Tax refund management method and system based on first-in first-out principle

Abstract

The invention relates to a tax refund management method and system based on a first-in first-out principle, and the method comprises the following steps: employing a warehousing management robot to generate timestamps for accepted and warehoused goods according to the warehousing order of an acceptance bill, verifying the consistency of the warehoused goods and the acceptance bill, and generating a fund payment list; during delivery, according to the timestamp sequence and a first-in first-out principle, related information of delivered goods is generated; calculating the total amount of the delivered goods according to the related information of the delivered goods, automatically generating a delivery batch number and invoicing information, and sending the invoicing information to a supplier; after the invoice returned by the supplier is received, the invoice is matched through the purchase order and the delivery batch number, and the consistency of the cargo details, the amount and the quantity is verified; and verifying the consistency of the invoice and the customs declaration data through an intelligent matching algorithm, and generating a corresponding tax refund batch number and a corresponding tax refund amount.

Description

Technical Field

[0001] The present application relates to the technical field, and more specifically, to a tax refund management method and system based on the first-in-first-out principle. Background Art

[0002] In traditional cross-border e-commerce tax refund management, the association between shipping, purchasing, invoicing and customs declarations is complex and time-consuming. Enterprises need to manually match purchase orders with invoice information and ensure the consistency of all data to meet customs and tax requirements. However, with the growth of business volume, manual operations are not only prone to errors, but may also lead to confusion in tax refund batches and delay the progress of tax refunds. Therefore, there is an urgent need for an automated system that can efficiently associate shipping, purchasing, invoicing and customs declarations, generate tax refund batch numbers, and achieve accurate and fast tax refund management.

[0003] Prior art, such as the Chinese patent with patent number "CN117217942A", discloses a cross-system data interaction method, which includes generating sales invoice information based on the order data of the enterprise system; obtaining the sales bill data transmitted back from the sales invoice information. Also, after checking and verifying the input invoice of the enterprise system, an invoice voucher is generated, the status of the invoice voucher is marked, the input bill data is generated, the input bill data is stored in the input invoice pool, and the receipt information is sent to the enterprise system; according to the input tax deduction rules, the input bill in the input invoice pool is checked for tax refund and tax refund information is generated. This method can interact with the data of the enterprise system, the financial system and the invoicing system, automatically generate sales invoice information and input invoice information, and solve the problem of heavy workload and easy errors of financial personnel.

[0004] The problem with the above-mentioned existing technology is that the method does not propose a mechanism for checking and correcting the accuracy of the source data. If there is a problem with the source data, it will affect the progress of subsequent tasks; and the method does not mention what kind of encryption means, secure transmission protocols, etc. are used to ensure that the data is not stolen or tampered with during the transmission process. Once a data security problem occurs, it will cause serious losses to the enterprise; the method lacks a manual intervention mechanism and cannot detect and handle problems in a timely manner. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a tax refund management method and system based on the first-in-first-out principle.

[0006] The technical solution of the present invention is as follows:

[0007] The present invention proposes a tax refund management method based on the first-in-first-out principle, comprising the following steps:

[0008] Step S1: Use the warehouse management robot to generate timestamps for the goods received in the order of the acceptance list, verify the consistency between the goods received and the acceptance list, and generate a fund payment list;

[0009] Step S2, generating the shipment-related information according to the first-in-first-out principle according to the timestamp order during shipment;

[0010] Step S3, calculating the total amount of the shipped goods based on the relevant information of the shipped goods, automatically generating the shipping batch number and invoicing information, and sending the invoicing information to the supplier;

[0011] Step S4: After receiving the invoice returned by the supplier, match the invoice with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity;

[0012] Step S5: Verify the consistency of the invoice and customs declaration data through the intelligent matching algorithm, and generate the corresponding tax refund batch number and tax refund amount.

[0013] As a preferred implementation, the generation of the shipment-related information according to the first-in-first-out principle according to the timestamp order during shipment includes the following steps:

[0014] Match the key fields of the delivery note and the purchase order, including order number, product code, delivery quantity and amount;

[0015] The earliest batches are prioritized based on the timestamp sorting mechanism, and the shipment quantity, batch number and timestamp are recorded to ensure that the shipment sequence is consistent with the first-in-first-out principle;

[0016] When shipping, query the associated warehousing information and obtain the relevant information of the corresponding shipped goods in order;

[0017] When the delivery inventory is insufficient, the batches are split according to the order of entry time.

[0018] As a preferred implementation, during the step of generating the shipment-related information according to the first-in-first-out principle according to the timestamp order, a locking mechanism is used to lock the corresponding inventory of the goods marked as the consignment status.

[0019] As a preferred implementation, in the step of calculating the total amount of the shipped goods based on the relevant information of the shipped goods, and automatically generating invoicing information to send to the supplier, the kafka message queue is used to solve the problem of data duplication and loss during long-interval data synchronization.

[0020] As a preferred implementation, the steps of calculating the total amount of the shipped goods based on the relevant information of the shipped goods and automatically generating the shipping batch number and invoicing information include:

[0021] Automatically collect invoice information based on the mapping relationship between invoice and invoicing information. Generate centralized invoicing records in batches according to the purchase order time sequence for scenarios involving multiple purchase orders or suppliers, and ensure data consistency. When a shipment batch involves multiple invoicing information, automatically associate and merge the invoicing information to generate complete invoicing information for the shipment batch.

[0022] As a preferred implementation, after receiving the invoice returned by the supplier, the invoice is matched by the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity; when the invoice does not match the goods corresponding to the purchase order and the delivery batch number, the exception is recorded and the user is prompted to make manual adjustments.

[0023] As a preferred implementation, the verification of the consistency of the invoice and customs declaration data by the intelligent matching algorithm includes the following steps:

[0024] Data acquisition and preprocessing:

[0025] Extract cargo information-related data from invoices and customs declarations, including cargo name, quantity, amount and corresponding sku, and clean and standardize the collected cargo information-related data;

[0026] Fuzzy matching:

[0027] The collected cargo information is converted into vector representation, and the term frequency-inverse document frequency TF-IDF value is used as the weight of the vector; the cosine similarity algorithm is used to calculate the similarity between the cargo description vectors in the invoice and the customs declaration; the specific formula is as follows:

[0028]

[0029] Where A and B are the description vectors of goods A and B respectively; ‖A‖ and ‖B‖ are the moduli of vectors A and B respectively; if the calculated cosine similarity is greater than the preset value, it is determined to be consistent at the fuzzy matching level;

[0030] Exact comparison of values:

[0031] Directly compare the quantity and amount of goods in the invoice and the customs declaration to see if they are equal. Set an error range during the process. If the difference in quantity or amount exceeds the error range, it will be marked as abnormal data, triggering a manual review process.

[0032] As a preferred implementation, in the step of verifying the consistency of the invoice and customs declaration data through the intelligent matching algorithm and generating the corresponding tax refund batch number and tax refund amount:

[0033] The tax refund batch number is generated by timestamp, contract number and customs declaration number; the tax refund amount is based on the purchase order unit price and the export amount of the customs declaration, and the tax refund amount is calculated proportionally.

[0034] As a preferred implementation, in the process of steps S1-S5, log records are generated for each step through blockchain technology, and the log records cover the complete operation chain for internal audit and tax department verification.

[0035] On the other hand, the present invention also provides a tax refund management system based on the first-in-first-out principle, comprising:

[0036] In the warehousing management module, the warehousing management robot generates timestamps for the goods received in the order of the acceptance list, verifies the consistency between the goods received and the acceptance list, and generates a fund payment list.

[0037] The delivery management module generates the delivery-related information according to the first-in-first-out principle based on the timestamp order during delivery;

[0038] The invoicing information generation module calculates the total amount of the shipped goods based on the relevant information of the shipped goods, automatically generates the shipping batch number and invoicing information, and sends the invoicing information to the supplier;

[0039] Verification module: after receiving the invoice returned by the supplier, the invoice is matched with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity;

[0040] The tax refund information generation module verifies the consistency of invoice and customs declaration data through an intelligent matching algorithm, and generates the corresponding tax refund batch number and tax refund amount.

[0041] The present invention has the following beneficial effects:

[0042] 1. Automated matching reduces manual intervention and improves data accuracy.

[0043] 2. The system quickly matches the delivery and warehousing order information according to the FIFO principle and generates tax refund batches, which is less likely to cause inventory squeeze and improve the overall circulation timeliness of goods.

[0044] 3. The data of shipment, warehousing information, invoices and customs declarations are automatically linked within the system to ensure consistency and reduce the difficulty of process auditing and verification.

[0045] 4. All operation data records are saved through blockchain and cannot be tampered with manually, making it convenient for enterprises and tax authorities to trace and verify. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 This is a schematic diagram of the method flow of Example 1; DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0050] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0051] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0052] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0053] Embodiment 1:

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the specific embodiments of the present application and refer to the attached Figure 1 , clearly and completely describe the technical solution of the present invention.

[0055] In order to solve the problems of the prior art, the present invention provides a tax refund management method based on the first-in-first-out principle, comprising the following steps:

[0056] Step S1: Use the warehouse management robot to generate timestamps for the goods received in the order of the acceptance list, verify the consistency between the goods received and the acceptance list, and generate a fund payment list;

[0057] Step S2, generating the shipment-related information according to the first-in-first-out principle according to the timestamp order during shipment;

[0058] The shipment is generated according to the first-in-first-out principle according to the timestamp order; including the following steps:

[0059] Match the key fields of the delivery note and the purchase order, including order number, product code, delivery quantity and amount;

[0060] The earliest batches are prioritized based on the timestamp sorting mechanism, and the shipment quantity, batch number and timestamp are recorded to ensure that the shipment sequence is consistent with the first-in-first-out principle;

[0061] When shipping, query the associated warehousing information and obtain the relevant information of the corresponding shipped goods in order;

[0062] When the delivery inventory is insufficient, the batches are split according to the order of entry time.

[0063] During the delivery, in the step of generating the information related to the shipped goods according to the first-in-first-out principle according to the timestamp order, a locking mechanism is used to lock the corresponding inventory of the goods marked as the consignment delivery status.

[0064] In this embodiment,

[0065] Monitor inventory status in real time during delivery. When goods are shipped from a batch, the inventory quantity of the corresponding batch is automatically reduced and the timestamp list of remaining inventory is updated.

[0066] If the inventory of a certain batch is insufficient, the system will automatically allocate it to the next batch to ensure the continuity of the delivery operation.

[0067] Set detection rules to automatically identify abnormal operations that violate FIFO rules (such as manual transfers or emergency shipments).

[0068] When an anomaly is detected, the system will mark the problem batch and notify the administrator to handle it, while recording the exception log to ensure data traceability.

[0069] Step S3, calculating the total amount of the shipped goods based on the relevant information of the shipped goods, automatically generating the shipping batch number and invoicing information, and sending the invoicing information to the supplier;

[0070] In the step of calculating the total amount of the shipped goods based on the relevant information of the shipped goods, and automatically generating invoicing information to send to the supplier, the Kafka message queue is used to solve the problem of data duplication and loss during long-interval data synchronization.

[0071] In this embodiment:

[0072] The time required for the invoicing system to notify the goods entering the warehouse is relatively long, which may lead to data duplication and data loss.

[0073] The Kafka message queue is used to solve the problem of data duplication and loss during long-interval data synchronization. Specifically, the Kafka message queue solves the problem of data duplication or loss through 12 strategies, including: 1. Producer confirmation mechanism; 2. Increase the replication factor; 3. Monitoring and alarm; 4. Reasonable configuration and capacity planning; 5. Set the producer's acks parameter to "all"; 6. Adjust the log storage space and maximum message size; 7. Use compression; 8. Turn off automatic offset submission; 9. Ensure that the broker is configured correctly; 10. Use synchronous replication; 11. Adjust the flush interval; 12. Avoid using unclean leader elections.

[0074] When inventory is insufficient, the goods of the subsequent purchase order are shipped in advance, disrupting the original FIFO order and causing the problem of wrong order. By adding an early warning mechanism, when insufficient inventory or abnormal delivery order is detected, the system will automatically issue an alarm and mark the batches that need manual confirmation.

[0075] The steps of calculating the total amount of the shipped goods according to the relevant information of the shipped goods and automatically generating the shipping batch number and invoicing information include:

[0076] Automatically collect invoice information based on the mapping relationship between invoice and invoice information; for scenarios involving multiple purchase orders or suppliers, generate centralized purchase invoice records in batches according to the purchase order time sequence, and ensure data consistency; when a batch of shipped goods involves multiple invoice information, automatically associate and merge invoice information to generate complete invoice information for the batch of shipped goods. If there is an inconsistency between the invoice and the shipment data (such as quantity or amount deviation), the system automatically triggers the exception repair process, records the exception and prompts the user to make manual adjustments.

[0077] Step S4: After receiving the invoice returned by the supplier, match the invoice with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity;

[0078] After receiving the invoice returned by the supplier, the invoice is matched with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity; when the invoice does not match the goods corresponding to the purchase order and the delivery batch number, the exception is recorded and the user is prompted to make manual adjustments.

[0079] Step S5: Verify the consistency of the invoice and customs declaration data through the intelligent matching algorithm, and generate the corresponding tax refund batch number and tax refund amount.

[0080] In the step of verifying the consistency of the invoice and customs declaration data through the intelligent matching algorithm and generating the corresponding tax refund batch number and tax refund amount:

[0081] The tax refund batch number is generated by timestamp, contract number and customs declaration number; the tax refund amount is based on the purchase order unit price and the export amount of the customs declaration, and the tax refund amount is calculated proportionally.

[0082] The verification of the consistency of invoice and customs declaration data by intelligent matching algorithm includes the following steps:

[0083] Data acquisition and preprocessing:

[0084] Extract cargo information-related data from invoices and customs declarations, including cargo name, quantity, amount and corresponding sku, and clean and standardize the collected cargo information-related data;

[0085] Fuzzy matching:

[0086] The collected cargo information is converted into vector representation, and the term frequency-inverse document frequency TF-IDF value is used as the weight of the vector; the cosine similarity algorithm is used to calculate the similarity between the cargo description vectors in the invoice and the customs declaration; the specific formula is as follows:

[0087]

[0088] Where A and B are the description vectors of goods A and B respectively; ‖A‖ and ‖B‖ are the moduli of vectors A and B respectively; if the calculated cosine similarity is greater than the preset value, it is determined to be consistent at the fuzzy matching level;

[0089] Exact comparison of values:

[0090] Directly compare the quantity and amount of goods in the invoice and the customs declaration to see if they are equal. Set an error range during the process. If the difference in quantity or amount exceeds the error range, it will be marked as abnormal data, triggering a manual review process.

[0091] In this embodiment:

[0092] In the case of inconsistency between delivery and customs declaration time, the system allows multiple batches of invoicing data to be associated with the same customs declaration form, and automatically calculates the corresponding tax refund amount.

[0093] Automatically detect whether there are duplicate customs declaration numbers or unrelated customs declaration records, and generate abnormal reminders to ensure the completeness of the tax refund process.

[0094] This method uses a distributed log system to collect, store and analyze log data, and supports real-time retrieval and monitoring of large-scale operation data. The specific technical means are as follows:

[0095] Log signature and hash verification:

[0096] When each log is generated, a unique fingerprint is calculated using the cryptographic hash algorithm SHA-256 and stored in a separate log index file.

[0097] By comparing the log with its hash value, detect whether there is any tampering.

[0098] Blockchain technology for log storage:

[0099] Write log data to a private or consortium blockchain, and use the blockchain's immutability to store key log information. Data is irreversibly written in chronological order to form a complete operation chain. Provide multi-party verification to enhance data credibility.

[0100] User identity and rights management:

[0101] Strong Authentication:

[0102] Use multi-factor authentication (such as password + biometrics or dynamic tokens) to ensure the authenticity of the operator's identity (OAuth2.0, SAML).

[0103] Data tampering detection:

[0104] Log comparison and anomaly detection: Periodically compare the hash values ​​of historical logs to detect and mark tampering risks. Use anomaly detection algorithms (such as time series models) to identify abnormal patterns in logs.

[0105] Automatic error correction and manual intervention mechanism:

[0106] The system allows operators to make manual adjustments under special circumstances, but all manual operations will be recorded by the system and approval records will be generated to ensure compliance.

[0107] Embodiment 2:

[0108] This embodiment provides a tax refund management system based on the first-in-first-out principle, including:

[0109] In the warehousing management module, the warehousing management robot generates timestamps for the goods received in the order of the acceptance list, verifies the consistency between the goods received and the acceptance list, and generates a fund payment list.

[0110] The delivery management module generates the delivery-related information according to the first-in-first-out principle based on the timestamp order during delivery;

[0111] The invoicing information generation module calculates the total amount of the shipped goods based on the relevant information of the shipped goods, automatically generates the shipping batch number and invoicing information, and sends the invoicing information to the supplier;

[0112] Verification module: after receiving the invoice returned by the supplier, the invoice is matched with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity;

[0113] The tax refund information generation module verifies the consistency of invoice and customs declaration data through an intelligent matching algorithm, and generates the corresponding tax refund batch number and tax refund amount.

[0114] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0115] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0118] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A tax refund management method based on the first-in-first-out principle, characterized in that: The following steps are involved: Step S1: Use the warehouse management robot to generate timestamps for the goods received in the order of the acceptance list, verify the consistency between the goods received and the acceptance list, and generate a fund payment list; Step S2, generating the shipment-related information according to the first-in-first-out principle according to the timestamp order during shipment; Step S3, calculating the total amount of the shipped goods based on the relevant information of the shipped goods, automatically generating the shipping batch number and invoicing information, and sending the invoicing information to the supplier; Step S4: After receiving the invoice returned by the supplier, match the invoice with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity; Step S5: Verify the consistency of the invoice and customs declaration data through the intelligent matching algorithm, and generate the corresponding tax refund batch number and tax refund amount.

2. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: The shipment is generated according to the first-in-first-out principle according to the timestamp order; including the following steps: Match the key fields of the delivery note and the purchase order, including order number, product code, delivery quantity and amount; The earliest batches are prioritized based on the timestamp sorting mechanism, and the shipment quantity, batch number and timestamp are recorded to ensure that the shipment sequence is consistent with the first-in-first-out principle; When shipping, query the associated warehousing information and obtain the relevant information of the corresponding shipped goods in order; When the delivery inventory is insufficient, the batches are split according to the order of entry time.

3. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: During the delivery, in the step of generating the information related to the shipped goods according to the first-in-first-out principle according to the timestamp order, a locking mechanism is used to lock the corresponding inventory of the goods marked as the consignment delivery status.

4. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: In the step of calculating the total amount of the shipped goods based on the relevant information of the shipped goods, and automatically generating invoicing information to send to the supplier, the Kafka message queue is used to solve the problem of data duplication and loss during long-interval data synchronization.

5. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: The steps of calculating the total amount of the shipped goods according to the relevant information of the shipped goods and automatically generating the shipping batch number and invoicing information include: Automatically collect invoice information based on the mapping relationship between invoice and invoicing information. Generate centralized invoicing records in batches according to the purchase order time sequence for scenarios involving multiple purchase orders or suppliers, and ensure data consistency. When a shipment batch involves multiple invoicing information, automatically associate and merge the invoicing information to generate complete invoicing information for the shipment batch.

6. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: After receiving the invoice returned by the supplier, the invoice is matched with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity; when the invoice does not match the goods corresponding to the purchase order and the delivery batch number, the exception is recorded and the user is prompted to make manual adjustments.

7. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: The verification of the consistency of invoice and customs declaration data by intelligent matching algorithm includes the following steps: Data acquisition and preprocessing: Extract cargo information-related data from invoices and customs declarations, including cargo name, quantity, amount and corresponding sku, and clean and standardize the collected cargo information-related data; Fuzzy matching: The collected cargo information is converted into vector representation, and the term frequency-inverse document frequency TF-IDF value is used as the weight of the vector; the cosine similarity algorithm is used to calculate the similarity between the cargo description vectors in the invoice and the customs declaration; the specific formula is as follows: Where A and B are the description vectors of goods A and B respectively; ‖A‖ and ‖B‖ are the moduli of vectors A and B respectively; if the calculated cosine similarity is greater than the preset value, it is determined to be consistent at the fuzzy matching level; Exact comparison of values: Directly compare the quantity and amount of goods in the invoice and the customs declaration to see if they are equal. Set an error range during the process. If the difference in quantity or amount exceeds the error range, it will be marked as abnormal data, triggering a manual review process.

8. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: In the step of verifying the consistency of the invoice and customs declaration data through the intelligent matching algorithm and generating the corresponding tax refund batch number and tax refund amount: The tax refund batch number is generated by timestamp, contract number and customs declaration number; the tax refund amount is based on the purchase order unit price and the export amount of the customs declaration, and the tax refund amount is calculated proportionally.

9. A tax refund management method based on the first-in-first-out principle according to claim 1, characterized in that: During steps S1-S5, log records are generated for each step through blockchain technology. The log records cover the complete operation chain for verification by internal audit and tax departments.

10. A tax refund management system based on the first-in-first-out principle, characterized in that: include: In the warehousing management module, the warehousing management robot generates timestamps for the goods received in the order of the acceptance list, verifies the consistency between the goods received and the acceptance list, and generates a fund payment list. The delivery management module generates the delivery-related information according to the first-in-first-out principle based on the timestamp order during delivery; The invoicing information generation module calculates the total amount of the shipped goods based on the relevant information of the shipped goods, automatically generates the shipping batch number and invoicing information, and sends the invoicing information to the supplier; Verification module: after receiving the invoice returned by the supplier, the invoice is matched with the purchase order and the delivery batch number to verify the consistency of the goods details, amount and quantity; The tax refund information generation module verifies the consistency of invoice and customs declaration data through an intelligent matching algorithm, and generates the corresponding tax refund batch number and tax refund amount.

Images