An international logistics full-link visualization system and method thereof

Abstract

The application relates to the technical field of logistics, in particular to an international logistics full-link visualization system and method thereof, which comprises the following steps: S1, obtaining original logistics data from a plurality of external logistics systems through a plurality of heterogeneous API adapters of a server terminal, and receiving sensing data from physical sensors through an Internet of Things protocol interface; S2, associating and fusing the original logistics data and the sensing data based on a logistics identifier through an event flow processing engine to generate a standardized logistics event sequence; S3, predicting a dynamic ETA of goods based on the logistics event sequence through a machine learning model; S4, generating a transportation path optimization suggestion according to a deviation between a predicted arrival time and a planned time; and S5, generating a data interface for visual display based on the logistics event sequence, the prediction result and the optimization suggestion.

Description

Technical Field

[0001] This application relates to the field of international logistics technology, and in particular to an international logistics end-to-end visualization system and method. Background Technology

[0002] The current globalization of trade has made the transportation distance and delivery logistics structure of cross-border goods more complex. In this context, tracking the progress of logistics orders can help enterprises better manage the goods transmission chain and facilitate logistics users to view the real-time progress of goods transmission. This allows logistics users to accurately understand the delivery time, thereby ensuring their trust in the enterprise's goods logistics chain. Currently, mature products that can achieve full-chain path tracking and visibility of links in cross-border trade are still scarce in the market. Therefore, this application proposes an international logistics full-chain visualization system and method to solve this technical problem. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the purpose of this application is to provide an international logistics end-to-end visualization system and method to address the technical problems mentioned in the background section.

[0004] The above-mentioned objective of this application is achieved through the following technical solution: an international logistics end-to-end visualization system, including a server terminal and a user terminal that communicates with the server terminal; The server terminal includes a data access module, a timeliness prediction module, and an optimization suggestion module; The data access module is configured to acquire raw logistics data from multiple external logistics systems through multiple heterogeneous application programming interface (API) adapters, and receive sensing data from physical sensors through an Internet of Things (IoT) protocol interface. The processing and fusion module is used to communicate with the data access module and is configured to run an event stream processing engine to associate and fuse the original logistics data and the sensor data based on logistics identifiers, and generate a standardized logistics event sequence that conforms to a unified data model. The time-of-arrival prediction module is used to communicate with the processing and fusion module, and is configured to predict the arrival time of goods and dynamically update the estimated arrival time (ETA) based on the logistics event sequence, historical transportation data and real-time external information through a machine learning model. The optimization suggestion module is communicatively connected to the timeliness prediction module and is configured to automatically generate transportation route optimization suggestions based on the deviation between the predicted arrival time and the planned time output by the timeliness prediction module. The application service module is used to communicate with the processing and fusion module, the timeliness prediction module, and the optimization suggestion module, and is configured to generate a data interface for visualization based on the logistics event sequence, prediction results, and optimization suggestions. The user terminal includes an integrated display screen, configured to obtain data from the application service module and render a graphical user interface on the integrated display screen to display end-to-end status information.

[0005] By adopting the above technical solution, this application realizes intelligent monitoring, prediction and decision support for the entire cross-border logistics process through a server terminal consisting of a data access module, a processing and fusion module, a timeliness prediction module, an optimization suggestion module and an application service module, and a user terminal equipped with a comprehensive display screen.

[0006] The data access module integrates multi-source logistics and sensor data through heterogeneous APIs and IoT interfaces; the processing and fusion module performs data association and standardization based on logistics identifiers to generate a unified logistics event sequence; the timeliness prediction module uses machine learning models to dynamically predict and update the estimated arrival time of goods based on event sequences, historical and real-time data; the optimization suggestion module automatically generates transportation route optimization schemes based on the deviation between the predicted time and the plan; the application service module generates a visual data interface based on the above information; and the user terminal renders and displays a visual interface of the entire link status through a comprehensive display screen. Therefore, this application achieves real-time transparency of the entire link status, transforming logistics management from passive response to pre-event prediction and in-event intervention through intelligent prediction and proactive optimization suggestions, significantly improving the reliability of transportation timeliness and the ability to cope with anomalies; at the same time, through an integrated visual interface, it greatly improves management efficiency and the level of intelligent decision-making, enhancing the overall resilience and operational efficiency of the supply chain.

[0007] Furthermore, the server terminal also includes an early warning module; the early warning module is configured to run a business rule engine to monitor the logistics event sequence, sensor data, and prediction deviations output by the timeliness prediction module, and automatically generate and push early warning information when the monitored data meets preset abnormal conditions; the early warning module is also integrated with a multi-party collaboration platform, configured to automatically convert early warning information into collaborative tasks, assign them to relevant responsible parties, and track the processing progress.

[0008] By adopting the above technical solution, this application enhances the proactive risk management and collaborative handling capabilities of the original system. The early warning module, through the operation of a business rules engine, monitors logistics event sequences, sensor data, and prediction deviations in real time. Once it identifies situations such as route deviation, time delays, or environmental anomalies that meet preset conditions, it automatically generates early warning information and pushes it to relevant personnel. Simultaneously, this module is deeply integrated with a multi-party collaboration platform, automatically converting early warning information into specific collaborative tasks, assigning them to relevant responsible parties such as cargo owners, carriers, and customs, and tracking task processing progress, thus forming a closed-loop management process of "monitoring-early warning-assignment-tracking." Its beneficial effects are: it achieves a fundamental shift in international logistics risk management from post-event remediation to pre-event early warning and in-event collaborative intervention, significantly shortening anomaly response time; through automated task assignment and progress visualization, it improves multi-party collaboration efficiency, clarifies responsibility attribution, and reduces communication costs and operational delays; and it further strengthens the reliability, transparency, and overall operational resilience of the entire supply chain.

[0009] Furthermore, the server terminal also includes a blockchain evidence storage module, configured to perform blockchain evidence storage for key logistics events; the blockchain evidence storage module is also used to store key collaborative operation records and electronic vouchers generated by the multi-party collaboration platform.

[0010] By adopting the above technical solution, the blockchain evidence storage module in this application establishes a traceable digital trust mechanism for the entire logistics chain. By storing key logistics events such as pickup, customs declaration, transportation, and receipt, as well as operation records such as task assignment, confirmation, and electronic receipt generated in the collaborative platform, the operation of each link has a unique and reliable timestamp and digital fingerprint.

[0011] This not only effectively prevents disputes such as document forgery, information tampering, or shirking of responsibility, but also provides all parties with verifiable evidence of authenticity, greatly enhancing the foundation of mutual trust and cooperation efficiency among cross-border logistics participants. Furthermore, the event stream processing engine is specifically configured to infer and supplement missing logistics node events through state machine logic; the processing and fusion module is also configured to, based on the logistics event sequence, fuse external risk data sources, construct and output supply chain risk indicators, which are used to generate a risk dashboard view in the graphical user interface.

[0012] By adopting the above technical solutions, this application significantly improves the system's capabilities in data continuity and macro-risk insight through the event stream processing engine and processing and fusion module. The event stream processing engine, through state machine logic, can intelligently infer and complete logistics events based on context and preset rules when some node data is delayed or missing, ensuring the continuity and real-time nature of the visualized trajectory, and effectively overcoming the information gap problem caused by poor system integration or missing information reporting.

[0013] Meanwhile, the processing and fusion module integrates external risk data such as ports, shipping routes, and weather to construct supply chain risk indicators such as congestion index and regional risk level, which are then centrally presented in the form of a dashboard in the graphical interface. This allows managers to not only track the status of specific goods, but also to grasp the vulnerable links and potential threats of the entire supply chain in real time.

[0014] Furthermore, the external logistics system includes at least two of the following: shipping company system, airline system, port terminal operating system, and customs clearance system.

[0015] By adopting the above technical solution and supporting data acquisition from at least two types of heterogeneous external systems, such as shipping companies, airlines, port operations, and customs clearance, the system can break down information barriers between different logistics links and automatically aggregate multi-dimensional information such as sea freight, air freight, port operations, and customs clearance status. This allows users to obtain complete logistics status across core nodes such as transportation, loading and unloading, and customs clearance within a unified interface without having to log into multiple independent platforms. This significantly improves the efficiency and completeness of information acquisition, provides a solid data foundation for end-to-end full-link visual management, and enhances the system's applicability and practicality in real-world complex logistics environments.

[0016] Furthermore, the multi-party collaboration platform supports at least two parties among cargo owners, carriers, customs, and warehouses to conduct online task collaboration, document sharing, and electronic signatures.

[0017] By adopting the above technical solution, and supporting at least two parties such as cargo owners, carriers, customs, and warehouses to collaborate online, share documents in real time, and complete electronic signatures, the system transforms the traditional fragmented communication model relying on email, telephone, and paper-based delivery into a structured online process. This not only significantly reduces information transmission delays and errors, but more importantly, all collaborative operations are recorded by the system and can be linked with early warning and evidence storage modules, ensuring the standardization of work processes and traceability of responsibility. This significantly improves the overall efficiency, transparency, and reliability of the logistics chain.

[0018] Furthermore, the supply chain risk indicators include at least one of the following: global port congestion index, shipping route risk map, and seasonal anomaly statistics, and support user-defined risk thresholds and early warning rules.

[0019] By adopting the aforementioned technical solutions and incorporating macro- and dynamic supply chain risk indicators such as the global port congestion index, shipping route risk maps, and seasonal anomaly statistics into the analysis system, the system can reveal potential overall risks and recurring threats from multiple perspectives, including industry level, geographical dimension, and time period. Simultaneously, the system supports user-defined risk thresholds and early warning rules, enabling it to adapt to the differentiated needs of various cargo characteristics, enterprise risk preferences, and business scenarios, thus achieving personalized and refined risk management.

[0020] This not only helps users to perceive and quantify external threats to the supply chain in advance, but also transforms macro-risk data into specific, timely, and executable management instructions through a configurable early warning mechanism.

[0021] Furthermore, the user terminal can be any of the following: a desktop or laptop computer or a mobile smart terminal with dedicated client software installed; or a large display device for command and dispatch in the logistics center.

[0022] Furthermore, an international logistics end-to-end visualization method, applicable to any of the above technical solutions, includes the following steps: S1. Obtain raw logistics data from multiple external logistics systems through multiple heterogeneous API adapters on the server terminal, and receive sensing data from physical sensors through the Internet of Things protocol interface. S2. Using an event stream processing engine, the original logistics data and the sensor data are associated and fused based on logistics identifiers to generate a standardized logistics event sequence. S3. Based on the logistics event sequence, predict the dynamic ETA of the goods using a machine learning model; S4. Based on the deviation between the predicted arrival time and the planned time, generate transportation route optimization suggestions; S5. Based on the logistics event sequence, prediction results, and optimization suggestions, generate a data interface for visualization. S6. Run the business rules engine to monitor and generate early warnings for anomalies in logistics event sequences, sensor data and prediction deviations, and convert the early warnings into collaborative tasks for assignment and tracking. S7. Blockchain-based evidence storage for key logistics events and collaborative operation records; S8. Based on logistics event sequences and external risk data, construct supply chain risk indicators and generate a risk dashboard view; S9. Obtain data through user terminals and render and display the full-link status, collaborative task interface, and risk dashboard view.

[0023] By adopting the above technical solutions, this application fully realizes the digital mapping, intelligent analysis, and collaborative management of the entire international logistics chain. First, it automatically accesses multi-source logistics data and real-time sensor information through heterogeneous APIs and IoT interfaces, providing a data foundation for the system. Then, it correlates and merges data based on unified identifiers to form standardized logistics event sequences, solving the problem of information silos. Subsequently, it uses machine learning models to analyze the event sequences, dynamically predict cargo arrival times, and proactively generate optimization suggestions, realizing a shift from passive tracking to proactive prediction. Simultaneously, it runs a business rules engine to monitor the entire chain data flow in real time, automatically identify anomalies and trigger warnings, and transforms warnings into online collaborative tasks for assignment and tracking, forming a closed loop of "monitoring, warning, and handling." On this basis, it uses blockchain to store key logistics events and collaborative operations, establishing an immutable trust mechanism. Furthermore, it integrates external risk data to construct supply chain-level risk indicators, generating a global risk dashboard view to enhance macro-risk perception capabilities. Finally, all information is integrated and visualized on the user terminal through a unified data interface, providing users with a panoramic view covering real-time status, collaborative tasks, and risk situation. This approach, through a streamlined and intelligent process, systematically integrates the fragmented, delayed, and highly manual processes in traditional logistics management into a unified, real-time, and automated intelligent operation system. This significantly improves the transparency, reliability, collaborative efficiency, and risk management level of international logistics. In summary, this application offers the following beneficial technical effects: at the information level, it breaks down data silos, achieving full-process transparency; at the management level, it shifts from post-event response to pre-event prevention through proactive early warning mechanisms; at the operational level, it automates processes by replacing manual verification with intelligent inference; and at the trust level, it establishes an immutable and trustworthy mechanism through blockchain-based evidence storage. This technical solution ultimately transforms international logistics from a traditionally uncertain state into a transparent, trustworthy, and intelligent visualized closed-loop system, significantly improving the reliability, security, and operational efficiency of the supply chain. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall structure in the embodiment; Figure 2 This is a schematic diagram illustrating the process in the embodiment. Detailed Implementation

[0025] The present application will be further described in detail below with reference to the accompanying drawings.

[0026] Example, refer to Figure 1An international logistics end-to-end visualization system, comprising a server terminal and a user terminal that communicates with the server terminal; The server terminal includes a data access module, a timeliness prediction module, a processing and fusion module, and an optimization suggestion module; The data access module is configured to acquire raw logistics data from multiple external logistics systems through multiple heterogeneous application programming interface (API) adapters, and receive sensing data from physical sensors through the Internet of Things (IoT) protocol interface. The processing and fusion module is used to communicate with the data access module and is configured to run the event stream processing engine to associate and fuse raw logistics data and sensor data based on logistics identifiers, generating a standardized logistics event sequence that conforms to a unified data model. The time-of-arrival prediction module communicates with the processing and fusion module and is configured to predict the arrival time of goods and dynamically update the estimated arrival time (ETA) based on logistics event sequences, historical transportation data, and real-time external information using a machine learning model. The optimization suggestion module communicates with the timeliness prediction module and is configured to automatically generate transportation route optimization suggestions based on the deviation between the predicted arrival time and the planned time output by the timeliness prediction module. The application service module is used to communicate and connect with the processing and fusion module, the timeliness prediction module, and the optimization suggestion module. It is configured to generate a data interface for visualization based on logistics event sequences, prediction results, and optimization suggestions. The user terminal includes an integrated display screen, configured to acquire data from the application service module and render a graphical user interface on the integrated display screen to display end-to-end status information.

[0027] This application achieves intelligent monitoring, prediction, and decision support for the entire cross-border logistics process through a server terminal consisting of a data access module, a processing and fusion module, a timeliness prediction module, an optimization suggestion module, and an application service module, as well as a user terminal equipped with a comprehensive display screen.

[0028] The data access module integrates multi-source logistics and sensor data through heterogeneous APIs and IoT interfaces; the processing and fusion module performs data association and standardization based on logistics identifiers to generate a unified logistics event sequence; the timeliness prediction module uses machine learning models to dynamically predict and update the estimated arrival time of goods based on event sequences, historical and real-time data; the optimization suggestion module automatically generates transportation route optimization schemes based on the deviation between the predicted time and the plan; the application service module generates a visual data interface based on the above information; and the user terminal renders and displays a visual interface of the entire link status through a comprehensive display screen. Therefore, this application achieves real-time transparency of the entire link status, transforming logistics management from passive response to pre-event prediction and in-event intervention through intelligent prediction and proactive optimization suggestions, significantly improving the reliability of transportation timeliness and the ability to cope with anomalies; at the same time, through an integrated visual interface, it greatly improves management efficiency and the level of intelligent decision-making, enhancing the overall resilience and operational efficiency of the supply chain.

[0029] In this embodiment, the server terminal also includes an early warning module, which is configured to run a business rule engine to monitor the prediction deviations of logistics event sequences, sensor data, and timeliness prediction modules. When the monitored data meets preset abnormal conditions, it automatically generates early warning information and pushes it. The early warning module is also integrated with a multi-party collaboration platform and configured to automatically convert early warning information into collaborative tasks, assign them to relevant responsible parties, and track the processing progress.

[0030] This application enhances the proactive risk management and collaborative response capabilities of the existing system. Through an early warning module that operates a business rules engine, it monitors logistics event sequences, sensor data, and prediction deviations in real time. Once pre-defined conditions are met, such as route deviations, time delays, or environmental anomalies, an early warning is automatically generated and pushed to relevant personnel. Simultaneously, this module is deeply integrated with a multi-party collaboration platform, automatically transforming early warning information into specific collaborative tasks, assigning them to cargo owners, carriers, customs, and other responsible parties, and tracking task progress, thus forming a closed-loop management process of "monitoring-early warning-assignment-tracking." Its beneficial effects include: achieving a fundamental shift in international logistics risk management from post-event remediation to pre-event early warning and in-event collaborative intervention, significantly shortening anomaly response time; improving multi-party collaboration efficiency through automated task assignment and progress visualization, clarifying responsibility, and reducing communication costs and operational delays; and further strengthening the reliability, transparency, and overall operational resilience of the entire supply chain.

[0031] The server terminal also includes a blockchain evidence storage module, configured to store key logistics events on the blockchain; the blockchain evidence storage module is also used to store key collaborative operation records and electronic certificates generated by the multi-party collaborative platform.

[0032] In this application, the blockchain evidence storage module establishes a traceable digital trust mechanism for the entire logistics chain. By storing key logistics events such as pickup, customs declaration, transportation, and receipt, as well as operation records such as task assignment, confirmation, and electronic receipt generated in the collaborative platform, the operation of each link has a unique and reliable timestamp and digital fingerprint.

[0033] This not only effectively prevents disputes such as document forgery, information tampering, or shirking of responsibility, but also provides all parties with verifiable evidence of authenticity, greatly enhancing the foundation of mutual trust and cooperation efficiency among cross-border logistics participants. The event stream processing engine is specifically configured to infer and supplement missing logistics node events through state machine logic; the processing and fusion module is also configured to build and output supply chain risk indicators based on logistics event sequences and external risk data sources, which are used to generate risk dashboard views in the graphical user interface.

[0034] This application significantly improves the system's capabilities in data continuity and macro-risk insight through the event stream processing engine and processing and fusion module. The event stream processing engine, through state machine logic, can intelligently infer and complete logistics events based on context and preset rules when some node data is delayed or missing, ensuring the continuity and real-time nature of the visualized trajectory, and effectively overcoming the information gap problem caused by poor system integration or missing information reporting.

[0035] Meanwhile, the processing and fusion module integrates external risk data such as ports, shipping routes, and weather to construct supply chain risk indicators such as congestion index and regional risk level, which are then centrally presented in the form of a dashboard in the graphical interface. This allows managers to not only track the status of specific goods, but also to grasp the vulnerable links and potential threats of the entire supply chain in real time.

[0036] External logistics systems include at least two of the following: shipping company systems, airline systems, port terminal operating systems, and customs clearance systems.

[0037] By supporting data acquisition from at least two types of heterogeneous external systems, including shipping companies, airlines, port operations, and customs clearance, the system breaks down information barriers between different logistics links, automatically aggregating multi-dimensional information such as sea freight, air freight, port operations, and customs clearance status. This allows users to access complete logistics status across core nodes such as transportation, loading and unloading, and customs clearance within a unified interface without having to log into multiple independent platforms. This significantly improves the efficiency and completeness of information acquisition, providing a solid data foundation for end-to-end, full-link visual management and enhancing the system's applicability and practicality in complex real-world logistics environments.

[0038] The multi-party collaboration platform supports at least two parties, including cargo owners, carriers, customs, and warehouses, to conduct online task collaboration, document sharing, and electronic signatures.

[0039] By supporting online task collaboration, real-time document sharing, and electronic signature completion among at least two parties, including cargo owners, carriers, customs, and warehouses, the system transforms the traditional fragmented communication model relying on email, telephone, and paper-based delivery into a structured online process. This not only significantly reduces information transmission delays and errors, but more importantly, all collaborative operations are recorded by the system and can be linked with early warning and evidence storage modules, ensuring the standardization of operational processes and traceability of responsibility. This significantly improves the overall efficiency, transparency, and reliability of the logistics chain.

[0040] Supply chain risk indicators include at least one of the following: global port congestion index, shipping route risk map, and seasonal anomaly statistics, and support user-defined risk thresholds and early warning rules.

[0041] By incorporating macro- and dynamic supply chain risk indicators such as the global port congestion index, shipping route risk maps, and seasonal anomaly statistics into its analysis system, the system can reveal potential overall risks and recurring threats from multiple perspectives, including industry level, geographical dimension, and time period. Simultaneously, the system supports user-defined risk thresholds and early warning rules, enabling it to adapt to the differentiated needs of various cargo characteristics, enterprise risk preferences, and business scenarios, achieving personalized and refined risk management.

[0042] This not only helps users to perceive and quantify external threats to the supply chain in advance, but also transforms macro-risk data into specific, timely, and executable management instructions through a configurable early warning mechanism.

[0043] The user terminal can be any of the following: a desktop or laptop computer or a mobile smart terminal with dedicated client software installed; or a large display device used for command and dispatch in a logistics center.

[0044] A method for visualizing the entire international logistics chain, applicable to any of the above-mentioned technical solutions, includes the following steps: S1. Obtain raw logistics data from multiple external logistics systems through multiple heterogeneous API adapters on the server terminal, and receive sensing data from physical sensors through the Internet of Things protocol interface. S2. Through the event stream processing engine, the raw logistics data and sensor data are associated and fused based on logistics identifiers to generate a standardized logistics event sequence. S3. Based on logistics event sequences, predict the dynamic ETA of goods through machine learning models; S4. Based on the deviation between the predicted arrival time and the planned time, generate transportation route optimization suggestions; S5. Based on logistics event sequences, prediction results, and optimization suggestions, generate a data interface for visualization. S6. Run the business rules engine to monitor and generate early warnings for anomalies in logistics event sequences, sensor data and prediction deviations, and convert the early warnings into collaborative tasks for assignment and tracking. S7. Blockchain-based evidence storage for key logistics events and collaborative operation records; S8. Based on logistics event sequences and external risk data, construct supply chain risk indicators and generate a risk dashboard view; S9. Obtain data through user terminals and render and display the full-link status, collaborative task interface, and risk dashboard view.

[0045] This application fully realizes the digital mapping, intelligent analysis, and collaborative management of the entire international logistics chain. First, it automatically accesses multi-source logistics data and real-time sensor information through heterogeneous APIs and IoT interfaces, providing a data foundation for the system. Then, it correlates and merges the data based on unified identifiers to form a standardized logistics event sequence. Subsequently, it uses machine learning models to analyze the event sequence, dynamically predict the arrival time of goods, and proactively generate optimization suggestions, realizing the transformation from passive tracking to proactive prediction. At the same time, it runs a business rule engine to monitor the entire chain data flow in real time, automatically identify anomalies and trigger early warnings, and transforms early warnings into online collaborative tasks for assignment and tracking, forming a closed loop of "monitoring-early warning-handling". On this basis, it uses blockchain to store key logistics events and collaborative operations, establishing an immutable trust mechanism. Furthermore, it integrates external risk data to construct supply chain-level risk indicators, generating a global risk dashboard view to improve macro-risk perception capabilities. Finally, all information is integrated and visualized on the user terminal through a unified data interface, providing users with a panoramic view covering real-time status, collaborative tasks, and risk situation. This approach, through a streamlined and intelligent process, systematically integrates the fragmented, delayed, and highly manual processes in traditional logistics management into a unified, real-time, and automated intelligent operation system. This significantly improves the transparency, reliability, collaborative efficiency, and risk management level of international logistics. Specific implementation process: Taking a cold chain container transportation order from port A in China to port B in the world as an example, this paper illustrates the collaborative working process and implementation effect of each module of the system in a specific logistics scenario.

[0046] The data access module and processing and fusion module are initially put into operation. Once a shipping order is generated, the data access module automatically retrieves raw data from multiple external logistics systems through its configured heterogeneous API adapters: for example, it obtains the container's booking confirmation and vessel schedule information from the shipping company's business system; it obtains the container's arrival, storage location, and actual loading time from the Terminal Operating System (TOS) of Port A; and it obtains the customs declaration application, review, and final release status of the shipment from the customs information system. Simultaneously, this module continuously receives real-time data streams from physical sensors attached to the container via an IoT interface supporting protocols such as MQTT, including the container's internal temperature (set to -18°C), humidity, door lock status, and precise GPS-based location information. This multi-source, heterogeneous raw data is transmitted in real-time to the processing and fusion module.

[0047] The processing and fusion module runs a high-performance event stream processing engine. This engine uses the unique identifier of this shipment—the container number—as the key index to perform real-time correlation, cleaning, and context fusion of the continuously flowing raw logistics data and sensor data.

[0048] For example, the engine intelligently matches and correlates "loaded on board" status updates from the shipping company's system, precise timestamps of "loading completed" from the TOS (Transportation System) at Port A, and data points from container sensors reporting "GPS coordinates moved to the departure channel" within the same time period. This process ultimately merges these data points to generate a standardized logistics event conforming to a predefined, unified data model: "Goods were loaded onto the ship at [specific time] at Port A, the ship has departed, and the current temperature inside the container is -17.8°C." Through this process, the system successfully transforms scattered, heterogeneous, and fragmented information into a series of standardized logistics events with clear timelines, unified structure, and explicit semantics, laying a high-quality data foundation for all subsequent intelligent analyses.

[0049] Based on the standardized logistics event sequence generated in the first phase, the system enters the intelligent analysis and decision support phase. The timeliness prediction module then activates, receiving continuously updated event sequences and integrating multi-dimensional external real-time information for comprehensive analysis. This module not only analyzes the container's current transportation location (e.g., having passed through the Suez Canal), but also obtains 72-hour storm warnings for the North Atlantic route and average berthing waiting times reported in real-time by Port B (e.g., 48 hours) through integrated external data interfaces, and retrieves past transportation timeliness records for the same route, similar seasons, and similar carriers from the historical database. Utilizing a built-in machine learning model (such as a gradient boosting decision tree algorithm), the module calculates and analyzes the aforementioned massive features, dynamically predicting that the container's estimated time of arrival (ETA) will be delayed by approximately 32 hours from the original plan, and continuously updates this prediction value as new data is injected.

[0050] Next, the optimization suggestion module operates based on the output of the timeliness prediction module. This module has built-in business rules; when it detects that the deviation between the predicted ETA and the original planned ETA exceeds a preset threshold, it automatically triggers the optimization analysis process. It comprehensively considers the current precise location of the vessel, available alternative routes, the capacity and operational efficiency of nearby backup ports, and the additional costs and time increments that different options may cause, generating a structured transportation route optimization suggestion after algorithmic weighing. For example: "Warning: Due to weather risks along the route and congestion at Port B, the current transport ETA is expected to be delayed by 32 hours. Recommended solution: Assess whether the vessel can conveniently transit at Port C and then use a rail link to reach its destination." This optimization suggestion is pushed to the user terminal interface of relevant management personnel in real time through the message interface generated by the application service module.

[0051] While providing prediction and optimization functions, the system continuously encapsulates information such as logistics event sequences, dynamic ETAs, and optimization suggestions into standardized data interfaces through the application service module, allowing for front-end visualization. More importantly, the early warning module continuously runs its embedded business rule engine in the background, providing 24 / 7 real-time monitoring of the entire data flow. This engine predefines a rich set of rules for judging abnormal situations. For example: "The continuous monitoring value of the temperature inside the cold chain cargo container exceeds the safety threshold for more than 30 minutes," or "The actual navigation trajectory of the ship deviates from the planned route by more than 20 nautical miles."

[0052] In this case, when the container temperature sensor data showed that the internal temperature had risen slowly to -15.2°C for unknown reasons and remained there for more than the set time, the business rules engine immediately recognized that the abnormal condition was met and automatically triggered a high-level alert. The system instantly generated an alert message containing detailed context information: "Container temperature abnormal! Current temperature -15.2°C, has exceeded the limit for 35 minutes, and there is a risk to the quality of the cargo. Current location: [longitude, latitude]." This alert was simultaneously sent to the shipper's mobile application, the carrier's dispatch center's monitoring screen, and the designated email address of the relevant insurance company through the integrated message push center.

[0053] The generation of an early warning is not the end of the process. The early warning module is deeply integrated with the multi-party collaborative platform of the system, which can automatically transform such structured early warning information into a traceable, executable, and clearly defined collaborative task. For example, the system automatically creates a task: "Emergency Handling of Container Temperature Anomaly," and assigns it to the responsible carrier for this shipment according to preset rules, while simultaneously sending a copy to the cargo owner and cargo insurer. The task details page automatically links to historical temperature anomaly graphs, a real-time location map of the container, and all relevant logistics events. The carrier's task recipient can directly update the task status on the collaborative platform: "Remote diagnosis and restart of refrigeration unit completed; onboard engineer notified for physical inspection," and can upload on-site inspection reports or images as attachments. The cargo owner's representative can view the entire process of the task in real time, from creation, assignment, processing to verification and closure, achieving a complete digital management process from "risk monitoring" to "early warning triggering" to "collaborative handling and closed-loop tracking."

[0054] To build a robust trust and traceability mechanism, the aforementioned blockchain-based evidence storage module stores operational records for key nodes throughout the logistics process. Whether it's critical logistics events such as customs electronic release, loading at port A, or triggering anomaly alarms, or key operational records like abnormal task assignments to carriers and carriers submitting processing reports generated on the collaborative platform, all will be captured by this module. The module extracts the core characteristics of the events to generate hash values, which, along with trusted timestamps and digital signatures from relevant parties, are written into the evidence storage platform built on consortium blockchain technology, forming immutable and publicly verifiable digital credentials. This provides efficient and authoritative electronic evidence for future cargo damage liability determination, insurance claim disputes, or compliance audits, greatly enhancing collaboration and mutual trust among all participants in cross-border logistics.

[0055] Furthermore, the processing and fusion module in this application possesses a more macro-level insight into supply chain risks. This module continuously connects to and integrates various external risk data sources, such as real-time throughput and congestion indices of major global ports, geopolitical dynamics and meteorological disaster warnings for key shipping routes, and statistics on typical operational anomalies from the same historical period. Based on internal logistics event flows and this external data, the module calculates and outputs a series of supply chain risk indicators through built-in models.

[0056] Ultimately, all processed, analyzed, and integrated information—including real-time location tracking maps for individual shipments, environmental monitoring data curves, complete logistics event timelines, dynamically updated predicted ETAs, currently active alert lists and collaborative task progress, and a macro-level supply chain risk dashboard—is transmitted to the user terminal through the unified data service interface provided by the application service module.

[0057] On the user terminal side, whether it's the cargo owner's operator on their dedicated client computer in their office or their manager through a mobile application on their smartphone, they can see a highly integrated and information-rich visual monitoring interface. On the map at the center of the interface, a dynamically moving icon clearly indicates that the container is sailing through the English Channel; the side panel displays in real-time that the internal temperature has returned to a stable -17.5°C; and the information bar below prominently displays: "Based on the latest data, the estimated arrival time at port B will be delayed by 32 hours. The system has generated optimization suggestions; please pay attention." Simultaneously, the status of the "Temperature Anomaly Handling" collaborative task related to this container is clearly visible.

[0058] In the carrier's global logistics command center, a large wall-to-wall LCD video wall forms the primary monitoring view of the system. On the screen, hundreds or even thousands of dots represent all transport units en route globally. The colors of these dots dynamically change according to risk indicators calculated by the system, making the overall risk situation immediately clear. One side of the screen continuously updates the latest early warning information and status updates of critical collaborative tasks from across the network. Dispatch and command personnel can use this panoramic view to quickly identify areas of abnormal activity, assess the overall network health, and efficiently allocate resources and conduct emergency command.

[0059] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An international logistics end-to-end visualization system, characterized in that, This includes the server terminal and the user terminal that communicates with the server terminal. The server terminal includes a data access module, a timeliness prediction module, a processing and fusion module, and an optimization suggestion module. The data access module is configured to acquire raw logistics data from multiple external logistics systems through multiple heterogeneous application programming interface (API) adapters, and receive sensing data from physical sensors through an Internet of Things (IoT) protocol interface. The processing and fusion module is used to communicate with the data access module and is configured to run an event stream processing engine to associate and fuse the original logistics data and the sensor data based on logistics identifiers, and generate a standardized logistics event sequence that conforms to a unified data model. The time-of-arrival prediction module is used to communicate with the processing and fusion module, and is configured to predict the arrival time of goods and dynamically update the estimated arrival time (ETA) based on the logistics event sequence, historical transportation data and real-time external information through a machine learning model. The optimization suggestion module is communicatively connected to the timeliness prediction module and is configured to automatically generate transportation route optimization suggestions based on the deviation between the predicted arrival time and the planned time output by the timeliness prediction module. The application service module is used to communicate with the processing and fusion module, the timeliness prediction module, and the optimization suggestion module, and is configured to generate a data interface for visualization based on the logistics event sequence, prediction results, and optimization suggestions. The user terminal includes an integrated display screen, configured to obtain data from the application service module and render a graphical user interface on the integrated display screen to display end-to-end status information.

2. The international logistics end-to-end visualization system according to claim 1, characterized in that, The server terminal also includes an early warning module; The early warning module is configured to run a business rule engine to monitor the logistics event sequence, sensor data, and prediction deviations output by the timeliness prediction module. When the monitored data meets preset abnormal conditions, it automatically generates early warning information and pushes it. The early warning module is also integrated with a multi-party collaboration platform and configured to automatically convert early warning information into collaborative tasks, assign them to relevant responsible parties, and track the processing progress.

3. The international logistics end-to-end visualization system according to claim 2, characterized in that, The server terminal also includes a blockchain evidence storage module, configured to perform blockchain evidence storage for key logistics events; the blockchain evidence storage module is also used to store key collaborative operation records and electronic vouchers generated by the multi-party collaboration platform.

4. The international logistics end-to-end visualization system according to claim 1, characterized in that, The event stream processing engine is specifically configured to infer and supplement missing logistics node events through state machine logic; the processing and fusion module is also configured to integrate external risk data sources based on the logistics event sequence, construct and output supply chain risk indicators, which are used to generate a risk dashboard view in the graphical user interface.

5. The international logistics end-to-end visualization system according to claim 1, characterized in that, The external logistics system includes at least two of the following: shipping company system, airline system, port terminal operating system, and customs clearance system.

6. The international logistics end-to-end visualization system according to claim 3, characterized in that, The multi-party collaboration platform supports at least two parties among cargo owners, carriers, customs, and warehouses to conduct online task collaboration, document sharing, and electronic signatures.

7. The international logistics end-to-end visualization system according to claim 4, characterized in that... The supply chain risk indicators include at least one of the following: global port congestion index, shipping route risk map, and seasonal anomaly statistics, and support user-defined risk thresholds and early warning rules.

8. The international logistics end-to-end visualization system according to claim 1, characterized in that... , The user terminal can be any of the following: a desktop or laptop computer or a mobile smart terminal with dedicated client software installed; or a large display device used for command and dispatch in the logistics center.

9. A method for visualizing the entire international logistics chain, applicable to the system as described in any one of claims 1-8, characterized in that, Includes the following steps: S1. Obtain raw logistics data from multiple external logistics systems through multiple heterogeneous API adapters on the server terminal, and receive sensing data from physical sensors through the Internet of Things protocol interface. S2. Using an event stream processing engine, the original logistics data and the sensor data are associated and fused based on logistics identifiers to generate a standardized logistics event sequence. S3. Based on the logistics event sequence, predict the dynamic ETA of the goods using a machine learning model; S4. Based on the deviation between the predicted arrival time and the planned time, generate transportation route optimization suggestions; S5. Based on the logistics event sequence, prediction results, and optimization suggestions, generate a data interface for visualization. S6. Run the business rules engine to monitor and generate early warnings for anomalies in logistics event sequences, sensor data and prediction deviations, and convert the early warnings into collaborative tasks for assignment and tracking. S7. Blockchain-based evidence storage for key logistics events and collaborative operation records; S8. Based on logistics event sequences and external risk data, construct supply chain risk indicators and generate a risk dashboard view; S9. Obtain data through user terminals and render and display the full-link status, collaborative task interface, and risk dashboard view.

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