify that the available inventory in the order system is greater than the required inventory for the order. This step is called inventory verification. If the available inventory in the order system is greater than the required inventory for the order, the verification passes and the system can arrange shipment. If the available inventory in the order system is less than the required inventory for the order, the verification fails, the system will mark the order as an "insufficient inventory" abnormal state, and may trigger predefined abnormal handling procedures, such as sending an alarm notification to operations personnel or placing the order in a pending processing queue to await inventory replenishment.

[0065] The system checks if the account balance is greater than the shipping cost of the current order. Different e-commerce platforms maintain a balance in their order systems. When shipping costs are due, the system checks if the account balance meets the requirements. If the balance is greater than the shipping cost, the check passes, and the system can arrange shipment. If the balance is less than the shipping cost, the check fails, and the system marks the order as "insufficient balance," potentially triggering predefined exception handling procedures. These procedures might include sending an alert to operations staff or placing the order in a waiting queue for the corresponding e-commerce platform to top up the balance.

[0066] By verifying order information, issues such as incorrect order shipments due to stock shortages, overselling due to insufficient stock, and delivery interruptions due to inability to pay shipping fees can be avoided. Transforming manual post-processing checks into a systematic, automated safeguard improves the accuracy, automation level, and business reliability of order processing across multiple platforms.

[0067] 302. Synchronize the validated order data to the order system and update the inventory data in the order system. For orders that pass all validation items, the system marks them as "valid orders" and writes their complete data to the core database table of the order system. After synchronization is complete, the system performs an inventory update operation, which can be to subtract the quantity required for this order from the "available inventory" of the product and add it to the "occupied locked inventory".

[0068] This embodiment establishes a secure, reliable, and real-time order processing workflow by introducing a verification and inventory synchronization mechanism. This reduces operational risks and customer complaints caused by data errors, overselling of inventory, or insufficient funds, and also ensures consistency between online and offline inventory data through real-time inventory updates.

[0069] In another embodiment, prior to the step of processing order data in the order system according to a business request instruction, the method further includes:

[0070] If the available inventory in the order system exceeds the inventory required for the order, the best shipping warehouse and logistics service will be allocated based on warehouse proximity, and / or inventory age priority, and / or delivery time.

[0071] Among these, warehouse distance refers to the geographical distance between the candidate warehouse and the delivery address. Understandably, the shorter the warehouse distance, the lower the shipping cost and the higher the priority. Inventory age priority refers to the length of time the inventory has been stored in the warehouse; the longer the storage time, the higher the inventory age. The system typically uses a "first-in, first-out" principle for inventory, prioritizing older inventory for shipment. Delivery timeliness is the average processing time required from order acceptance to package delivery to the logistics provider; a higher delivery timeliness results in higher priority.

[0072] It is understandable that the allocation of optimal shipping warehouses and logistics services in this embodiment based on warehouse distance / warehouse age priority / delivery timeliness can be based on a single criterion, such as unconditionally assigning shipping tasks to the nearest warehouse; or it can be achieved by using a weighted algorithm to comprehensively consider the three factors and obtain an optimal allocation strategy for shipping warehouses and logistics services, such as prioritizing warehouses with closer proximity and older warehouse age while meeting the minimum delivery timeliness requirement. This embodiment does not limit the allocation strategy for shipping warehouses and logistics services.

[0073] This embodiment improves the operational efficiency of warehousing and logistics by setting allocation rules for shipping warehouses and logistics services in the order system, transforming manual allocation into system data-driven allocation. This ensures delivery efficiency while reducing logistics costs and the risk of inventory backlog.

[0074] In another embodiment, the order data in the order system is processed according to the business request instruction, and the analysis and processing results are output, including: steps 401, 402, and 403.

[0075] 401. Based on the business request instruction, retrieve the order data of the target store within a preset time period from the order system. Upon receiving the business request instruction, the system will parse the instruction and extract two key parameters: the target store and the preset time period, and obtain the corresponding order data based on these two key parameters.

[0076] 402. Based on the large language model, analyze and process order data to obtain corresponding statistical results. The large language model accessed by the system receives order data, combines it with business request instructions, analyzes the user's intent, and finally performs analysis and processing to obtain corresponding statistical results, and outputs an analysis instruction.

[0077] 403. Trigger the data visualization engine to display the statistical results. The data visualization engine receives the analysis instructions output by the large language model and generates visual analysis charts and structured reports based on the analysis instructions.

[0078] One shortcoming of existing technologies is the lack of an intuitive human-computer interaction method, making it difficult for users without a technical background to easily gain in-depth business insights. This embodiment constructs an automated "query-analysis-display" process. By integrating a large language model, the system does not need to pre-write all analysis code, but instead gains the ability to dynamically respond to complex user queries, improving the flexibility of data analysis and the user experience.

[0079] In other embodiments, the analysis instructions output by the large language model also include reasons for differences, improvement suggestions, etc., and the data visualization engine can provide sales suggestions to merchants based on the output analysis instructions.

[0080] In another embodiment, the business request instruction includes at least one of the following: statistics on the total order sales data of the target store within a preset time period, statistics on the difference in order sales of the target store within the preset time period compared to the previous time period, and statistics on the difference in order sales of the target store on different e-commerce platforms within the preset time period.

[0081] When the business request instruction is to collect the total sales data of the target store within a preset time period, the system treats all the order data of the target store within the preset time period as a whole dataset. The large language model connected to the system performs summation calculation on the data representing transaction amount in the dataset and generates analysis instructions to be output to the data visualization engine. The data visualization engine generates a result reflecting the total sales data of the target store within the preset time period based on the analysis instructions.

[0082] When the business request instruction is to calculate the difference in order sales of a target store within a preset time period compared to the previous time period, similar to the steps described above, the system obtains the first dataset for the preset time period and the second dataset for the previous time period. The large language model calculates the total sales amount for each dataset and calculates the difference between the total sales amounts for the two time periods. After obtaining the difference value, an analysis instruction is generated and sent to the data visualization engine. The data visualization engine then generates a result showing the difference in order sales of a target store within a preset time period compared to the previous time period based on the analysis instruction.

[0083] When the business request instruction is to statistically analyze the differences in order sales of a target store across different e-commerce platforms within a preset time period, similar to the steps described above, the system obtains the order sales from the first e-commerce platform within the preset time period as the third dataset, the order sales from the second e-commerce platform as the fourth dataset, and so on. The large language model calculates the total sales amount for each dataset and calculates the difference between the total sales amounts corresponding to different e-commerce platforms. After obtaining the difference value, an analysis instruction is generated and sent to the data visualization engine. The data visualization engine then generates a statistical result of the differences in order sales of a target store across different e-commerce platforms within a preset time period based on the analysis instruction.

[0084] This embodiment analyzes the above-mentioned instruction types with analytical intent, transforming complex business problems into standardized data processing tasks, enabling users to efficiently obtain deterministic analysis results of different dimensions and scales.

[0085] In another embodiment, the order information analysis method further includes: acquiring and updating the entire logistics status of the order from shipment to receipt in real time; monitoring the logistics status based on preset anomaly judgment rules, and automatically issuing an early warning when an abnormal event is triggered.

[0086] It acquires and updates the entire logistics status of an order in real time, from shipment to receipt. This status includes, but is not limited to, warehouse order receipt, order placement, picking, outbound shipment, courier pickup, in transit, being delivered, and successful delivery. Real-time acquisition of this end-to-end status ensures the timeliness of order information.

[0087] Based on preset anomaly detection rules, the logistics status is monitored, and an alert is automatically issued when an anomaly event is triggered. Anomalies include, but are not limited to, overdue orders, logistics service anomalies, and internet access anomalies. For example, the warehouse must complete the process from receiving an order to delisting within 4 hours; otherwise, it is marked as an overdue order. If the logistics information cannot be obtained normally from the logistics service provider, the order is marked as a logistics service anomaly. If there is no internet access information for an order within 24 hours after the warehouse ships the goods, it is marked as an internet access anomaly.

[0088] This embodiment enables visualized management of the order completion process through real-time tracking of the entire logistics chain and rule-based automatic early warning, strengthens the risk control mechanism, and improves customer experience.

[0089] This application proposes an order information analysis system, including:

[0090] The order management module is used to access the data interfaces of different e-commerce platforms and receive order data from the store on different e-commerce platforms through the data interfaces; it is also used to respond to user input instructions, obtain the corresponding business request instructions; and based on the business request instructions, analyze and process the order data of the store on different e-commerce platforms and output the results.

[0091] The order management module is the core of the system, responsible for the entire process management from data access to result output. Its functions are implemented through the following sub-processes:

[0092] Data Access and Integration: This module connects to data interfaces of different e-commerce platforms and receives order data from authorized stores on these platforms. Optionally, this module pre-sets a unified data template to standardize and transform heterogeneous order data from various platforms through mapping relationships, and then integrates and stores it in the system's unified database.

[0093] Command Response and Processing: This module provides a user interface to receive user input commands, which can be structured commands or natural language commands. For structured commands, the order management module analyzes the structured commands to obtain business request commands. For natural language commands, this module uses a pre-defined large language model for semantic understanding and converts them into business request commands. Based on the business request commands, the module analyzes and processes the order data and outputs analysis commands.

[0094] Output Results: Used to output analysis results based on analysis commands. The generated analysis results can be displayed on the user interface in the form of text, charts, or reports. For example, it can trigger a data visualization engine to render the statistical results as bar charts or pie charts.

[0095] In this embodiment, the system uses the order management module to achieve one-stop, full-link automated processing of multi-platform order data, from access, integration, intelligent parsing to analysis and visualization.

[0096] In another embodiment, the order information analysis system further includes:

[0097] The inventory management module is used to verify the order data when the order management module receives order data from different e-commerce platforms; and to store the order data that passes the verification.

[0098] The verification may include at least one of the following:

[0099] Data verification function: After the order management module receives new order data from different e-commerce platforms, the inventory management module is triggered and performs real-time verification of the order data. The verification includes, but is not limited to:

[0100] Mapping relationship verification: Verify whether the product codes on the e-commerce platform have been correctly associated with the internal inventory units of the system.

[0101] Inventory adequacy check: Query the current inventory and verify whether the available inventory quantity is greater than or equal to the quantity required by the order.

[0102] Balance verification: Verify whether the store account balance is sufficient to cover the estimated shipping cost of the order.

[0103] Data storage and update function: For orders that pass all verification items, the inventory management module marks them as "valid orders" and writes their complete data into the core database table of the order system. After synchronization is completed, the system performs an inventory update operation, which can be to subtract the quantity required for this order from the "available inventory" of the product and add it to the "occupied locked inventory".

[0104] The order management module and the inventory management module communicate through an internal system interface. A typical workflow is as follows: the order management module receives the raw order data and then calls the verification service of the inventory management module; the inventory management module returns the verification result; the order management module decides, based on the verification result, whether to store the data and proceed with the process, or to mark the order as abnormal.

[0105] This embodiment introduces an order management module and separates resource verification into the order management module, which can achieve functional decoupling, reduce the difficulty of maintaining different modules, and improve the reliability of the system.

[0106] Order information analysis systems can, for example, Figure 5 As shown, the order information analysis system includes an order management module 501, an inventory management module 502, a cross-border e-commerce platform 503, a domestic e-commerce platform 504, and an independent website e-commerce platform 505.

[0107] The order management module 501 connects to the data interfaces of the cross-border e-commerce platform 503, the domestic e-commerce platform 504, and the independent website e-commerce platform 505, and receives order data from authorized stores on different platforms through these interfaces.

[0108] The order management module 501 and the inventory management module 502 are communicatively connected. The inventory management module 502 is used to perform real-time verification of the order data received by the order management module 501. Verified order data is stored, and the inventory data of the order system is updated in real time.

[0109] The order management module 501 connects to a large language model to perform semantic understanding of users' natural language commands and convert them into business request instructions. Based on the business request instructions, it analyzes and processes order data, outputs analysis instructions, and generates analysis and processing results.

[0110] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for analyzing order information. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0111] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to this application and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the above method embodiment.

[0113] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the above method embodiment.

[0114] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above method embodiments.

[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0116] In the description of this specification, references to terms such as "some embodiments," "other embodiments," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiments or examples.

[0117] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing order information, characterized in that, include: In response to the user's input command, obtain the business request command corresponding to the input command; Based on business request instructions, process the order data of the target store in the order system and output the analysis and processing results; The order system contains order data from authorized stores on different e-commerce platforms.

2. The method according to claim 1, characterized in that, Before the step of processing order data in the order system according to business request instructions, the method further includes: Connect the data interfaces of different e-commerce platforms to the order system; The data interface receives order data from various stores on different e-commerce platforms.

3. The method according to claim 2, characterized in that, The process of receiving order data from various stores on different platforms through the data interface includes: A pre-defined, unified data template; The data interfaces of different e-commerce platforms are mapped to the data template to obtain standardized order data and then integrated.

4. The method according to claim 2, characterized in that, Before the step of processing order data in the order system according to business request instructions, the method further includes: Upon receiving order data from different e-commerce platforms, the order data is verified. The verified order data is synchronized to the order system, and the inventory data of the order system is updated.

5. The method according to claim 4, characterized in that, Before the step of processing order data in the order system according to business request instructions, the method further includes: If the available inventory in the order system exceeds the inventory required for the order, the best shipping warehouse and logistics service are allocated according to warehouse distance / inventory age priority / delivery timeliness.

6. The method according to claim 1, characterized in that, Based on business request instructions, process the order data in the order system and output the analysis and processing results, including: According to the business request instruction, the order data of the target store within a preset time period is obtained from the order system; Based on the large language model, the order data is analyzed and processed to obtain the corresponding statistical results; Trigger the data visualization engine to display the statistical results.

7. The method according to claim 6, characterized in that, The business request instruction includes at least one of the following: statistics on the total order sales data of the target store within a preset time period, statistics on the difference in order sales of the target store within the preset time period compared to the previous time period, and statistics on the difference in order sales of the target store on different e-commerce platforms within the preset time period.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: Real-time acquisition and updates of the entire logistics status of an order from shipment to receipt; Based on preset anomaly detection rules, the logistics status is monitored, and an early warning is issued when an abnormal event is triggered.

9. An order information analysis system, characterized in that, include: The order management module is used to access the data interfaces of different e-commerce platforms and receive order data from the store on different e-commerce platforms through the data interfaces. It is also used to respond to user input instructions, obtain business request instructions corresponding to the input instructions, and, based on the business request instructions, analyze and process the order data of the store on different e-commerce platforms and output the analysis results.

10. The system according to claim 9, characterized in that, Also includes: The inventory management module is used to verify the order data when the order management module receives order data from different e-commerce platforms; The verified order data is synchronized to the inventory management module, and the inventory data in the inventory management module is updated.

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