A smart waybill management system

Abstract

An intelligent waybill management system includes a data access layer, a data processing layer, a data storage layer, and a data service layer. The data access layer handles data access, including data in table, message, socket, and file types. The data processing layer performs different data processing steps according to the data type. The data storage layer stores the processed data. The data service layer handles data applications after processing and storage, allowing for unified data operations. This intelligent waybill management system can generate various economic and social benefits.

Description

Technical Field

[0001] This invention relates to the field of logistics and transportation technology, and specifically to an intelligent waybill management system. Background Technology

[0002] In the building materials industry, the shipment of raw materials or products typically requires settlement via waybills. A waybill is a crucial document recording cargo transportation information and settlement costs, containing essential information such as the shipper, consignee, cargo details, quantity, mode of transport, and fees. Currently, in most manufacturing enterprises, the printing, retrieval, sorting, storage, and statistical settlement of waybills still largely rely on manual processing. Manual processing suffers from high error rates, low efficiency, difficulties in archiving and management, and inconvenient retrieval. Summary of the Invention

[0003] In view of the technical defects and drawbacks existing in the prior art, embodiments of the present invention provide an intelligent waybill management system that overcomes or at least partially solves the above problems, the specific solution of which is as follows:

[0004] An intelligent waybill management system includes: a data access layer, a data processing layer, a data storage layer, and a data service layer. The overall data flow of the system is divided into: real-time data processing flow, online analysis data processing flow, and offline data processing flow. Each data processing flow passes through the data access layer, data processing layer, data storage layer, and data service layer in sequence.

[0005] Specifically, the real-time data processing flow includes:

[0006] The data access layer is used to collect source data through data acquisition and conversion programs, convert the data into a message string format that the platform can recognize, and then temporarily store the data in the data access layer message server.

[0007] The data processing layer is used to receive message information from the message server, parse the messages, and perform different processing actions on the messages according to the needs of business functions.

[0008] The data storage layer is used to transfer data results to the data storage layer according to their different types after the data processing layer has completed the data processing.

[0009] The data service layer is used to apply the data after the data processing and data storage steps are completed. All data operations are performed through the data service layer.

[0010] The online data processing flow includes:

[0011] The data access layer is used to archive and write offline loaded data with online data sources, thereby reducing repeated access to the source system, avoiding burden on the production environment, and improving the overall system performance. By periodically archiving offline and online data to the server, data consistency is maintained, so that data conflicts do not occur when using different data sources for data analysis.

[0012] The data storage layer is used to collect data periodically through the server interface using a data acquisition program, and then store the data in the data storage layer after formatting it through a preprocessing program.

[0013] The data processing layer is used to retrieve corresponding data from the data storage layer. Based on the needs of offline analysis scenarios, it uses big data technology to complete the operation processing process for different scenarios and stores the results of offline processing into the offline processing data model to meet the business requirements of online query.

[0014] The data service layer is used to apply the data after the data processing and data storage steps are completed. All data operations are performed through the data service layer.

[0015] The offline data processing flow includes:

[0016] The data access layer is used to directly push source data from the source system or actively collect source data through the collection program and then collect the data to the server.

[0017] The data storage layer is used to collect data periodically through the server interface using a data acquisition program, and then store the data in the data storage layer after formatting it through a preprocessing program.

[0018] The data processing layer is used to complete the operation processing process of different scenarios according to the needs of offline analysis scenarios through big data technology, including post-analysis, post-statistics, business research, etc., and store the results of offline processing into the offline processing data model;

[0019] Data Service Layer: After data processing and data storage are completed, the data application is carried out uniformly through the data service layer.

[0020] Furthermore, the different processing actions performed on messages according to the needs of business functions include real-time rule calculation, real-time key-value (KV) storage, and generation of manifest files;

[0021] in:

[0022] Real-time rule calculation involves continuously analyzing the data using a real-time calculation engine based on the business rules defined by the system after the system collects relevant data on waybills.

[0023] The real-time key-value (KV) storage design allows for fast writing and querying. The relevant information for each waybill is stored as a key-value pair in the Redis database. When new waybill information is generated or the status of an existing waybill is updated, the corresponding waybill information is pushed to the KV storage in real time, enabling fast data access and supporting real-time data analysis.

[0024] The manifest file is generated by the system based on the waybill data when the logistics waybill is completed or reaches a specific milestone. The manifest file supports CSV and Excel document formats. The manifest file contains the waybill number, status, transportation route, and cost information. The manifest file is automatically sent to the relevant requesting parties as needed. At the same time, the generated manifest file is archived to provide vouchers for later review, accounting and customer service, and to facilitate tracking and query.

[0025] Furthermore, the step of transferring the data results to the data storage layer according to their type after data processing at the data processing layer includes:

[0026] The calculation results of real-time rules are stored in the real-time processing model mart;

[0027] The calculation results of the real-time rules include the following:

[0028] Waybill status: includes the current status of the waybill, including accepted, in transit, completed, and abnormal;

[0029] Waybill priority: This includes the processing priority of waybills calculated based on business rules and real-time data;

[0030] Performance indicators: including key performance indicators calculated in real time based on transportation timeliness, cost, and service quality;

[0031] Customer feedback: This includes real-time collection of customer feedback on services and the resulting analytics.

[0032] Cost calculation: This includes freight calculations based on real-time data, which includes weight, volume, and distance.

[0033] The processing model marketplace is a central platform for storing and managing real-time data processing models and calculation results. The processing model marketplace contains a variety of data processing models for analyzing, calculating, and optimizing incoming real-time data.

[0034] Real-time key-value (KV) data is stored in the real-time processed data view;

[0035] The generated data for the real-time manifest file is stored in the structured HDFS (Distributed File System).

[0036] Furthermore, processing the model marketplace includes:

[0037] An optimization model, including a resource scheduling optimization model, is used to optimize the scheduling of vehicles and personnel based on transportation demand and available resources to improve overall efficiency.

[0038] Classification and clustering models, wherein the classification and clustering models include:

[0039] Customer segmentation models are used to classify customers using waybill data in order to enable precise marketing and personalized services;

[0040] The waybill clustering analysis model is used to cluster waybills with similar characteristics to help identify patterns and optimize transportation strategies.

[0041] The cost calculation model includes a dynamic pricing model, which is used to dynamically calculate freight costs based on transportation distance, time requirements, and market demand in order to maximize profits.

[0042] Furthermore, the process of completing tasks in different scenarios includes:

[0043] For the waybill creation scenario, the system obtains the transportation-related information input by the user, such as the shipper, consignee, type of goods, quantity, and mode of transportation, and automatically generates a waybill and assigns a unique waybill number.

[0044] For waybill tracking scenarios, GPS or RFID technology is used to regularly update waybill status, allowing users to view the transportation progress and location information of their goods on the platform;

[0045] For cost management scenarios, transportation costs are automatically calculated based on waybill information, and cost details are generated.

[0046] For abnormal handling scenarios, the system monitors the transportation status and automatically sends notifications to relevant personnel once an abnormality is detected, thereby activating the emergency response process.

[0047] For data analysis scenarios, we collect and store transportation data, and use big data analytics to generate reports and visualizations to help companies understand transportation efficiency, costs, and customer needs.

[0048] For customer service scenarios, we provide online services such as waybill tracking, shipping status updates, and complaint and suggestion collection;

[0049] For scenarios involving automatic push of logistics information, transportation status updates are automatically sent via SMS and email.

[0050] For inventory management scenarios, it integrates warehousing system data and automatically updates inventory information, enabling seamless integration between transportation and warehousing;

[0051] For compliance and regulatory scenarios, the system automatically checks whether the transportation process is compliant based on industry standards and relevant laws and regulations.

[0052] For multi-party collaboration scenarios, a shared platform containing waybill information is provided, allowing shippers, carriers, and consignees to access relevant waybill information in real time.

[0053] Furthermore, the intelligent waybill management system also includes a self-service printing system, which is used to identify vehicle information and corresponding waybill information through the vehicle weighing platform;

[0054] The system obtains the user's print request and, based on the print request, automatically prints the corresponding waybill or receipt information with one click using a printer. The print request includes the user's identity information and the waybill document to be printed.

[0055] Furthermore, the waybill management system also includes an automatic classification system. The automatic classification system is used to identify waybill documents placed by drivers on self-service terminals based on machine vision recognition technology, identify the content on the waybill documents, take high-definition photos of the waybill documents and archive the corresponding images, and open the corresponding type of storage cabinet based on the waybill content for drivers to self-classify and store the corresponding waybill documents.

[0056] Furthermore, the machine vision recognition technology includes text recognition technology or QR code detection technology.

[0057] Furthermore, based on text recognition technology, the system identifies the waybill documents placed by the driver on the self-service terminal and recognizes the content on the waybill documents, including:

[0058] Image preprocessing: The waybill image is preprocessed using image processing functions in the OpenCV library, including noise removal, grayscale conversion, and binarization to highlight text areas;

[0059] Region detection: Using a contour detection algorithm, text regions in the waybill image are detected and extracted from the image;

[0060] OCR Recognition: Based on the characteristics of the waybill and the features of the text, the TesseractOCR engine is used for text recognition;

[0061] Font and size adaptation: The Tesseract OCR engine is adapted to different fonts and sizes. If the text on the waybill uses a specific font and size, the model is trained to adapt to the specific font and size through Tesseract's font training function.

[0062] Text post-processing: The extracted text content is post-processed, including text correction, semantic analysis, and entity recognition.

[0063] Furthermore, based on text recognition technology, the system identifies the waybill documents placed by the driver on the self-service terminal and recognizes the content on the waybill documents, including:

[0064] QR code detection: Using image processing technology, a QR code detection algorithm is used to locate and detect the QR code area in the waybill image;

[0065] QR code decoding: Once a QR code area is detected, the corresponding QR code is decoded and converted into text information.

[0066] The present invention has the following beneficial effects:

[0067] (1) Improved work efficiency: The system enables self-service order printing, automatic identification and storage, and automatic statistics of return order data, which can greatly reduce the workload of internal staff and improve work efficiency. Drivers can also reduce queuing time and improve transportation efficiency through self-service printing and other methods.

[0068] (2) It can reduce economic losses caused by human error: The system can take pictures of each document and archive it at the same time as it identifies it. In this way, firstly, it can avoid economic disputes caused by damage or loss of paper documents, and secondly, it can avoid the problem of inconsistent settlement data due to human sorting errors.

[0069] (3) Social Benefits: The intelligent waybill management system, through data collection and aggregation from various stations and big data analysis, can gradually achieve information sharing and transparency of order logistics information from stations to the group and then to the entire industry. In the future, it will become a digital economy internet platform for the entire industry. Through this platform, logistics needs, traffic conditions, and market trends in different regions can be analyzed, providing reference for industry decision-making. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the system data architecture of the intelligent waybill management system provided in an embodiment of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the present invention, and not all of the 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.

[0072] like Figure 1As shown in the figure, an intelligent waybill management system provided by this invention includes a data access layer, a data processing layer, a data storage layer, and a data service layer. The data access layer is used to access data, including data in the form of data tables, messages, sockets, and files. The data processing layer is used to perform different data processing according to different data types. The data storage layer is used to store the processed data. The data service layer is used to apply the data after the data processing and data storage steps are completed, and all data operations are performed uniformly through the data service layer.

[0073] Specifically, the data includes real-time data, online data, and offline processing. Correspondingly, for different types of data, the overall data flow of the system includes real-time data processing flow, online analysis data processing flow, and offline data processing flow.

[0074] Specifically, the real-time data processing flow includes:

[0075] The data access layer is used to collect source data (including data tables, messages, etc.) through data acquisition and conversion programs, convert the data into a message string format that the platform can recognize, and then temporarily store the data in the data access layer message server.

[0076] The data processing layer is used to receive message information from the message server, parse the messages, and perform different processing actions on the messages according to the needs of business functions, such as: real-time rule calculation, real-time key-value storage, and generation of manifest files.

[0077] Among them, real-time rule calculation involves continuously analyzing the data after the system collects the relevant data of the waybill, according to the business rules defined by the system, using a real-time calculation engine. For example, when the system receives the generation or status change of a waybill, it collects data such as the shipper, consignee, cargo information, and transportation route in the waybill, and uses the Apache Kafka stream processing engine to continuously analyze the above data. Based on the analysis results, it uses real-time traffic data to optimize the route.

[0078] The real-time key-value (KV) storage design allows for fast writing and querying. The relevant information for each waybill is stored as a key-value pair in the Redis database. When new waybill information is generated or the status of an existing waybill is updated, the corresponding waybill information is pushed to the KV storage in real time. This data storage method enables fast data access and supports real-time data analysis.

[0079] The manifest file is generated by the system when a logistics waybill is completed or reaches a specific milestone. The manifest file supports CSV and Excel document formats and contains waybill number, status, transportation route, and cost information. The manifest file is automatically sent to relevant requesting parties, such as shippers, consignees, and transportation companies, as needed. At the same time, the generated manifest file is archived to provide vouchers for later review, accounting, and customer service, facilitating tracking and inquiry.

[0080] The data storage layer is used to transfer data results to the data storage layer according to their different types after the data processing layer has completed the data processing.

[0081] The calculation results of real-time rules are stored in the real-time processing model marketplace. In the intelligent waybill management system, the calculation results of real-time rules include the following:

[0082] Waybill status: This includes the current status of the waybill, such as accepted, in transit, completed, or abnormal.

[0083] Waybill Priority: Calculate the processing priority of waybills based on business rules and real-time data.

[0084] Performance indicators: These include key performance indicators (KPIs) that are calculated in real time, such as transportation timeliness, cost, and service quality.

[0085] Customer feedback: Real-time collection of customer feedback on services and generation of analytical results.

[0086] Cost calculation: The freight cost is calculated based on real-time data (such as weight, volume, distance, etc.).

[0087] A Real-time Processing Model Repository is a central platform for storing and managing real-time data processing models and their computational results. Such a repository typically contains multiple data processing models that can analyze, compute, and optimize incoming real-time data.

[0088] The intelligent waybill management system utilizes various data processing models to support real-time decision-making, optimize the transportation process, and improve service quality. These models include:

[0089] The optimization model includes:

[0090] Resource scheduling optimization model: used to optimize the scheduling of vehicles and personnel based on transportation demand and available resources to improve overall efficiency.

[0091] Classification and clustering models, including:

[0092] Customer segmentation model: Used to classify customers using waybill data in order to enable precise marketing and personalized services.

[0093] Waybill clustering analysis model: used to cluster waybills with similar characteristics to help identify patterns and optimize transportation strategies.

[0094] The cost calculation model includes:

[0095] Dynamic pricing model: used to dynamically calculate freight costs based on factors such as transportation distance, time requirements, and market demand in order to maximize profits;

[0096] Real-time key-value (KV) data is stored in real-time processed data views, such as business details, real-time alarms, and real-time metric statistics.

[0097] There are several reasons and benefits to storing real-time key-value (KV) data in a real-time processed data view:

[0098] Fast querying and access: Real-time data views provide rapid query responses, helping users and systems quickly obtain the information they need. The key-value (KV) storage structure makes it simple and efficient to quickly retrieve the corresponding value based on the key.

[0099] Real-time performance: Real-time data view supports real-time reading and processing of the latest data, which is crucial for application scenarios that require immediate response (such as transportation scheduling, customer inquiries, etc.).

[0100] Flexibility: KV storage offers a flexible data model that facilitates the storage of both structured and unstructured data, adapting to different types of data needs. This is particularly useful in the dynamic environment of transportation management.

[0101] Data Analysis and Processing: Key-value data stored in the real-time data view can be linked and analyzed with other relevant data to gain deeper insights. For example, analyzing waybill data can be used to classify customers in order to achieve precise marketing and personalized services.

[0102] Real-time manifest file generation and storage are performed in structured HDFS. Storing manifest files in structured HDFS (distributed file system) not only improves data management efficiency and availability, but also lays a solid foundation for subsequent analysis, processing, and big data applications. The following are the advantages of HDFS.

[0103] a. Data Management and Organization:

[0104] Structured storage: HDFS supports the storage of structured data, which can clearly organize the contents of manifest files, making them easy to manage and retrieve.

[0105] b. High availability:

[0106] HDFS provides high reliability and fault tolerance mechanisms, and historical waybill data can be stored redundantly to ensure that the data is not lost.

[0107] c. Extensibility:

[0108] HDFS supports horizontal scaling, enabling it to handle large-scale data storage needs. As the volume of real-time data increases, the need for manifest file storage can also be expanded accordingly.

[0109] d. High-throughput operation:

[0110] HDFS is designed for large-scale data transfers, thus offering high throughput for both data reading and writing, making it suitable for storing real-time updated manifest files.

[0111] e. Data Processing and Analysis:

[0112] Storing manifest files in HDFS allows for easy integration with big data processing tools such as Hadoop and Spark, supporting complex analysis and processing of the data in the manifest files.

[0113] f. Supports multiple data formats:

[0114] HDFS can flexibly support a variety of data formats (such as text, CSV, Parquet, etc.) to meet the needs of manifest files in different business scenarios.

[0115] g. Facilitates data auditing and tracking:

[0116] Structured storage simplifies the update record of audit checklist files, enabling the tracking of data change history and ensuring data compliance and auditability.

[0117] The data service layer is used to apply the data after the data processing and data storage steps are completed. All data operations are performed through the data service layer.

[0118] The online data processing flow includes:

[0119] The data access layer is used to archive and write offline loaded data with online data sources, thereby reducing repeated access to the source system, avoiding burden on the production environment, and improving the overall system performance. By periodically archiving offline and online data to the server, data consistency is maintained, so that data conflicts do not occur when using different data sources for data analysis.

[0120] The data storage layer is used to collect data periodically through the server interface using a data acquisition program, and then store the data in the data storage layer after formatting it through a preprocessing program.

[0121] The data processing layer is used to retrieve corresponding data from the data storage layer. Based on the needs of offline analysis scenarios, it uses big data technology to complete the operation processing process for different scenarios and stores the results of offline processing into the offline processing data model to meet the business requirements of online query.

[0122] The job processing procedures for different scenarios include:

[0123] Waybill creation scenario: When goods need to be transported, the system generates a waybill.

[0124] For waybill creation scenarios, the system obtains transportation-related information input by the user, such as shipper, consignee, type of goods, quantity, and mode of transport, and automatically generates a waybill and assigns a unique waybill number.

[0125] Waybill tracking scenario: Real-time monitoring of cargo transportation status.

[0126] For waybill tracking scenarios, the system uses technologies such as GPS and RFID to regularly update waybill status, allowing users to view the transportation progress and location of goods on the platform and achieve real-time tracking.

[0127] Cost management scenario: Managing transportation costs and settlement processes.

[0128] For cost management scenarios, it automatically calculates transportation costs based on waybill information and generates cost details, supports multiple payment methods, and simplifies the settlement process.

[0129] Exception handling scenario: Handling unexpected situations during transportation (such as delays, damage, etc.).

[0130] For abnormal handling scenarios, the system monitors the transportation status. Once an abnormality is detected, the system automatically sends a notification to relevant personnel to activate the emergency handling process and supports users to manually report problems.

[0131] Data analysis scenario, scenario description: Analyze historical transportation data to optimize operations.

[0132] For data analysis scenarios, we collect and store transportation data, and use big data analytics to generate reports and visualizations to help businesses understand transportation efficiency, costs, and customer needs.

[0133] Customer service scenario, scenario description: to improve customer experience and handle customer inquiries.

[0134] For customer service scenarios, online services such as waybill tracking, transportation status updates, and complaint and suggestion collection are provided to increase customer-business interaction and improve customer satisfaction.

[0135] Automatic logistics information push scenario: Provide relevant personnel with automatic notifications of transportation progress.

[0136] For scenarios involving automatic push notifications of logistics information, transportation status updates are automatically sent via SMS and email to ensure that all relevant personnel receive the information in a timely manner.

[0137] Inventory management scenario: Integration with warehouse management system to monitor inventory status in real time.

[0138] For inventory management scenarios, it integrates warehousing system data and automatically updates inventory information, enabling seamless integration between transportation and warehousing and reducing inventory costs.

[0139] Compliance and regulatory scenarios: Scenario description: Ensure transportation compliance with relevant regulations.

[0140] For compliance and regulatory scenarios, the system automatically checks whether the transportation process is compliant and whether necessary safety checks have been conducted, based on industry standards and relevant laws and regulations.

[0141] Multi-party collaboration scenario: Collaboration between different transportation participants.

[0142] For multi-party collaboration scenarios, a shared platform containing waybill information is provided, allowing shippers, carriers, and consignees to access relevant waybill information in real time, promoting information transparency and collaboration.

[0143] The above scenarios, through intelligent management and technological means, have improved the efficiency and transparency of the logistics industry, optimized transportation costs and processes, and enhanced customer experience.

[0144] The data service layer is used to apply the data after the data processing and data storage steps are completed. All data operations are performed through the data service layer.

[0145] The offline data processing flow includes:

[0146] The data access layer is used to directly push source data from the source system or actively collect source data through the collection program and then collect the data to the server.

[0147] The data storage layer is used to collect data periodically through the server interface using a data acquisition program, and then store the data in the data storage layer after formatting it through a preprocessing program.

[0148] The data processing layer is used to complete the operation processing process of different scenarios according to the needs of offline analysis scenarios through big data technology, including post-analysis, post-statistics, business research, etc., and store the results of offline processing into the offline processing data model;

[0149] Data Service Layer: After data processing and data storage are completed, the data application is carried out uniformly through the data service layer.

[0150] In some embodiments, the intelligent waybill management system further includes a self-service printing system, which is used to identify vehicle information and corresponding waybill information through a vehicle weighing platform;

[0151] The system obtains the user's print request and, based on the print request, automatically prints the corresponding waybill or receipt information with one click using a printer. The print request includes the user's identity information and the waybill document to be printed.

[0152] Self-service printing of waybills and receipts is a key function of the intelligent waybill management system. Traditional waybill management requires manual printing, often with only one person assigned to each station, resulting in significant time and manpower wastage. The intelligent waybill management system of this invention allows drivers to easily print waybill receipts at self-service terminals, eliminating the need for manual processing. The system identifies vehicle information and corresponding waybill information through a vehicle weighing platform. Drivers confirm the documents to be printed by swiping their ID card or scanning a QR code with their mobile phone, and the system automatically prints five copies of the waybill or receipt with a single click.

[0153] In some embodiments, the waybill management system further includes an automatic classification system. The automatic classification system is used to identify waybill documents placed by drivers on self-service terminals based on machine vision recognition technology, identify the content on the waybill documents, take high-definition photos of the waybill documents and archive the corresponding images, and open the corresponding type of storage cabinet based on the waybill content for drivers to self-classify and store the corresponding waybill documents.

[0154] Automatic sorting and storage is another important function of the intelligent waybill management system. Traditional waybill management requires manual sorting, organization, and archiving. However, the intelligent waybill management system of this invention allows drivers to place waybills on self-service terminals. Machine vision identifies the return order content and automatically opens the corresponding storage cabinet. Drivers then self-sort and store the waybills automatically, facilitating subsequent retrieval and management. This significantly reduces the workload of internal sorting personnel. Furthermore, during the machine vision sorting process, each document is photographed in high definition and archived. Even if a document is lost or damaged later, previous photos can be retrieved in the system for verification, greatly facilitating settlement work with project sites and logistics companies.

[0155] The recognition algorithm for waybill content utilizes computer vision and natural language processing technologies, combined with deep learning and traditional image processing algorithms, mainly including text recognition technology or QR code detection technology.

[0156] The specific process of text recognition includes:

[0157] Image preprocessing: The waybill image is preprocessed using image processing functions in the OpenCV library, including noise removal, grayscale conversion, binarization, etc., to highlight the text areas.

[0158] Region detection: Using contour detection algorithms, text regions in the waybill image are detected and extracted from the image.

[0159] OCR Recognition: Based on the characteristics of the waybill and the features of the text, the Tesseract OCR engine is used for text recognition.

[0160] Font and size adaptation: The Tesseract OCR engine is adapted for different fonts and sizes. If the text on the waybill uses a specific font and size, the model is trained using Tesseract's font training function to adapt to these specific fonts and sizes.

[0161] Text post-processing: The extracted text content can be post-processed, including text correction, semantic analysis, entity recognition, etc. For example, natural language processing technology can be used to perform semantic analysis on the extracted text to extract key information, such as the name of the goods, quantity, destination, etc.

[0162] This invention first uses the OpenCV library to read the waybill image and converts it to a grayscale image. Then, it uses the `image_to_string` function from the pytesseract library to recognize the text in the grayscale image, specifying the language as English ('eng'). Finally, it prints the recognition result. In this way, the Tesseract OCR engine is used to recognize textual information in the waybill, including sender and recipient information, cargo information, waybill number, etc.

[0163] The specific process for QR code detection includes:

[0164] QR code detection: Using image processing technology, a QR code detection algorithm is used to locate and detect the QR code area in the waybill image.

[0165] QR code decoding: Once a QR code area is detected, it needs to be decoded and converted into text information. Commonly used QR code decoding algorithms include ZBar and ZXing, which can decode QR codes and extract the information they contain.

[0166] This invention first uses the OpenCV library to read the waybill image and convert it into a grayscale image. Then, it uses the decode function of the ZBar library to decode the QR code in the image to obtain the information contained therein. Finally, it prints out the decoding result and displays the original image.

[0167] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent waybill management system, characterized in that, include: The system consists of a data access layer, a data processing layer, a data storage layer, and a data service layer. The overall data flow is divided into three categories: real-time data processing flow, online analysis data processing flow, and offline data processing flow. Each data processing flow passes through the data access layer, data processing layer, data storage layer, and data service layer in sequence. Specifically, the real-time data processing flow includes: The data access layer is used to collect source data through data acquisition and conversion programs, convert the data into a message string format that the platform can recognize, and then temporarily store the data in the data access layer message server. The data processing layer is used to receive message information from the message server, parse the messages, and perform different processing actions on the messages according to the needs of business functions. The data storage layer is used to transfer data results to the data storage layer according to their different types after the data processing layer has completed the data processing. The data service layer is used to apply the data after the data processing and data storage steps are completed. All data operations are performed through the data service layer. The online data processing flow includes: The data access layer is used to archive and write data by combining offline loaded data with online data sources; by periodically archiving offline and online data to the server, data consistency is maintained, so that data conflicts do not occur when using different data sources for data analysis; The data storage layer is used to collect data periodically through the server interface using a data acquisition program, and then store the data in the data storage layer after formatting it through a preprocessing program. The data processing layer is used to retrieve corresponding data from the data storage layer. Based on the needs of offline analysis scenarios, it uses big data technology to complete the operation processing process for different scenarios and stores the results of offline processing into the offline processing data model to meet the business requirements of online query. The data service layer is used to apply the data after the data processing and data storage steps are completed. All data operations are performed through the data service layer. The offline data processing flow includes: The data access layer is used to directly push source data from the source system or actively collect source data through the collection program and then collect the data to the server. The data storage layer is used to collect data periodically through the server interface using a data acquisition program, and then store the data in the data storage layer after formatting it through a preprocessing program. The data processing layer is used to complete the task processing process for different scenarios based on the needs of offline analysis using big data technology, and to store the results of offline processing into the offline processing data model. Data Service Layer: After data processing and data storage are completed, the data application is carried out uniformly through the data service layer.

2. The intelligent waybill management system according to claim 1, characterized in that, The process of performing different message processing actions according to business function needs includes real-time rule calculation, real-time key-value storage, and list file generation. in: Real-time rule calculation involves continuously analyzing the data using a real-time calculation engine based on the business rules defined by the system after the system collects relevant data for waybills. The real-time key-value (KV) storage design allows for fast writing and querying. The relevant information for each waybill is stored as a key-value pair in the Redis database. When new waybill information is generated or the status of an existing waybill is updated, the corresponding waybill information is pushed to the KV storage in real time, enabling fast data access and supporting real-time data analysis. The manifest file is generated by the system based on the waybill data when the logistics waybill is completed or reaches a specific milestone. The manifest file is automatically sent to the relevant requesting parties as needed, and the generated manifest file is archived to provide vouchers for later review, accounting and customer service, making it easy to track and query.

3. The intelligent waybill management system according to claim 2, characterized in that, The step of transferring the data results to the data storage layer according to their type after data processing at the data processing layer includes: The calculation results of real-time rules are stored in the real-time processing model mart; The calculation results of the real-time rules include the following: Waybill status: includes the current status of the waybill, including accepted, in transit, completed, and abnormal; Waybill priority: This includes the processing priority of waybills calculated based on business rules and real-time data; Performance indicators: including key performance indicators calculated in real time based on transportation timeliness, cost, and service quality; Customer feedback: This includes real-time collection of customer feedback on services and the resulting analytics. Cost calculation: including freight calculation results based on real-time data; The processing model marketplace is a central platform for storing and managing real-time data processing models and calculation results. The processing model marketplace contains a variety of data processing models for analyzing, calculating, and optimizing incoming real-time data. Real-time key-value (KV) data is stored in the real-time processed data view; The generated data for the real-time manifest file is stored in structured HDFS.

4. The intelligent waybill management system according to claim 3, characterized in that, The processing model marketplace includes: An optimization model, including a resource scheduling optimization model, is used to optimize the scheduling of vehicles and personnel based on transportation demand and available resources to improve overall efficiency. Classification and clustering models, wherein the classification and clustering models include: Customer segmentation models are used to classify customers using waybill data in order to enable precise marketing and personalized services; The waybill clustering analysis model is used to cluster waybills with similar characteristics to help identify patterns and optimize transportation strategies. The cost calculation model includes a dynamic pricing model, which is used to dynamically calculate freight costs based on transportation distance, time requirements, and market demand in order to maximize profits.

5. The intelligent waybill management system according to claim 1, characterized in that, The process of completing tasks in different scenarios includes: For the waybill creation scenario, the system obtains the transportation-related information input by the user, automatically generates a waybill, and assigns a unique waybill number; For waybill tracking scenarios, GPS or RFID technology is used to regularly update waybill status, allowing users to view the transportation progress and location information of their goods on the platform; For cost management scenarios, transportation costs are automatically calculated based on waybill information, and cost details are generated. For abnormal handling scenarios, the system monitors the transportation status and automatically sends notifications to relevant personnel once an abnormality is detected, thereby activating the emergency response process. For data analysis scenarios, we collect and store transportation data, and use big data analytics to generate reports and visualizations to help companies understand transportation efficiency, costs, and customer needs. For customer service scenarios, we provide online services such as waybill tracking, shipping status updates, and complaint and suggestion collection; For scenarios involving automatic push of logistics information, transportation status updates are automatically sent via SMS and email. For inventory management scenarios, it integrates warehousing system data and automatically updates inventory information, enabling seamless integration between transportation and warehousing; For compliance and regulatory scenarios, the system automatically checks whether the transportation process is compliant based on industry standards and relevant laws and regulations. For multi-party collaboration scenarios, a shared platform containing waybill information is provided, allowing shippers, carriers, and consignees to access relevant waybill information in real time.

6. The intelligent waybill management system according to claim 1, characterized in that, The intelligent waybill management system also includes a self-service printing system, which is used to identify vehicle information and corresponding waybill information through the vehicle weighing platform. The system obtains the user's print request and, based on the print request, automatically prints the corresponding waybill or receipt information with one click using a printer. The print request includes the user's identity information and the waybill document to be printed.

7. The intelligent waybill management system according to claim 1, characterized in that, The waybill management system also includes an automatic classification system. The automatic classification system is used to identify waybill documents placed by drivers on self-service terminals based on machine vision recognition technology, identify the content on the waybill documents, take high-definition photos of the waybill documents and archive the corresponding images, and open the corresponding type of storage cabinet based on the waybill content for drivers to self-classify and store the corresponding waybill documents.

8. The intelligent waybill management system according to claim 7, characterized in that, The machine vision recognition technology includes text recognition technology or QR code detection technology.

9. The intelligent waybill management system according to claim 8, characterized in that, The system uses text recognition technology to identify waybills placed by drivers on self-service terminals and recognizes the content of these waybills, including: Image preprocessing: The waybill image is preprocessed using image processing functions in the OpenCV library, including noise removal, grayscale conversion, and binarization to highlight text areas; Region detection: Using a contour detection algorithm, text regions in the waybill image are detected and extracted from the image; OCR Recognition: Based on the characteristics of the waybill and the features of the text, the TesseractOCR engine is used for text recognition; Font and size adaptation: The Tesseract OCR engine is adapted to different fonts and sizes. If the text on the waybill uses a specific font and size, the model is trained to adapt to the specific font and size through Tesseract's font training function. Text post-processing: The extracted text content is post-processed, including text correction, semantic analysis, and entity recognition.

10. The intelligent waybill management system according to claim 8, characterized in that, The system uses text recognition technology to identify waybills placed by drivers on self-service terminals and recognizes the content of these waybills, including: QR code detection: Using image processing technology, a QR code detection algorithm is used to locate and detect the QR code area in the waybill image; QR code decoding: Once a QR code area is detected, the corresponding QR code is decoded and converted into text information.

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