A method for automatic compliance checking of waybills

Abstract

The application discloses a kind of automatic verification methods of waybill compliance verification system, and combines image recognition technology, internet of things technology, geographic location processing technology etc., after user submits document data.The compliance verification service of document in background is automatically triggered, and the compliance verification process of classification is carried out according to various businesses: the accuracy of data, document data etc. involved in logistics transportation process is verified by cross verification matching, and whether document is compliance is quickly and accurately analyzed.

Description

Technical Field

[0001] This application relates to an automatic verification method for waybill compliance. Background Technology

[0002] Waybill information typically involves a large amount of data, including basic information and carrier information. Basic information includes driver's license information, vehicle license information, and other certificate information. Waybill information includes electronic waybill documents, bank statements, return receipts, tracking data, tax registration information, and scanned copies of contracts. Regarding waybill compliance, manual processing is conventionally used. When manually reviewing waybill compliance, verifiers need to manually check each of the above items to ensure the authenticity and validity of documents, the correspondence between bank statements and the waybill, the clarity and accuracy of return receipts, the consistency of tracking with the loading and unloading locations of the goods, and the absence of abnormalities in tax registration. Because of the human element, the verification process can lead to inconsistent standards, the inability to determine whether multiple waybills are used repeatedly based on bank documents, the reasonableness of vehicle tracking, and a large amount of repetitive work, resulting in low efficiency. Whether various documents within a single waybill have passed review, and whether the entire waybill has passed review, still requires human judgment, which is time-consuming and labor-intensive. There is an urgent need for an automated review system that can schedule various business reviews, summarize the reviews, and arrive at a final approval result. Summary of the Invention

[0003] The purpose of this invention is to provide an automatic verification method for waybill compliance, so as to overcome the shortcomings of the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a verification method for an automatic verification system for waybill compliance, comprising: Step 1: Configure audit items. Configure 6 audit items and set all values ​​of bit 0 to bit 5 of AuditMode to 1; Step 2: Establish a probe service, masquerading as a MySQL slave database, to monitor and audit project data items; Step 3: Send the inspection and audit project data items to Kafka for decoupling; Step 4: The scheduling service subscribes to Kafka, and the scheduling service categorizes data according to business type, forwarding different types of data to the corresponding Kafka topics; Step 5: Set up four verification services: basic information verification, payment verification, receipt verification, and contract verification. Assign the six verification items to the corresponding verification services. The verification service subscribes to the corresponding Kafka topic and processes similar data verifications in a distributed manner. Step 6: The verification service performs verification based on the compliance verification model data and verification rules. If the verification fails, the reason for the failure is recorded. Step 7: Record two values ​​in Redis: AuditSuccFlag and WaybillAuditFlag. WaybillAuditFlag records the audited items, and AuditSuccFlag records the result value of the audited items, set to 1 if the audit is successful. After successful verification, retrieve the AuditSuccFlag value from Redis based on the waybill, set the corresponding bit of the AuditSuccFlag item to 1, and then write the result AuditSuccFlag back to Redis. Step 8: After the corresponding project verification is completed, retrieve the WaybillAuditFlag from Redis based on the corresponding waybill number, set the bit of the WaybillAuditFlag corresponding to the project to 1, write the result WaybillAuditFlag back to Redis, and notify the audit summary service; Step 9: After receiving the project verification completion notification, the verification summary service retrieves the WaybillAuditFlag and AuditSuccFlag of the waybill from Redis. It performs a bitwise AND operation between WaybillAuditFlag and the configured AuditMode, and compares the result value with AuditMode. If they are exactly the same, it means that all items of the waybill have been reviewed and completed. It then checks whether AuditSuccFlag & AuditMode are equal to AuditMode. If they are equal, the final review result of the waybill is that the review is passed; otherwise, the review fails. Step 10: The summary service writes the final audit results of the waybill back to the database.

[0005] Preferably, the audit items include: carrier information, driver information, vehicle information, payment transaction information, receipt information, and contract information.

[0006] Preferably, when the Kafka callback notification data arrives, the verification service notifies the OCR service to recognize the corresponding type of image. The OCR service recognizes the data on the image and returns the result to the corresponding verification service. Preferably, the compliance verification model includes: a carrier information compliance verification model, including... Modeling data items: Name, ID number, ID card validity period, ID card photo (front side), ID card (back side); Verification rules: The name matches the name in the picture, the ID number matches the ID number in the picture, and the ID card is valid and not expired; Driver information compliance modeling data includes: Modeling data items: driver's name, driver's ID number, ID card validity period, address, issuing authority, driver's license name, driver's license ID number, permitted vehicle type, ID card photo, ID card back photo, driver's license photo; Verification rules: The name must match the name in the ID card and driver's license photos; the ID number must match the name in the ID card and driver's license photos; the ID card must not be expired; the age limit is 20-60 years old. The vehicle information compliance verification model includes: Modeling data items: license plate number, vehicle owner, road transport permit number, image of the front page of the vehicle registration certificate, image of the back page of the vehicle registration certificate, and image of the road transport permit; Verification rules: The license plate number must match the images of the vehicle registration certificate and the road transport permit. The payment transaction information compliance verification model includes: Modeling data items: payment transaction number, payer's name, payee's name, freight payment amount, and payment voucher image; Verification rules: The payer and payee must match the payment voucher image; the payment amount on the payment voucher image must exceed the freight payment amount on the waybill. The compliance verification model for receipt information includes: Modeling data items: Waybill number, license plate number, transport tonnage, loading and unloading time, and return receipt image; Verification rules: The waybill number, license plate number, transport tonnage, loading and unloading time must be consistent with the data on the return receipt image; Contract information compliance verification model, including: Modeling data items: Contract number, Party A, Party B, Contract amount, Contract image; Verification rules: The contract number, Party A, Party B, and contract amount must match the data in the contract image.

[0007] Compared with existing technologies, the verification method of the automatic verification system for waybill compliance proposed in this application standardizes and regulates different types of audit services, making the services more specialized and facilitating cluster deployment. Furthermore, by establishing a bitmask pattern, it is extremely convenient to quickly determine audit items and results through bitwise operations, and the bitmask pattern is easily expandable. The entire invention combines OCR intelligent recognition and cross-verification of data, which can greatly reduce manpower and achieve the goal of cost reduction and efficiency improvement. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart of an automatic verification method for waybill compliance according to a specific embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] Combination Figure 1 As shown, this invention combines image recognition technology, IoT technology, and geolocation processing technology to automatically trigger a backend document compliance verification service after the user submits document data. The verification process is categorized according to various business types: cross-checking and matching data and documents involved in logistics and transportation to verify data accuracy; quickly and accurately analyzing which document data is compliant and automatically approved; which document data is obviously incorrect and automatically fails to pass the review, returning it to the driver for re-upload; and which document data requires manual processing by reviewers. Through automated and rapid analysis, most document data can be processed automatically by the system, with only a very small number requiring manual intervention. This greatly saves labor costs and speeds up the review process. Automated recognition can be performed 24 / 7 without hindering business processing.

[0012] To achieve the above functions, it is necessary to classify and model the data items for compliance verification in the document data: 1. Carrier Information Compliance Verification Model: Modeling data items: Name, ID number, ID card validity period, ID card photo (front side), ID card (back side) Verification rules: The name matches the name in the picture, the ID number matches the one in the picture, and the ID card is valid and not expired. 2. Driver information compliance modeling data: Modeling data items: Driver's name, driver's ID number, ID card validity period, address, issuing authority, driver's license name, driver's license ID number, permitted vehicle types, ID card photo, ID card back photo, driver's license image. Verification rules: The name must match the name in the ID card and driver's license photos; the ID number must match the name in the ID card and driver's license photos; the ID card must not be expired; and the age limit is 20-60 years old. 3. Vehicle Information Compliance Verification Model: Modeling data items: license plate number, vehicle owner, road transport permit number, front page image of vehicle registration certificate, back page image of vehicle registration certificate, and road transport permit image. Verification rules: The license plate number must match the images of the vehicle registration certificate and the road transport permit. 4. Payment transaction information compliance verification model: Modeling data items: Payment transaction number, payer's name, payee's name, shipping fee payment amount, payment voucher image. Verification rules: The payer and payee must match the payment voucher image, and the payment amount on the payment voucher image must exceed the freight payment amount on the waybill. 5. Validation model for compliance of receipt information: Modeling data items: Waybill number, license plate number, transport tonnage, loading and unloading time, return receipt image. Verification rules: The waybill number, license plate number, tonnage, loading and unloading time must be consistent with the data on the delivery receipt image. 6. Contract Information Compliance Verification Model: Modeling data items: Contract number, Party A, Party B, Contract amount, Contract image Verification rules: The contract number, Party A, Party B, and contract amount must match the data in the contract image.

[0013] To achieve rapid scheduling and review of various types of data on waybills and quickly determine whether the review is complete, a standard review model for waybills needs to be established, represented by a 64-bit unsigned integer (AuditMode). An unsigned integer can support a maximum of 64 review items. Based on the data currently covered by waybills, six items need to be verified, occupying bits 0 to 5 for the items requiring review. bit0 Carrier Review Items bit1 Driver verification items bit2 Vehicle Verification Items bit3 payment review items bit4 Receipt Review Items bit5 Contract Review Items For each waybill, a waybill verification item variable (WaybillAuditFlag, 64-bit unsigned integer) is set. When a verification item is completed, the corresponding bit of WaybillAuditFlag is set to 1.

[0014] The aggregated audit service performs a bitwise AND operation between WaybillAuditFlag and AuditMode. If the result equals AuditMode, then all projects have been audited; otherwise, there are still projects pending audit.

[0015] The waybill also needs to establish an audit success flag variable (AuditSuccFlag, a 64-bit unsigned integer) to record successfully audited items. Each bit corresponds to the item specified in AuditMode. When an item is successfully audited, the corresponding bit in this variable is set to 1. Once the aggregate audit service determines that all items in the waybill have passed audits, it performs a bitwise AND operation between AuditSuccFlag and AuditMode. If the result equals AuditMode, the waybill audit is successful; otherwise, the audit fails.

[0016] If 64 items are not enough, the array can be expanded to achieve the goal of not limiting the number of items to be reviewed.

[0017] Specifically, the present invention provides an automatic verification method for waybill compliance, comprising: Step 1) Configure AuditMode so that bits 0 through 5 are all 1. Six projects need to be audited. Step 2) Establish a probe service, disguised as a MySQL slave database, to monitor and verify the data items of interest to the model in real time (data items corresponding to carrier information, driver information, vehicle information, payment information, return receipt information, and contract information). Step 3) Send the detected data items to Kafka for decoupling. Step 4) The scheduling service subscribes to Kafka, and Kafka callbacks notify the scheduling service of the arrival of new data. Step 5) The scheduling service categorizes business data according to business type and forwards different types of data to different Kafka topics to achieve distributed processing of the verification service cluster. Carrier information, driver information, and vehicle information correspond to the basic information verification service topic. Step 6) Each verification service subscribes to the corresponding Kafka Topic, dedicated to handling similar data verifications, using distributed processing. Step 7) When the Kafka callback notification data arrives, each verification service notifies the OCR service to recognize the corresponding image type. Step 8) The OCR service identifies the data in the image and returns the results to the corresponding verification service. Step 9) The verification service performs the verification process based on the data and verification rules of the compliance verification model above. If the verification fails for the verification rules, the reason for the failure needs to be recorded.

[0018] Step 10) If the project verification is successful, retrieve the AuditSuccFlag value from Redis based on the waybill (initialize it to 0 if it does not exist), set the bit corresponding to the AuditSuccFlag project to 1, and then write the result AuditSuccFlag back to Redis. Step 11) After the corresponding project verification is completed, retrieve the WaybillAuditFlag from Redis based on the corresponding waybill number (initialize it to 0 if it does not exist), set the bit of the WaybillAuditFlag corresponding to the project to 1, write the result WaybillAuditFlag back to Redis, and notify the audit summary service. Step 12) After receiving the project verification completion notification, the verification summary service retrieves the WaybillAuditFlag and AuditSuccFlag from Redis. It then performs a bitwise AND operation between WaybillAuditFlag and the configured AuditMode (WaybillAuditFlag & AuditMode). The result is compared with AuditMode. If they are identical, it indicates that all items in the waybill have been audited. Next, it checks if AuditSuccFlag & AuditMode equals AuditMode. If they are equal, the final audit result for the waybill is "audited"; otherwise, the audit fails. Step 13) The aggregation service writes the final audit results of the waybill back to the database. Step 14) Users can obtain the final review result through the tracking number. This invention provides an automated verification method for waybill compliance, standardizing and streamlining different types of audit services, making them more specialized and facilitating cluster deployment. Furthermore, by establishing a bitmask pattern, it allows for convenient and rapid determination of audit items and results through bitwise operations; the bitmask pattern is easily expandable. The entire invention combines OCR intelligent recognition and cross-validation of data, significantly reducing manual labor and achieving cost reduction and efficiency improvement.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0020] The above are merely specific embodiments of this application. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A verification method for an automatic verification system for waybill compliance, characterized in that: include: Step 1: Configure audit items. Configure 6 audit items and set all values ​​of bit 0 to bit 5 of AuditMode to 1; Step 2: Establish a probe service, masquerading as a MySQL slave database, to monitor and audit project data items; Step 3: Send the inspection and audit project data items to Kafka for decoupling; Step 4: The scheduling service subscribes to Kafka, and the scheduling service categorizes data according to business type, forwarding different types of data to the corresponding Kafka topics; Step 5: Set up four verification services: basic information verification, payment verification, receipt verification, and contract verification. Assign the six verification items to the corresponding verification services. The verification service subscribes to the corresponding Kafka topic and processes similar data verifications in a distributed manner. Step 6: The verification service performs verification based on the compliance verification model data and verification rules. If the verification fails, the reason for the failure is recorded. Step 7: After successful verification, retrieve the AuditSuccFlag value from Redis based on the waybill, set the corresponding bit of the AuditSuccFlag item to 1, and then write the result AuditSuccFlag back to Redis; Step 8: After the corresponding project verification is completed, retrieve the WaybillAuditFlag from Redis based on the corresponding waybill number, set the bit of the WaybillAuditFlag corresponding to the project to 1, write the result WaybillAuditFlag back to Redis, and notify the audit summary service; Step 9: After receiving the project verification completion notification, the verification summary service retrieves the WaybillAuditFlag and AuditSuccFlag of the waybill from Redis. It performs a bitwise AND operation between WaybillAuditFlag and the configured AuditMode, and compares the result value with AuditMode. If they are exactly the same, it means that all items of the waybill have been reviewed and completed. It then checks whether AuditSuccFlag & AuditMode are equal to AuditMode. If they are equal, the final review result of the waybill is that the review is passed; otherwise, the review fails. Step 10: The summary service writes the final audit results of the waybill back to the database.

2. The verification method of the automatic verification system for waybill compliance according to claim 1, characterized in that: The audit items include: carrier information, driver information, vehicle information, payment transaction information, receipt information, and contract information.

3. The verification method of the automatic verification system for waybill compliance according to claim 1, characterized in that: When the Kafka callback notification data arrives, the verification service notifies the OCR service to recognize the corresponding image type. The OCR service then identifies the data in the image and returns the result to the corresponding verification service.

4. The verification method of the automatic verification system for waybill compliance according to claim 1, characterized in that: The compliance verification model includes: a carrier information compliance verification model, including... Modeling data items: Name, ID number, ID card validity period, ID card photo (front side), ID card (back side); Verification rules: The name matches the name in the picture, the ID number matches the one in the picture, and the ID card is valid and not expired; Driver information compliance modeling data includes: Modeling data items: driver's name, driver's ID number, ID card validity period, address, issuing authority, driver's license name, driver's license ID number, permitted vehicle type, ID card photo, ID card back photo, driver's license photo; Verification rules: The name must match the name on the ID card and driver's license photos; the ID number must match the name on the ID card and driver's license photos; the ID card must not be expired; the age limit is 20-60 years old. The vehicle information compliance verification model includes: Modeling data items: license plate number, vehicle owner, road transport permit number, image of the front page of the vehicle registration certificate, image of the back page of the vehicle registration certificate, and image of the road transport permit; Verification rule: The license plate number must match the data on the vehicle registration certificate and road transport permit images; The payment transaction information compliance verification model includes: Modeling data items: payment transaction number, payer's name, payee's name, freight payment amount, and payment voucher image; Verification rules: The payer and payee must match the data on the payment voucher image; the payment amount on the payment voucher image must exceed the freight payment amount on the waybill. The compliance verification model for receipt information includes: Modeling data items: Waybill number, license plate number, transport tonnage, loading and unloading time, and return receipt image; Verification rules: The waybill number, license plate number, transport tonnage, loading and unloading time must be consistent with the data on the return receipt image; The contract information compliance verification model includes: Modeling data items: Contract number, Party A, Party B, Contract amount, Contract image; Verification rules: The contract number, Party A, Party B, and contract amount must match the data in the contract image.

Images