Intelligent waybill management system

Abstract

An intelligent waybill management system comprises a data access layer, a data processing layer, a data storage layer and a data service layer. Wherein the data access layer is used for the access of access data, and comprises data of a data table, a message, a socket and a file type; the data processing layer is used for carrying out different data processing according to different data types, the data storage layer is used for carrying out data storage on the processed data, and the data service layer is used for carrying out unified data operation on data application through the data service layer after the steps of data processing and data storage are completed. The intelligent waybill management system provided by the invention can generate economic benefits and social benefits in multiple aspects.

Description

Technical Field

[0001] The present invention relates to the field of logistics and transportation technology, and in particular to an intelligent waybill management system. Background Art

[0002] In the building materials industry, the shipment of raw materials or products usually needs to be settled through waybills. Waybills are an important document that records cargo transportation information and settlement costs. They contain important information such as the shipper, consignee, cargo information, quantity, transportation method, and costs. At present, in various production companies, the printing, query, sorting, storage, and statistical settlement of waybills are still mostly handled manually. Manual processing has problems such as high error rate, low processing efficiency, difficulty in archiving management, and inconvenient query. Summary of the invention

[0003] In view of the technical defects and technical drawbacks in the prior art, the embodiments of the present invention provide an intelligent waybill management system that overcomes the above problems or at least partially solves the above problems. The specific solution 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] The real-time data processing flows include:

[0006] The data access layer is used to collect source data through the data collection and conversion program, convert the data into a message string format that can be recognized by the platform, 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 message, and perform different processing actions on the message according to the needs of business functions;

[0008] The data storage layer is used to transfer the data results to the data storage layer according to different types after the data processing layer completes the data processing;

[0009] The data service layer is used to apply data after completing the data processing and data storage steps, and uniformly operate the data through the data service layer;

[0010] Among them, the processing flow for online analysis data includes:

[0011] The data access layer is used to archive and write offline loaded data by combining it with the online data source, so as to reduce repeated access to the source system, avoid burdening the production environment, and improve the overall performance of the system; by regularly archiving offline and online data to the server, data consistency is maintained, so that data conflicts will not occur when using different data sources for data analysis;

[0012] The data storage layer is used to periodically collect data through the server interface using a data collection program, and then format the data through a pre-processing program and store it in the data storage layer;

[0013] The data processing layer is used to obtain the corresponding data from the data storage layer, complete the job processing process of different scenarios through big data technology according to the needs of offline analysis scenarios, and store the offline processing result data in the offline processing data model to meet the business requirements of online queries;

[0014] The data service layer is used to apply data after completing the data processing and data storage steps, and uniformly operate the data through the data service layer;

[0015] Among them, the offline data processing flow includes:

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

[0017] The data storage layer is used to periodically collect data through the server interface using a data collection program, and then format the data through a pre-processing program and store it in the data storage layer;

[0018] The data processing layer is used to complete the job 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 offline processing result data in the offline processing data model;

[0019] Data service layer: used for data application after completing data processing and data storage steps, and uniformly performs data operations through the data service layer.

[0020] Furthermore, the message is processed in different ways according to the needs of the business functions, including real-time rule calculation, real-time KV (Key-Value) storage, and generation of a list file;

[0021] in:

[0022] Real-time rule calculation is to use the real-time calculation engine to continuously analyze the data after the system collects the relevant data of the waybill according to the business rules defined by the system;

[0023] The real-time KV storage design allows fast writing and querying. The relevant information of 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 when the logistics waybill is completed or reaches a specific milestone. The system generates a manifest file based on the waybill data. The manifest file supports CSV and Excel document formats. The manifest file content includes the waybill number, status, transportation route, and cost information. The manifest file is automatically sent to the relevant demander as required, and the generated manifest file is archived at the same time to provide vouchers for subsequent review, audit and customer service, which is convenient for tracking and inquiry.

[0025] Furthermore, after the data processing layer completes the data processing, the data results are sent to the data storage layer according to different types, including:

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

[0027] The calculation results of 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: 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: including real-time collection of customer feedback on services and the generated analysis results;

[0032] Cost calculation: including freight calculation results based on real-time data, including weight, volume and distance;

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

[0034] Real-time KV warehousing data is stored in the real-time processing data view;

[0035] The generated data of the real-time inventory file is stored in a structured HDFS (distributed file system).

[0036] Further, processing the model mart includes:

[0037] An optimization model, wherein the optimization model includes a resource scheduling optimization model for optimizing the scheduling of vehicles and personnel according to transportation demand and available resources to improve overall efficiency;

[0038] Classification and clustering model, the classification and clustering model includes:

[0039] Customer segmentation model, which is used to classify customers using waybill data in order to achieve precision marketing and personalized services;

[0040] Waybill clustering analysis model, which 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 for dynamically calculating freight costs based on transportation distance, timeliness requirements and market demand to maximize revenue.

[0042] Furthermore, the process of completing the job processing of different scenarios includes:

[0043] For waybill creation scenarios, obtain the transportation-related information entered by the user, such as shipper, consignee, cargo type, quantity, mode of transportation, etc., automatically generate a waybill, and assign a unique waybill number;

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

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

[0046] For abnormal handling scenarios, the transportation status is monitored and once an abnormal situation is found, a notification is automatically sent to relevant personnel to activate the emergency handling process;

[0047] For data analysis scenarios, we collect and store transportation data, and use big data analysis technology to generate reports and visualization charts to help companies understand transportation efficiency, costs, and customer needs;

[0048] For customer service scenarios, we provide online services such as waybill query, transportation status update, and complaint and suggestion collection;

[0049] For the scenario of automatic push of logistics information, the transportation status update is automatically sent via SMS and email;

[0050] For inventory management scenarios, it integrates warehouse system data and automatically updates inventory information, making transportation and warehousing seamlessly connected;

[0051] For compliance and regulatory scenarios, automatically check whether the transportation process is compliant according to industry standards and relevant laws and regulations;

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

[0053] Furthermore, 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;

[0054] Obtain the user's printing request, and based on the user's printing request, automatically print the corresponding waybill or receipt information through the printer with one click, wherein the printing 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, which is used to identify the waybill documents placed by the driver on the self-service terminal based on machine vision recognition technology, and identify the content on the waybill documents, take high-definition photos of the waybill documents and archive the corresponding pictures, and at the same time open the corresponding type of storage cabinet based on the waybill content, so that the driver can 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, the waybill document placed by the driver on the self-service terminal is identified based on the text recognition technology, and the contents on the waybill document are identified, including:

[0058] Image preprocessing: Use the image processing functions in the OpenCV library to preprocess the waybill image, including noise removal, grayscale, and binarization to highlight the text area;

[0059] Region detection: Use the contour detection algorithm to detect the text area in the waybill image and extract the text area from the image;

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

[0061] Font and size adaptation: Adapt the TesseractOCR engine to different fonts and sizes. If the text on the waybill uses a specific font and size, use Tesseract's font training function to train the model to adapt to the corresponding specific font and size.

[0062] Text post-processing: Post-processing is performed on the extracted text content, including text correction, semantic analysis, and entity recognition.

[0063] Furthermore, the waybill document placed by the driver on the self-service terminal is identified based on the text recognition technology, and the contents on the waybill document are identified, including:

[0064] QR code detection: Using image processing technology and QR code detection algorithms 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 can realize self-service printing of orders, automatic identification and storage, and automatic statistics of receipt data, which can greatly reduce the workload of internal page personnel and improve work efficiency. Drivers can also reduce waiting time in queues and improve transportation efficiency through self-service printing and other methods.

[0068] (2) It can reduce economic losses caused by human errors: The system can take photos of each document and archive it while identifying it. This method can avoid settlement disputes caused by damage or loss of paper documents, and avoid settlement data inconsistencies caused by human sorting errors.

[0069] (3) Social benefits: The intelligent waybill management system can gradually realize the information sharing and transparency of order logistics information from the site to the group and then to the entire industry through data collection and aggregation of data from each site, and through big data analysis. In the future, it will become a digital economy Internet platform for the entire industry. Through this platform, the logistics demand, traffic conditions, market trends, etc. in different regions can be analyzed to provide a reference for industry decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of the system data architecture of the intelligent waybill management system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] 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 present invention, not all 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.

[0072] like Figure 1As shown, an intelligent waybill management system provided by an embodiment of the present invention includes a data access layer, a data processing layer, a data storage layer and a data service layer; wherein the data access layer is used for accessing data, including data tables, messages, sockets and file types of data; 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, and the data service layer is used to apply the data after completing the data processing and data storage steps, and uniformly perform data operations through the data service layer.

[0073] Specifically, the data includes real-time data, online data and offline processing. Correspondingly, for different 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] The real-time data processing flows include:

[0075] The data access layer is used to collect source data (including data tables, messages, etc.) through the data collection and conversion program, convert the data into a message string format that can be recognized by the platform, 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 message, and perform different processing actions on the message according to the needs of business functions, such as real-time rule calculation, real-time KV storage, and list file generation;

[0077] Among them, real-time rule calculation is to use the real-time computing engine to continuously analyze the data after the system collects the waybill-related data according to the business rules defined by the system. For example, when the system receives the waybill generation or status change, it collects the data including the shipper, consignee, cargo information, transportation route, etc. in the waybill, and uses the Apache Kafka stream processing engine to continuously analyze the above data, and uses real-time traffic data to optimize the route according to the analysis results.

[0078] The real-time KV storage design allows fast writing and querying. The relevant information of 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 when the logistics waybill is completed or reaches a specific milestone. The system generates a manifest file based on the waybill data. The manifest file supports CSV and Excel document formats. The manifest file content includes the waybill number, status, transportation route, and cost information. The manifest file is automatically sent to relevant demanders, such as the shipper, consignee, and transportation company, according to demand. At the same time, the generated manifest file is archived to provide vouchers for subsequent review, accounting, and customer service, which is convenient for tracking and inquiry.

[0080] The data storage layer is used to transfer the data results to the data storage layer according to different types after the data processing layer completes the data processing;

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

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

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

[0084] Performance indicators: including real-time calculated key performance indicators (KPIs) such as transportation timeliness, cost, and service quality.

[0085] Customer feedback: Collect customer feedback on services in real time and generate analysis results.

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

[0087] The Real-time Processing Model Repository is a central platform for storing and managing real-time data processing models and their calculation results. This mart usually contains multiple data processing models that can analyze, calculate and optimize the incoming real-time data.

[0088] In the smart waybill management system, a variety of data processing models are used to support real-time decision-making, optimize the transportation process and improve service quality, including:

[0089] Optimization model, including:

[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 achieve precision 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] Cost calculation model, including:

[0095] Dynamic pricing model: used to dynamically calculate freight rates based on transportation distance, timeliness requirements, market demand, etc. to maximize revenue;

[0096] Real-time KV storage data is stored in real-time processing data views, such as business details, real-time alarms, real-time indicator statistics, etc.

[0097] There are several reasons and benefits for storing real-time KV (Key-Value) data in real-time processing data views:

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

[0099] Real-time: Real-time processing 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 provides a flexible data model that facilitates the storage of structured and unstructured data and adapts to different types of data requirements, which is very useful in the changing environment of transportation management.

[0101] Data analysis and processing: KV data stored in the real-time processing data view can be combined with other related data for analysis to gain deeper insights. For example, you can analyze waybill data to classify customers in order to achieve precision marketing and personalized services.

[0102] Real-time inventory files are generated and stored in the structured HDFS. Storing inventory files in the structured HDFS (distributed file system) can not only improve data management efficiency and availability, but also lay 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 and can clearly organize the contents of inventory files for easy management and retrieval.

[0105] b. High Availability:

[0106] HDFS provides high reliability and fault tolerance mechanisms, and can ensure that historical waybill data is not lost through redundant storage.

[0107] c. Scalability:

[0108] HDFS supports horizontal expansion and can handle large-scale data storage needs. As the amount of real-time data increases, the demand for inventory file storage can also expand accordingly.

[0109] d. High throughput operation:

[0110] HDFS is designed for large-scale data transfer, so it has high throughput when reading and writing data and is suitable for storing inventory files that are updated in real time.

[0111] e. Data processing and analysis:

[0112] Storing manifest files in HDFS can be easily integrated with big data processing tools (such as Hadoop, Spark, etc.), supporting complex analysis and processing of data in manifest files.

[0113] f.Support multiple data formats:

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

[0115] g. Facilitate data auditing and tracking:

[0116] Structured storage makes it easier to record updates to audit list files, and can track the history of data changes to ensure data compliance and auditability.

[0117] The data service layer is used to apply data after completing the data processing and data storage steps, and uniformly operate the data through the data service layer;

[0118] Among them, the processing flow for online analysis data includes:

[0119] The data access layer is used to archive and write offline loaded data by combining it with the online data source, so as to reduce repeated access to the source system, avoid burdening the production environment, and improve the overall performance of the system; by regularly archiving offline and online data to the server, data consistency is maintained, so that data conflicts will not occur when using different data sources for data analysis;

[0120] The data storage layer is used to periodically collect data through the server interface using a data collection program, and then format the data through a pre-processing program and store it in the data storage layer;

[0121] The data processing layer is used to obtain the corresponding data from the data storage layer, complete the job processing process of different scenarios through big data technology according to the needs of offline analysis scenarios, and store the offline processing result data in the offline processing data model to meet the business requirements of online queries;

[0122] The job processing process for different scenarios includes:

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

[0124] For the waybill creation scenario, obtain the transportation-related information entered by the user, such as shipper, consignee, cargo type, quantity, transportation method, etc., automatically generate the waybill, and assign a unique waybill number.

[0125] Waybill tracking scenario, scenario description: real-time monitoring of cargo transportation status.

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

[0127] Cost management scenario, scenario description: Manage transportation costs and settlement processes.

[0128] For cost management scenarios, the transportation costs are automatically calculated based on the waybill information and the cost details are generated. It can support multiple payment methods and simplify the settlement process.

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

[0130] For exception handling scenarios, the transportation status is monitored. Once an abnormal situation is found, the system automatically sends a notification to the 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, transportation data is collected and stored, and big data analysis technology is used to generate reports and visual charts to help companies understand transportation efficiency, costs and customer needs.

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

[0134] For customer service scenarios, we provide online services such as waybill inquiry, transportation status update, complaint and suggestion collection, etc. to increase the interaction between customers and enterprises and improve customer satisfaction.

[0135] Scenario for automatic push of logistics information. Scenario description: Provide automatic notification of transportation progress to relevant personnel.

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

[0137] Inventory management scenario, scenario description: Integrate with the warehouse management system to understand the inventory status in real time.

[0138] For inventory management scenarios, the warehouse system data is integrated and inventory information is automatically updated to achieve seamless connection between transportation and warehousing, thereby reducing inventory costs.

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

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

[0141] Multi-party collaboration scenario,Scenario description: Collaboration between different transport participants.

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

[0143] The above scenarios improve the efficiency and transparency of the logistics industry, optimize transportation costs and processes, and enhance customer experience through intelligent management and technical means.

[0144] The data service layer is used to apply data after completing the data processing and data storage steps, and uniformly operate the data through the data service layer;

[0145] Among them, the offline data processing flow includes:

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

[0147] The data storage layer is used to periodically collect data through the server interface using a data collection program, and then format the data through a pre-processing program and store it in the data storage layer;

[0148] The data processing layer is used to complete the job 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 offline processing result data in the offline processing data model;

[0149] Data service layer: used for data application after completing data processing and data storage steps, and uniformly performs data operations 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] Obtain the user's printing request, and based on the user's printing request, automatically print the corresponding waybill or receipt information through the printer with one click, wherein the printing request includes the user's identity information and the waybill document to be printed.

[0152] Self-service printing of waybills is one of the important functions of the intelligent waybill management system. The traditional waybill management method requires manual printing of waybills, and often only one person is assigned to a station to print waybills, which consumes a lot of queuing time and human resources. The intelligent waybill management system of the present invention can allow drivers to easily print waybills on self-service terminals through self-service printing, without waiting for manual processing. The system identifies vehicle information and corresponding waybill information through the vehicle weighing platform. The driver confirms the documents to be printed by swiping his ID card or scanning the code with his mobile phone, and automatically prints the five-way bill or receipt information with one click.

[0153] In some embodiments, the waybill management system also includes an automatic classification system, which is used to identify the waybill documents placed by the driver on the self-service terminal based on machine vision recognition technology, and identify the content on the waybill documents, take high-definition photos of the waybill documents and archive the corresponding pictures, and at the same time open the corresponding type of storage cabinet based on the waybill content, so that the driver can self-classify and store the corresponding waybill documents.

[0154] Automatic classification and storage is another important function of the intelligent waybill management system. The traditional waybill management method requires manual classification, sorting, and archiving of waybills, while the intelligent waybill management system of the present invention can allow the driver to place the waybill on the self-service terminal, identify the content of the return slip through machine vision, and automatically open the corresponding storage cabinet. Through the driver's self-service classification and storage, the waybill is automatically classified and stored, which is convenient for subsequent query and management. This method greatly reduces the workload of the internal page sorting personnel, and during the sorting process of machine vision recognition, each document will be photographed and archived in high definition. Even if the document is lost or damaged later, the previous photos can be queried in the system for verification, which greatly facilitates the settlement work with the project site and logistics company.

[0155] Among them, the recognition algorithm for waybill content utilizes computer vision and natural language processing technology, 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: Use the image processing functions in the OpenCV library to preprocess the waybill image, including noise removal, grayscale, binarization, etc., to highlight the text area.

[0158] Region detection: Use the contour detection algorithm to detect the text area in the waybill image and extract the text area 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: Adapt the Tesseract OCR engine to different fonts and sizes. If the text on the waybill uses a specific font and size, use Tesseract's font training function to train the model 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] The present invention first uses the OpenCV library to read the waybill image and converts it into a grayscale image, then uses the image_to_string function of the pytesseract library to recognize the text in the grayscale image, specifies the language as English ('eng'), and finally prints out the recognition result. In this way, the Tesseract OCR engine is used to recognize the text information in the waybill, including the consignee and consignor information, cargo information, waybill number, etc.

[0163] The specific process of 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 the QR code area is detected, the QR code needs to be decoded and converted into text information. Commonly used QR code decoding algorithms include ZBar, ZXing, etc., which can decode the QR code and extract the information contained in it.

[0166] The present invention first uses the OpenCV library to read the waybill image and converts it into a grayscale image, then uses the decode function of the ZBar library to decode the QR code in the image to obtain the information contained therein, and finally 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 principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent waybill management system, characterized in that: include: Data access layer, data processing layer, data storage layer and 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; The real-time data processing flows include: The data access layer is used to collect source data through the data collection and conversion program, convert the data into a message string format that can be recognized by the platform, 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 message, and perform different processing actions on the message according to the needs of business functions; The data storage layer is used to transfer the data results to the data storage layer according to different types after the data processing layer completes the data processing; The data service layer is used to apply data after completing the data processing and data storage steps, and uniformly operate the data through the data service layer; Among them, the processing flow for online analysis data includes: The data access layer is used to archive and write offline loaded data by combining it with the online data source; by regularly archiving offline and online data to the server, data consistency is maintained so that data conflicts will not occur when using different data sources for data analysis; The data storage layer is used to periodically collect data through the server interface using a data collection program, and then format the data through a pre-processing program and store it in the data storage layer; The data processing layer is used to obtain the corresponding data from the data storage layer, complete the job processing process of different scenarios through big data technology according to the needs of offline analysis scenarios, and store the offline processing result data in the offline processing data model to meet the business requirements of online queries; The data service layer is used to apply data after completing the data processing and data storage steps, and uniformly operate the data through the data service layer; Among them, the offline data processing flow includes: The data access layer is used to collect source data directly from the source system or actively collect source data through the collection program and collect the data on the server; The data storage layer is used to periodically collect data through the server interface using a data collection program, and then format the data through a pre-processing program and store it in the data storage layer; The data processing layer is used to complete the job processing process of different scenarios according to the needs of offline analysis scenarios through big data technology, and store the offline processing result data in the offline processing data model; Data service layer: used for data application after completing data processing and data storage steps, and uniformly performs data operations through the data service layer.

2. The intelligent waybill management system according to claim 1, characterized in that: The message is processed in different ways according to the needs of business functions, including real-time rule calculation, real-time KV storage and list file generation; in: Real-time rule calculation is to use the real-time calculation engine to continuously analyze the data after the system collects the relevant data of the waybill according to the business rules defined by the system; The real-time KV storage design allows fast writing and querying. The relevant information of 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 when the logistics waybill is completed or reaches a specific milestone. The system generates a manifest file based on the waybill data, and automatically sends the manifest file to the relevant demander according to demand. At the same time, the generated manifest file is archived to provide evidence for subsequent review, audit and customer service, which is convenient for tracking and inquiry.

3. The intelligent waybill management system according to claim 2, characterized in that: After the data processing layer completes the data processing, the data results are sent to the data storage layer according to different types, including: The calculation results of real-time rules are stored in the real-time processing model mart; The calculation results of real-time rules include the following: Waybill status: includes the current status of the waybill, including accepted, in transit, completed, and abnormal; Waybill priority: 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: including real-time collection of customer feedback on services and the generated analysis results; Cost calculation: including freight calculation results based on real-time data; The processing model mart is a central platform for storing and managing real-time data processing models and calculation results. The processing model mart contains a variety of data processing models for analyzing, calculating and optimizing incoming real-time data. Real-time KV warehousing data is stored in the real-time processing data view; The generated data of the real-time inventory file is stored in a structured HDFS.

4. The intelligent waybill management system according to claim 3, characterized in that: Processing model mart includes: An optimization model, wherein the optimization model includes a resource scheduling optimization model for optimizing the scheduling of vehicles and personnel according to transportation demand and available resources to improve overall efficiency; Classification and clustering model, the classification and clustering model includes: Customer segmentation model, which is used to classify customers using waybill data in order to achieve precision marketing and personalized services; Waybill clustering analysis model, which 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 for dynamically calculating freight costs based on transportation distance, timeliness requirements and market demand to maximize revenue.

5. The intelligent waybill management system according to claim 1, characterized in that: The process of completing the job processing of different scenarios includes: For waybill creation scenarios, obtain the transport-related information entered by the user, automatically generate a waybill, and assign a unique waybill number; For waybill tracking scenarios, the waybill status is regularly updated through GPS or RFID technology, allowing users to view the transportation progress and location information of the goods on the platform; For cost management scenarios, the transportation costs are automatically calculated based on the waybill information and the cost details are generated; For abnormal handling scenarios, the transportation status is monitored and once an abnormal situation is found, a notification is automatically sent to relevant personnel to activate the emergency handling process; For data analysis scenarios, we collect and store transportation data, and use big data analysis technology to generate reports and visualization charts to help companies understand transportation efficiency, costs, and customer needs; For customer service scenarios, we provide online services such as waybill query, transportation status update, and complaint and suggestion collection; For the scenario of automatic push of logistics information, the transportation status update is automatically sent via SMS and email; For inventory management scenarios, it integrates warehouse system data and automatically updates inventory information, making transportation and warehousing seamlessly connected; For compliance and regulatory scenarios, automatically check whether the transportation process is compliant according to industry standards and relevant laws and regulations; For multi-party collaboration scenarios, a shared platform containing waybill information is provided for 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; Obtain the user's printing request, and based on the user's printing request, automatically print the corresponding waybill or receipt information through the printer with one click, wherein the printing 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, which is used to identify the waybill documents placed by the driver on the self-service terminal based on machine vision recognition technology, and identify the content on the waybill documents, take high-definition photos of the waybill documents and archive the corresponding pictures, and at the same time open the corresponding type of storage cabinet based on the waybill content, so that the driver can 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: Based on text recognition technology, the waybill document placed by the driver on the self-service terminal is identified, and the contents on the waybill document are identified, including: Image preprocessing: Use the image processing functions in the OpenCV library to preprocess the waybill image, including noise removal, grayscale, and binarization to highlight the text area; Region detection: Use the contour detection algorithm to detect the text area in the waybill image and extract the text area from the image; OCR recognition: Based on the characteristics of the waybill and the text, the Tesseract OCR engine is used for text recognition; Font and size adaptation: Adapt the TesseractOCR engine to different fonts and sizes. If the text on the waybill uses a specific font and size, use Tesseract's font training function to train the model to adapt to the corresponding specific font and size. Text post-processing: Post-processing is performed on the extracted text content, including text correction, semantic analysis, and entity recognition.

10. The intelligent waybill management system according to claim 8, characterized in that: Based on text recognition technology, the waybill document placed by the driver on the self-service terminal is identified, and the contents on the waybill document are identified, including: QR code detection: Using image processing technology and QR code detection algorithms 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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