Multi-airport luggage full-process tracking method and system

Abstract

The invention relates to a multi-airport luggage full-process tracking method and system, and the method comprises the following steps: data collection, tag collection of temperature, humidity and acceleration data, obtaining of a luggage tag image by a high-definition camera, synchronization of airline flight dynamic information, collection of airport real-time operation data and passenger feedback, personalized demands, data conversion, and data processing. The method comprises the following steps: coding and converting into a sequence token suitable for large model processing, a multi-head attention mechanism, focusing luggage timeliness, equipment load and other key parameters according to a specific rule, cooperating an intelligent label with a block chain, dynamically optimizing sensor parameters, recording luggage key state information and performing personalized feedback, and performing automatic identification and path planning. The system combines computer vision and a multi-head attention mechanism to identify labels in real time and optimize a transfer path, virtualizes luggage accompanying service, provides intelligent interaction and immersive experience, performs cross-airport collaborative management, monitors global airport dynamics in real time, establishes a risk early warning model, and coordinates resources to execute a transfer adjustment scheme.

Description

Technical Field

[0001] The invention belongs to the technical field of air transportation and logistics management, and specifically relates to a method and system for tracking the entire process of luggage at multiple airports. Background Art

[0002] In modern air transportation, the efficiency and accuracy of baggage management are of great significance to improving passenger satisfaction and ensuring transportation safety. Existing technologies mainly focus on baggage identification and tracking, but there are still certain limitations in multi-airport collaborative management, data security and intelligent decision-making.

[0003] Chinese invention patent CN116309692B discloses a method, device and medium for airport security check person-bag binding based on deep learning. The method obtains real-time monitoring video of the user placing the luggage, extracts effective images, and uses deep learning models to extract user identity features and luggage item features to achieve luggage-user binding and real-time tracking. Although this method improves the security during the security check, its main limitation is that it is limited to luggage binding and tracking at a single airport, lacks cross-airport collaborative management capabilities, and the efficiency and accuracy of processing large amounts of multi-source data still need to be improved.

[0004] Chinese invention patent CN115228753B discloses a method and device for visually tracking and displaying luggage. This method detects and displays the location of luggage by combining code reading and recognition with photoelectric sensing, and uses photoelectric sensors to monitor the status of luggage in real time and track it in real time on the display screen. Although this method achieves visual tracking of luggage location, its main limitation is that it relies on the distribution of physical sensors, has low scalability and data security, and is difficult to achieve unified management and intelligent optimization of luggage across multiple airports. Summary of the invention

[0005] In view of the shortcomings of the above-mentioned prior art, the present invention proposes a multi-airport baggage full-process tracking method and system, which aims to achieve efficient, intelligent and safe full-process tracking and management of baggage between multiple airports by integrating advanced smart tags, blockchain technology and multi-head attention mechanism, thereby improving the overall efficiency of baggage management and user experience.

[0006] To solve the above problems, the present invention provides a multi-airport baggage full-process tracking method and system, comprising the following steps: S1: Data collection, including: Collect sensor data from smart tags and baggage tag images; collect flight dynamic information and real-time operation data; collect baggage query, feedback and personalized demand information submitted by terminals; S2: Data conversion, converting the collected multi-source data into sequence tokens that can be processed by large models, including: converting the baggage tag text identified by computer vision into fixed-dimensional text tokens through natural language processing technology; converting flight take-off and landing times into timestamp sequence tokens, encoding the equipment status in the real-time airport operation data in binary, and encoding the baggage flow according to the number of baggage passing through key monitoring points; classifying and encoding passenger interaction information according to the interaction type, and encoding query information into sequence tokens containing keywords and time; S3: Use multi-head attention mechanism to focus on key parameters of luggage, including: Use the timeliness focus to focus on the timeliness requirements of baggage, use the equipment load focus to pay attention to the airport equipment load, use the resource focus to pay attention to the resource reserves and deployment capabilities of different airports, and use the weather focus to monitor the impact of weather changes on airports in different regions; S4: Smart label and blockchain collaborative processing, including: Analyze the converted sequence tokens based on the big model and dynamically optimize the operating parameters of the smart tag sensor; use blockchain technology to record the key status information of the luggage to ensure the immutability and transparency of the data; and feedback the luggage status information to passengers in a personalized and easy-to-understand way through the blockchain; S5: Automatic identification and dynamic path planning optimization, including: The computer vision system can be used to identify baggage tags in real time to improve recognition accuracy. The multi-head attention mechanism is combined with real-time data to generate and dynamically optimize the baggage transfer path to ensure efficient transportation. S6: Virtual luggage accompaniment service, including: Provide intelligent interactive services based on passenger needs, respond to passenger inquiries and feedback, and provide an immersive experience; S7: Cross-airport collaborative management execution, including: Real-time monitoring of weather, equipment and flight dynamics at airports around the world to establish risk warning models; when warnings are triggered, coordinate multi-airport resources, formulate and implement baggage transfer adjustment plans to ensure the stability and efficiency of the baggage transportation network.

[0007] In the S2 data conversion step, the data encoding of the smart tag includes encoding the temperature data in degrees Celsius with one decimal place, encoding the humidity data in percentage form, and encoding the acceleration data according to the directional components of the three-dimensional coordinate axes.

[0008] The steps of collaborative processing between S4 smart tags and blockchain include: dynamically adjusting the collection frequency and accuracy of the smart tag sensor according to the transportation stage of the luggage; recording the key status information of the luggage through blockchain technology, and customizing the information feedback strategy according to the type of passenger.

[0009] The S5 automatic identification and dynamic path planning optimization steps include: using the multi-head attention mechanism to improve the recognition accuracy of baggage tags in different lighting, angles and stacking scenarios; dynamically generating and optimizing baggage transfer routes based on real-time flight dynamics and airport operation data.

[0010] The multi-airport baggage full-process tracking system includes: a data collection module for executing step S1; A data conversion module, used to execute step S2 in claim 1; Large model processing module with built-in multi-head attention mechanism for executing S3 steps; Smart label and blockchain collaboration module, used to execute step S4; Automatic identification and dynamic path planning module, used to execute step S5; A virtual baggage companion service module, used to execute step S6; Cross-airport collaborative management module is used to execute S7 steps.

[0011] The data conversion module includes: an encoding rule unit for encoding multi-source data; an embedding combination unit for semantically fusing the encoded data to generate sequence tokens suitable for large model processing.

[0012] The large model processing modules include: a multi-head attention mechanism, which is used to focus on key parameters such as baggage timeliness, equipment load, resource reserves and weather impact; a model training unit, which uses federated learning and incremental learning methods to continuously optimize model performance to adapt to the dynamically changing airport operating environment.

[0013] The smart tag and blockchain collaborative modules include: an environmental prediction unit, which predicts environmental changes that luggage may encounter based on machine learning and deep learning algorithms; an information feedback unit, which pushes luggage status information through multiple channels based on passenger portraits.

[0014] The automatic identification and dynamic path planning modules include: a tag recognition model that combines multi-head attention and deep neural network technology to improve the accuracy of baggage tag recognition; a path planning unit that generates the optimal transfer path based on real-time data and dynamically adjusts the baggage transfer order to ensure priority processing of high-priority baggage.

[0015] The cross-airport collaborative management modules include: a risk warning unit, which monitors the weather, equipment and flight dynamics of airports around the world in real time, and establishes and updates risk warning models; an emergency linkage unit, which coordinates multi-airport resources, formulates and implements baggage transfer adjustment plans when the warning is triggered; and an execution supervision unit, which continuously monitors the execution of each airport during the emergency handling process, allocates resources in a timely manner, and ensures the efficiency and smoothness of the emergency handling process.

[0016] In summary, this application has the following beneficial effects: 1. By installing smart tags on luggage and deploying high-definition cameras at key transfer nodes, accurate identification and real-time tracking of luggage in each transfer link are achieved. The temperature, humidity and acceleration sensors built into the smart tags can monitor the environmental status of the luggage in real time, while the high-definition cameras ensure clear capture of the luggage tag images, thereby significantly reducing the risk of luggage loss or mishandling. The multi-head attention mechanism is used to focus on and analyze the key parameters of luggage timeliness requirements, equipment load, resource reserves and deployment capabilities, and weather changes, improving the accuracy of luggage tracking and the comprehensiveness of decision-making, ensuring efficient management of luggage throughout the entire transportation process.

[0017] 2. Through the automatic identification and dynamic path planning module, combined with real-time data and multi-head attention mechanism, the baggage transfer path can be dynamically generated and optimized. This not only ensures the priority handling of high-priority baggage, but also effectively avoids equipment congestion and resource waste, thereby significantly improving the overall efficiency of baggage transportation. The cross-airport collaborative management module monitors the operational dynamics of airports around the world in real time, establishes a risk warning model, and coordinates multi-airport resources when the warning is triggered, formulates and implements baggage transfer adjustment plans. This collaborative management mechanism can respond quickly to abnormal situations, ensure the stability and efficient operation of the baggage transportation network, and further improve transportation efficiency and system resilience.

[0018] 3. The large model processing module has a built-in multi-head attention mechanism, which can deeply analyze multi-source data, focus on the key parameters of luggage, and realize intelligent decision-making and automated control. This greatly reduces manual intervention and improves the automation level and operational efficiency of the system. The virtual luggage companion service module provides personalized services based on passenger needs through intelligent interaction and immersive experience. This not only improves the user experience, but also enhances the emotional connection between passengers and luggage through intelligent means, further promoting the intelligent development of the system.

[0019] 4. The smart tag and blockchain collaboration module records the key status information of the luggage through blockchain technology to ensure the immutability and transparency of the data. This not only enhances the data security of the system, but also improves the user's trust in the luggage management system. The environmental prediction unit, based on machine learning and deep learning algorithms, can predict in advance the environmental changes that the luggage may encounter and dynamically optimize the smart tag sensor parameters. This prediction and optimization mechanism ensures the safety and stability of the luggage in different environments, further improving the overall safety and reliability of the system.

[0020] 5. The cross-airport collaborative management module monitors the weather, equipment and flight dynamics of airports around the world in real time through the risk warning unit, and establishes and updates the risk warning model. When the warning is triggered, the system can quickly coordinate the resources of multiple airports, formulate and implement the baggage transfer adjustment plan, ensure the stability and efficient operation of the baggage transportation network under abnormal conditions, and execute the supervision unit to continuously monitor the execution of each airport during the emergency handling process, and timely deploy resources to ensure the efficiency and smoothness of the emergency handling process. This supervision and deployment mechanism effectively improves the system's response speed and processing capabilities when responding to emergencies, and enhances the overall stability and resilience of the system.

[0021] 6. The information feedback unit customizes information feedback strategies according to different types of passengers (such as business passengers and passengers carrying special items), and pushes luggage status information in a personalized and easy-to-understand manner through mobile applications or airport displays, thereby improving passengers' service experience and satisfaction. The virtual luggage accompaniment service module provides passengers with an immersive luggage transportation scene through augmented reality (AR) or virtual reality (VR) technology, thereby enhancing the emotional connection between passengers and luggage and further improving the overall user experience.

[0022] 7. The data conversion module can efficiently encode and semantically fuse multi-source data through encoding rule units and embedded combination units to generate sequence tokens suitable for large model processing. This efficient data processing mechanism ensures that the system can process massive amounts of data quickly and accurately, improving the overall data processing efficiency. The system architecture adopts a modular design, and each functional module works independently and collaboratively, which facilitates the expansion, maintenance and upgrade of the system. Such a design not only improves the overall reliability and flexibility of the system, but also facilitates future functional expansion and technology upgrades, ensuring the long-term sustainable development of the system.

[0023] 8. The large model processing module and cross-airport collaborative management module use big data analysis technology to establish a risk warning model, which can provide early insight into potential operational crises, provide strong decision-making support for operational management, and improve the scientificity and predictability of overall operational management. The multi-head attention mechanism can intelligently analyze and focus on key parameters of luggage, generate optimized decisions, and realize intelligent control during luggage transfer. This intelligent decision-making and optimized control mechanism not only improves operational efficiency, but also ensures the efficiency and safety of the luggage transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is the workflow diagram of this application; Figure 2 This is the system architecture diagram of this application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and beneficial effects of the present invention more clear, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings to facilitate understanding by technicians.

[0026] Embodiment 1: like Figure 1 to Figure 2 As shown, this embodiment introduces in detail a method for tracking the entire process of multi-airport baggage, covering key steps from data collection, data conversion, application of multi-head attention mechanism, to collaborative processing of smart tags and blockchain, automatic identification and dynamic path planning optimization. The specific implementation method of each step will be described step by step below.

[0027] Step S1: Data collection Smart label data collection: Smart tags are installed on each piece of luggage. The smart tags have built-in temperature sensors, humidity sensors and acceleration sensors. The sensors collect the temperature, humidity and movement status of the luggage in the environment (such as acceleration changes) in real time. The sensor data is regularly uploaded to the central data server through low-power wireless communication technology (such as Bluetooth low energy BLE).

[0028] Baggage tag image acquisition: High-definition cameras are installed at key baggage transfer nodes at each airport (such as security checkpoints, sorting centers, and boarding gates). The cameras continuously capture images of baggage tags passing by and transmit the image data to the computer vision system for real-time recognition and processing.

[0029] Flight dynamic information collection: The system connects with the airline's flight information system through a secure and efficient interface protocol (such as API), obtains dynamic data such as flight take-off and landing times, delays, aircraft model information, etc. in real time, and stores it in the central database.

[0030] Airport real-time operation data collection: By connecting with the airport operation management system, we can obtain real-time operation data including equipment status (such as conveyor belts and sorting equipment operation status), staffing (number of people on duty) and baggage flow (number of bags passing through key monitoring points per minute). The data is updated to the central data server in real time through secure encrypted transmission to ensure the real-time and accuracy of the data.

[0031] Passenger interaction information collection: Passengers can submit their baggage enquiries, feedback and personalized demand information through mobile phone applications, self-service terminals or manual counters. The system will classify and encode the collected interactive information and store it in the central database for subsequent processing.

[0032] Step S2: Data conversion Luggage tag text conversion: The luggage tag text identified by the computer vision system is first cleaned using natural language processing (NLP) technology to remove noise characters and correct spelling errors. The cleaned text is converted into fixed-dimensional text tokens through a word vector model to facilitate large-scale model processing.

[0033] Flight take-off and landing time conversion: The flight take-off and landing times are converted into timestamp sequence tokens according to the international standard time format.

[0034] Delays are quantified and coded in minutes, and aircraft information is coded according to the International Air Transport Association (IATA) standard code.

[0035] Real-time data conversion for airport operations: Equipment status is encoded in binary to indicate operating (1) or fault (0) status. Staffing data is encoded based on the number of people actually on duty. Baggage flow data is encoded based on the number of bags passing through key monitoring points per minute.

[0036] Conversion of passenger interaction information: Passenger interaction information is classified and encoded according to the interaction type. Query information includes keywords and time, which are converted into sequence tokens. Feedback information includes feedback content and feedback emotional tendency, which are converted into corresponding sequence tokens. Personalized demand information is encoded according to demand category and priority.

[0037] Step S3: Use multi-head attention mechanism to focus on key parameters of luggage Timeliness concerns: The timeliness concern head focuses on the timeliness requirements of the baggage, and combines the flight connection time and the transit stop time to accurately calculate the optimal transfer speed and route selection for the baggage. For example, at the initial stage of the baggage transfer process entering the airport, the timeliness concern head obtains the flight connection time and transit stop time corresponding to the baggage, combines the airport internal map data and the preset transfer speed model for different areas, and calculates the theoretically fastest route set to the next link.

[0038] Equipment load focus: The equipment load monitoring head is used to monitor the real-time load of each conveyor belt and sorting equipment in the airport, marking the congested sections and idle sections. At predetermined time intervals (such as 5 minutes), the equipment load monitoring head dynamically switches the baggage transfer route according to the changes in equipment load, giving priority to guiding the baggage flow to the idle sections.

[0039] Resource focus: Use resource focus to pay attention to the resource reserves and deployment capabilities of different airports, and reasonably arrange the transfer destination of luggage. According to the idle manpower and equipment of each airport, choose the most suitable transfer equipment for the luggage.

[0040] Weather concerns: Use weather monitoring to monitor weather changes at airports in different regions, predict flight cancellations or delays due to bad weather in advance, and adjust baggage transportation strategies. For example, during the daily monitoring phase, when the bad weather indicators at a certain regional airport exceed the set threshold, a potential operational crisis warning is triggered, and a baggage transfer adjustment plan is formulated in advance.

[0041] Step S4: Smart label and blockchain collaborative processing Dynamically optimize smart tag sensor parameters: The converted sequence tokens are analyzed based on the big model, and the operating parameters of the smart tag sensor are adjusted dynamically. For example, based on the transportation stage of the luggage, combined with the historical luggage transportation data of the same type of flights and the analysis of the current weather and airport busyness, the environmental changes that the luggage may encounter are predicted. When it is predicted that the luggage will pass through a high-temperature area, the temperature sensor of the smart tag is instructed to increase the collection frequency to 10 times per minute, and the accuracy is adjusted to two decimal places to accurately capture potential risks.

[0042] Blockchain technology records key luggage status information: Blockchain technology is used to record the key status information of luggage to ensure the data is tamper-proof and transparent. Every change in luggage status (such as temperature, humidity changes, arrival at transfer nodes, etc.) will be recorded on the blockchain to form an unalterable luggage status log.

[0043] Personalized, easy-to-understand feedback on baggage status: S5: Automatic identification and dynamic path planning optimization, including: The computer vision system can be used to identify baggage tags in real time to improve recognition accuracy. The multi-head attention mechanism is combined with real-time data to generate and dynamically optimize the baggage transfer path to ensure efficient transportation. In a multi-airport scenario, in order to achieve efficient baggage transportation, it is necessary to dynamically select the transfer path based on real-time data. The key factors affecting the transfer path selection usually include: Time Sensitivity: the flight departure and arrival times of the baggage, the passenger transfer time, etc. Equipment Load: The real-time busyness of each conveyor belt and sorting equipment; Resource Availability: the available personnel and facility reserves at each airport; Weather Impact: Delays or reduced equipment availability that may result from inclement weather.

[0044] In order to fully consider the above factors, the corresponding dynamic cost function can be defined on the graph model or network model, and the multi-head attention mechanism can be combined to assign variable weights to each path or edge, so as to optimize the baggage transfer decision at different times.

[0045] Combination of multi-head attention mechanism and path planning Weights of multi-head attention mechanism The multi-head attention mechanism consists of K "heads", corresponding to different focus points, for example: First focus: Head time Timeliness concerns Second focus: Head load Equipment load concern The third focus: Head resource Resource reserve and deployment focus The fourth focus: Head weather Weather concerns By analyzing the current status of the baggage and the real-time operation data of the airport, the large model will output an attention weight vector that changes over time as follows: Transshipment Network Model The airport and its internal transfer nodes are abstracted as G=(V,E), where: V is a set of nodes (such as security checkpoints, sorting centers, boarding gates, etc.), |V|=N; E is a set of directed edges (representing conveyor belts or accessible transfer routes), |E|=M; (u,v)∈E indicates that there is a feasible transit channel from node u to node v.

[0046] Dynamic cost function For each edge (u,v)∈E, define a time-varying "comprehensive cost" C(u,v,t) to measure the cost or time of the luggage passing through the edge. It can be divided into 4 sub-cost functions, corresponding to 4 attention heads: Time cost time (u,v,t) reflects the estimated time required for the baggage to pass through edge (u,v) or the impact on the connecting flight.

[0047] Equipment load cost f load (u, v, t) reflects the busyness of the conveyor belt and sorting equipment on this edge. The higher the load, the lower the baggage passing efficiency.

[0048] Resource storage cost resource(u, v, t) reflects the available manpower and equipment deployment at a node or airport. The cost is higher when manpower and equipment are insufficient.

[0049] Weather impact cost weather (u,v,t) When the weather is bad, the availability of the edge or node will decrease, the transportation speed will slow down or the success rate will decrease, and the corresponding cost will increase.

[0050] Using the weight α(t) output by the multi-head attention mechanism, the “comprehensive” cost function can be obtained as follows: Dynamic path planning objectives Given the starting node s where the baggage is located and the destination node (or airport) d, find a path P at time t that minimizes the cumulative cost. If the path consists of an edge sequence (e 1 ,e 2 ....e L ), then the objective function is: Dynamic update: At preset time intervals (e.g., 5 minutes) or whenever key operational parameters change significantly (e.g., sudden equipment failure or weather warning), the big model recalculates the new attention weight α(t) and updates the cost function of each edge, thereby replanning the optimal transfer path for the baggage.

[0051] Implementation process of dynamic path planning Baggage detection and identification The computer vision system is used to identify the luggage tag in real time and obtain the current location of the luggage.

[0052] Multi-head attention mechanism calculates weights The large model analyzes the latest sequence token (including timeliness, load, resources, weather, etc.) and outputs α(t).

[0053] Update edge cost Recalculate C(u,v,t) for each feasible edge (u,v) to reflect the latest operation status and attention focus.

[0054] Optimal path search On G=(V,E), use the shortest path algorithm (such as Dijkstra, A*, or a custom multi-objective algorithm) to solve the optimal path P ∗ (t), satisfies the following formula: If the system adopts parallel multi-objective optimization, the search can also be performed using an algorithm based on the Pareto optimal solution.

[0055] Execution and Monitoring Send the optimal path to the baggage transfer control system to achieve automatic control of conveyor belts, sorting equipment, etc. Continuously monitor operational data and baggage location, and repeat steps 2 to 4 whenever there is a significant change in the environment to dynamically update the baggage transfer route.

[0056] Adaptability of multi-head attention mechanism As time goes by and the environment changes (such as flight delays, equipment load, and sudden weather changes), the attention weight α(t) can be adaptively adjusted so that the cost function C(u,v,t) reflects the priority needs of baggage transfer in real time.

[0057] Real-time and robustness: through dynamic path planning, the route is automatically re-planned at fixed intervals or when key operating parameters change suddenly, ensuring that baggage can still arrive at the destination smoothly and efficiently in the complex and changing airport environment.

[0058] Comprehensive multi-factor decision-making, taking into account factors such as timeliness, equipment load, resources, weather, etc., can significantly improve transfer efficiency and reduce baggage mishandling or delays.

[0059] Scalability: If more attention heads need to be included in the future (such as cost attention heads, carbon emission attention heads, etc.), it is only necessary to add corresponding sub-cost functions and attention weights to the comprehensive cost function to maintain compatibility with the existing framework.

[0060] The information feedback strategy is customized according to the type of passengers, and the key status information of the luggage on the blockchain is fed back to the passengers in a personalized and easy-to-understand way through mobile phone applications or airport displays. For example, for business passengers, the luggage connection time information is highlighted, such as the countdown of the remaining transfer time; for passengers carrying special items, the environmental safety information of the items is highlighted, such as whether the temperature and humidity are suitable for the storage of special medicines.

[0061] At each transfer node, the baggage collects environmental data and image data through smart tags and cameras, and synchronizes flight dynamic information with real-time airport operation data.

[0062] The inquiries, feedback and personalized demand information submitted by passengers through the terminal are collected and classified and coded by the system in real time.

[0063] The collected multi-source data is converted into sequence tokens through specific encoding rules to ensure a unified data format for easy processing by large models.

[0064] Various types of data (such as sensor data, image text, flight information, operational data, and passenger interaction information) are encoded separately to generate structured sequence tokens.

[0065] The multi-head attention mechanism built into the large model focuses on the timeliness requirements of luggage, equipment load, resource reserves and allocation capabilities, and the impact of weather changes on luggage transfer.

[0066] Each focus calculates the optimal transfer speed and route of baggage based on real-time and historical data analysis, and optimizes the baggage transfer path.

[0067] The large model analyzes the converted sequence tokens and dynamically adjusts the operating parameters of the smart tag sensor to adapt to environmental changes that may be encountered during luggage transportation.

[0068] The key status information of the luggage is recorded through blockchain technology to ensure the immutability and transparency of the data.

[0069] The system provides personalized feedback of baggage status information to passengers through multiple channels based on passenger type, thus improving user experience.

[0070] The computer vision system identifies baggage tags in real time, improves recognition accuracy, and ensures accurate tracking of baggage at each transfer node.

[0071] The large model is combined with real-time data and uses a multi-head attention mechanism to generate and dynamically optimize the luggage transfer path to ensure that the luggage arrives at its destination efficiently and safely.

[0072] Provide intelligent interactive services based on passenger needs, respond to passenger inquiries and feedback, provide immersive experience, and enhance the emotional connection between passengers and luggage.

[0073] Real-time monitoring of weather, equipment and flight dynamics at airports around the world to establish risk warning models. When warnings are triggered, coordinate multi-airport resources, formulate and implement baggage transfer adjustment plans to ensure the stability and efficiency of the baggage transportation network.

[0074] Through the combination of smart tags and high-definition cameras, accurate identification and real-time tracking of luggage at each transfer node can be achieved, reducing the risk of luggage loss or mishandling.

[0075] The multi-head attention mechanism is used to focus on key parameters, and the baggage transfer path is dynamically optimized based on real-time data to ensure that the baggage arrives at the destination efficiently and safely, shortening the baggage transportation time.

[0076] Through the application of large models and multi-head attention mechanisms, intelligent decision-making and automated control can be achieved in the baggage transfer process, reducing manual intervention and improving operational efficiency.

[0077] Blockchain technology is used to record the key status information of luggage, ensuring the immutability and transparency of data, and enhancing the trust of the system and data security.

[0078] Customize information feedback strategies according to different passenger types, provide personalized and easy-to-understand baggage status information, and improve passengers' service experience and satisfaction.

[0079] Through cross-airport collaborative management and risk warning models, potential operational crises can be predicted and responded to in advance, ensuring the stable and efficient operation of the baggage transportation network under abnormal circumstances and reducing baggage delay losses.

[0080] Through the method described in this embodiment, full-process intelligent tracking and efficient transfer of luggage between multiple airports can be achieved, the accuracy and efficiency of luggage management can be improved, and the service experience of passengers can be enhanced. It has significant practical value and broad application prospects.

[0081] Embodiment 2: like Figure 1 to Figure 2 As shown, this embodiment describes in detail a multi-airport baggage full-process tracking system, covering key modules such as data collection, data conversion, large model processing, smart tag and blockchain collaborative processing, automatic identification and dynamic path planning optimization, virtual baggage companion service, and cross-airport collaborative management execution. The specific implementation method and function of each module will be introduced step by step below.

[0082] The data acquisition module is responsible for acquiring various types of baggage-related data from various data sources, including smart tag sensor data, baggage tag images, flight dynamic information, airport real-time operation data and passenger interaction information.

[0083] Smart tags are installed on each piece of luggage, with built-in temperature sensors, humidity sensors and acceleration sensors. The sensors regularly upload data to the central data server through low-power wireless communication technology (such as Bluetooth Low Energy BLE).

[0084] Install high-definition cameras at key transfer nodes at each airport (such as security checkpoints, sorting centers, and boarding gates) to continuously capture images of luggage tags passing by. The cameras transmit image data in real time over the network to the computer vision system for recognition and processing.

[0085] The system connects with the airline's flight information system through a secure and efficient interface protocol (such as API) to obtain real-time dynamic data such as flight take-off and landing times, delays, aircraft model information, etc. The acquired data is stored in the central database for subsequent processing.

[0086] Through the data interface with the airport operation management system, real-time operation data including equipment status (such as conveyor belts and sorting equipment operating status), staffing (number of people on duty) and baggage flow (number of bags passing through key monitoring points per minute) are obtained. Data is updated to the central data server in real time through encrypted transmission to ensure the real-time and accuracy of data.

[0087] Passengers submit their baggage enquiries, feedback and personalised demand information through mobile phone applications, self-service terminals or manual counters. The system will classify and encode the collected interactive information and store it in the central database for subsequent processing.

[0088] The data conversion module is responsible for encoding and embedding multi-source heterogeneous data, generating sequence tokens suitable for large model processing, and ensuring data format unification and semantic fusion.

[0089] Coding rules unit: Different encoding rules are set according to the sensor type: temperature data is encoded in degrees Celsius and accurate to one decimal place. Humidity data is encoded in percentage form. Acceleration data is encoded according to the directional components of the three-dimensional coordinate axes.

[0090] The baggage tag text identified by computer vision is cleaned by natural language processing technology, and after removing noise characters and correcting spelling errors, it is converted into a fixed-dimensional text token according to the word vector model. The flight take-off and landing times are converted into timestamp sequence tokens according to the international standard time format, the delays are quantified and encoded in minutes, and the aircraft model information is encoded according to the International Air Transport Association (IATA) standard code.

[0091] The equipment status in the real-time data of airport operations is represented by binary code to indicate the operating (1) or fault (0) status. The staffing data is encoded by the actual number of people on duty. The baggage flow data is encoded based on the number of bags passing through key monitoring points per minute. Passenger interaction information is coded according to the interaction type. The query information includes keywords and time, which are converted into sequence tokens. The feedback information includes feedback content and feedback emotional tendency, which are converted into corresponding sequence tokens. Personalized demand information is encoded according to demand category and priority.

[0092] Embedded Combination Unit: An efficient embedding algorithm is used to semantically fuse the encoded multi-source data to generate sequence tokens suitable for large model processing. This ensures that different types of data can be effectively combined semantically, reduces data redundancy, and improves data processing efficiency.

[0093] The large model processing module has a built-in multi-head attention mechanism, which is responsible for in-depth analysis of the converted sequence tokens, focusing on the key parameters of the luggage, and continuously optimizing the model performance through the model training unit.

[0094] Multi-head attention mechanism: The model contains multiple attention heads, each of which focuses on different key parameters of baggage: The timeliness attention head focuses on the timeliness requirements of baggage. The equipment load attention head pays attention to the load of airport equipment. The resource attention head pays attention to the resource reserves and deployment capabilities of different airports. The weather attention head monitors the impact of weather changes on airports in different regions. Each attention head uses parallel computing to comprehensively process multiple aspects of information to improve the accuracy and comprehensiveness of decision-making.

[0095] Model training unit: Federated learning and incremental learning methods are used to train models using distributed computing resources. Model parameters are continuously optimized to enhance the generalization ability of the model and ensure that it can adapt to the dynamically changing airport operating environment. Historical data and real-time data are used for training to ensure the model's responsiveness to the latest situation.

[0096] The smart tag and blockchain collaborative module is responsible for dynamically optimizing the sensor parameters of the smart tag and using blockchain technology to record the key status information of the luggage, ensuring the data is tamper-proof and transparent, while providing personalized feedback of the luggage status information to passengers.

[0097] Environmental Prediction Unit: Based on machine learning and deep learning algorithms, the sequence tokens processed by the large model are analyzed to predict the environmental changes that the luggage may encounter. For example, when it is predicted that the luggage will enter a high-temperature area, the temperature sensor parameters of the smart tag are adjusted in advance.

[0098] Information feedback unit: According to the passenger portrait, luggage status information is pushed to passengers in a personalized and easy-to-understand way through multiple channels. Different information feedback strategies are customized for different types of passengers (such as business passengers and passengers carrying special items) to improve user experience.

[0099] Blockchain records: Blockchain technology is used to record the key status information of luggage to ensure the data is tamper-proof and transparent. Every change in luggage status (such as temperature, humidity changes, arrival at transfer nodes, etc.) will be recorded on the blockchain to form an unalterable luggage status log.

[0100] The automatic identification and dynamic path planning module uses a computer vision system to identify luggage tags in real time, and uses a multi-head attention mechanism combined with real-time data to generate and dynamically optimize the luggage transfer path to ensure efficient luggage transportation.

[0101] Tag recognition model: Combining multi-head attention mechanism and deep neural network technology, the recognition accuracy of luggage tags in different lighting, angles and stacking scenarios is improved. Regular incremental learning and training methods are adopted to continuously optimize the recognition model using newly collected image data to ensure that the recognition accuracy is stable at more than 98%.

[0102] Path planning unit: Based on real-time flight dynamics and airport operation data, the multi-head attention mechanism is used to generate the optimal transfer path. The baggage transfer order is dynamically adjusted to ensure that high-priority baggage is handled first. The path planning results are updated to the airport baggage transfer control system in real time, and the running direction and speed of the conveyor belt and sorting equipment are automatically adjusted to achieve automated transfer.

[0103] The virtual luggage companion service module provides intelligent interactive services and immersive experiences based on passenger needs, enhancing the emotional connection between passengers and luggage and improving user experience.

[0104] Intelligent interactive service: Combining natural language processing (NLP) and conversational AI technology, it provides intelligent interactive services to respond to passengers' inquiries and feedback. Passengers interact with the virtual luggage companion through the mobile app to obtain luggage status information, transfer suggestions and other services.

[0105] Immersive Experience: Using augmented reality (AR) or virtual reality (VR) technology, exclusive baggage transportation scenes are generated in real time based on passengers' interests and preferences. As the luggage status changes, the virtual scene details are dynamically updated to enhance passengers' emotional connection and experience.

[0106] The cross-airport collaborative management module is responsible for real-time monitoring of weather, equipment and flight dynamics at airports around the world, establishing a risk warning model, coordinating multi-airport resources when a warning is triggered, formulating and implementing baggage transfer adjustment plans, and ensuring the stability and efficiency of the baggage transportation network.

[0107] Risk early warning unit: Utilize big data analysis technology to monitor weather, equipment and flight dynamics at airports around the world in real time.

[0108] Establish and continuously update risk warning models to gain early insight into potential operational crises. When severe weather indicators at a certain airport exceed set thresholds (such as heavy rain, wind speed, etc.), or equipment failures or large-scale flight delays are detected, a potential operational crisis warning is triggered.

[0109] Emergency linkage unit: When an early warning is triggered, coordinate the resources of multiple airports, formulate and implement a baggage transfer adjustment plan. Notify the backup airports around the affected airport to start receiving preparations and re-plan the multi-level transfer routes of the affected baggage. According to the resource reserves of each airport, reasonably allocate baggage transfer tasks to ensure the stability and efficiency of baggage transportation.

[0110] Execution supervision unit: Continuously monitor the implementation of each airport during the emergency response process to ensure the effective implementation of the baggage transfer adjustment plan. If it is found that an airport is lagging behind in implementation, timely deploy additional resources to support, such as additional personnel and emergency equipment, to ensure the efficiency and smoothness of the entire emergency response process.

[0111] The data acquisition module collects multi-source data through smart tags, cameras, flight information systems, airport operation management systems and passenger terminals, and transmits the data to the data conversion module.

[0112] The encoding rule unit in the data conversion module specifically encodes the collected data, and the embedding combination unit semantically fuses the encoded data to generate a sequence token suitable for large model processing and transmit it to the large model processing module.

[0113] The large model processing module has a built-in multi-head attention mechanism to analyze sequence tokens, focus on the key parameters of luggage, generate optimization decisions, and transmit them to the smart tag and blockchain collaboration module and the automatic identification and dynamic path planning module.

[0114] The smart tag and blockchain collaborative module dynamically optimizes the smart tag sensor parameters, uses blockchain technology to record luggage status information, and provides personalized feedback of the luggage status information to passengers through the information feedback unit.

[0115] The automatic identification and dynamic path planning module uses a computer vision system to identify baggage tags in real time, and combines a multi-head attention mechanism to generate and dynamically optimize baggage transfer routes to ensure efficient baggage transportation.

[0116] The virtual luggage companion service module provides intelligent interactive services and immersive experiences based on passenger needs, enhancing the emotional connection between passengers and their luggage.

[0117] The cross-airport collaborative management module monitors the operational dynamics of airports around the world in real time, establishes a risk warning model, coordinates multi-airport resources when a warning is triggered, formulates and implements baggage transfer adjustment plans, and ensures the stability and efficiency of the baggage transportation network.

[0118] At each transfer node, the luggage collects environmental data and image data through smart tags and cameras, and synchronizes flight dynamic information and airport operation data in real time.

[0119] The system encodes multi-source data through the encoding rule unit, embeds it into the combination unit for semantic fusion, and generates sequence tokens.

[0120] The large model processing module uses a multi-head attention mechanism to analyze sequence tokens, focus on key parameters of luggage, and generate optimized decisions.

[0121] The smart tag and blockchain collaborative module dynamically adjusts sensor parameters based on the analysis results of the large model, records luggage status information, and provides feedback to passengers.

[0122] The automatic identification and dynamic path planning module optimizes the transfer path by real-time identification of baggage tags and combining the multi-head attention mechanism to ensure efficient transportation.

[0123] The virtual luggage companion service module provides intelligent interaction and immersive experience to enhance user experience.

[0124] The cross-airport collaborative management module coordinates multi-airport resources through real-time monitoring and risk warning to ensure the stability and efficiency of the baggage transportation network.

[0125] Through modular design, each functional module works independently and collaboratively, which facilitates system expansion, maintenance and upgrades, and improves the overall reliability and flexibility of the system.

[0126] The data conversion module achieves unified processing of multi-source data through specific encoding and embedding combinations, ensuring data format consistency and semantic fusion, and improving data processing efficiency.

[0127] The large model processing module has a built-in multi-head attention mechanism that can efficiently focus on key baggage parameters, support intelligent decision-making, and optimize baggage transfer routes.

[0128] Blockchain technology is used to record the key status information of luggage, ensuring the immutability and transparency of data and improving the security and trust of the system.

[0129] The application of large models and multi-head attention mechanisms enables intelligent decision-making and automated control in the baggage transfer process, reduces manual intervention, and improves operational efficiency.

[0130] The automatic identification and dynamic path planning module optimizes the baggage transfer path in real time to ensure that the baggage arrives at the destination efficiently and safely.

[0131] The virtual luggage companion service module provides personalized services through intelligent interaction and immersive experience, enhances the emotional connection between passengers and luggage, and improves user satisfaction.

[0132] The system customizes information feedback strategies according to different passenger types, provides personalized and easy-to-understand baggage status information, and improves service quality.

[0133] The cross-airport collaborative management module provides early insight into potential operational crises through real-time monitoring and risk warning, quickly responds to and coordinates multi-airport resources, ensures the stable and efficient operation of the baggage transportation network under abnormal circumstances, and reduces the risk of baggage delays and loss.

[0134] Through the combination of smart tags and high-definition cameras, accurate identification and real-time tracking of luggage at each transfer node can be achieved, reducing the risk of luggage loss or mishandling.

[0135] The multi-head attention mechanism combines with real-time data to optimize the transfer route, ensure efficient luggage transportation, shorten luggage transportation time, and improve overall operational efficiency.

[0136] Through the multi-airport baggage full-process tracking system described in this embodiment, it is possible to achieve full-process intelligent tracking and efficient transfer of baggage on a global scale, improve the accuracy and efficiency of baggage management, enhance the service experience of passengers, and have significant practical value and broad application prospects.

[0137] Embodiment 3: like Figure 1 to Figure 2 As shown, this embodiment describes in detail the advanced functions and optimization mechanisms of a multi-airport baggage full-process tracking system, covering the environment prediction unit and information feedback unit in the smart tag and blockchain collaboration module, the tag recognition model and path planning unit in the automatic recognition and dynamic path planning module, and the risk warning unit, emergency linkage unit and execution supervision unit in the cross-airport collaborative management module. The specific implementation method and functions of each module will be introduced step by step below.

[0138] The smart tag and blockchain collaborative module is responsible for predicting the environmental changes that the luggage may encounter through the environmental prediction unit, and pushing luggage status information through multiple channels based on passenger portraits through the information feedback unit, thereby improving data security and transparency while enhancing user experience.

[0139] Environmental Prediction Unit: Based on machine learning (ML) and deep learning (DL) algorithms, the sequence tokens processed by the large model are analyzed to predict the environmental changes (such as temperature, humidity, vibration, etc.) that the luggage may encounter during transportation.

[0140] By using historical transportation data and real-time environmental data to establish a predictive model, potential environmental risks can be identified in advance.

[0141] For example, when it is predicted that the luggage is about to enter a high-temperature area, the system automatically adjusts the temperature sensor parameters of the smart tag to increase the frequency and accuracy of data collection to ensure more accurate status monitoring of the luggage in a high-temperature environment.

[0142] Information feedback unit: Baggage status information is pushed through multiple channels (such as mobile applications, airport displays, emails, etc.) based on the passenger's personal profile (such as passenger type, luggage nature, preference settings, etc.).

[0143] Customize feedback strategies for different types of travelers: Business travelers: Highlight luggage timeliness information, such as the countdown to remaining transfer time, to ensure that luggage arrives on time.

[0144] Passengers carrying special items: The environmental safety information of the luggage is displayed with emphasis, such as whether the temperature and humidity are suitable for the storage of special medicines, to ensure the safety of special items.

[0145] Information feedback is presented in an easy-to-understand format with pictures and texts, which enhances passengers' perception and trust in the status of their luggage.

[0146] The automatic identification and dynamic path planning module improves the recognition accuracy of baggage tags by combining the multi-head attention mechanism and deep neural network technology, generates the optimal transfer path based on real-time data, dynamically adjusts the baggage transfer order, and ensures priority processing of high-priority baggage.

[0147] Tag recognition model: Combining multi-head attention mechanism with deep neural network (DNN) technology, the recognition accuracy of luggage tags in different lighting, angles and stacking scenarios is improved.

[0148] Model training uses a large amount of diverse luggage tag image data, including complex scenes such as different lighting conditions, shooting at different angles, partial occlusion, etc., to ensure the robustness and accuracy of the model.

[0149] A regular incremental learning and training method is adopted to continuously optimize the recognition model using newly acquired image data to ensure that the recognition accuracy rate remains stable at above 98%.

[0150] Path planning unit: Based on real-time flight dynamics and airport operation data, the multi-head attention mechanism is used to generate the optimal transfer path.

[0151] Dynamically adjust the baggage transfer sequence to ensure that high-priority baggage (such as business class baggage and special item baggage) is handled first.

[0152] The path planning results are updated to the airport baggage transfer control system in real time, and the running direction and speed of the conveyor belts and sorting equipment are automatically adjusted to achieve automated transfer.

[0153] Route planning takes into account multiple factors, such as equipment load, baggage timeliness requirements, resource reserves, etc., to ensure the optimization and efficiency of the transfer route.

[0154] The cross-airport collaborative management module is responsible for real-time monitoring of weather, equipment and flight dynamics at airports around the world, establishing a risk warning model, coordinating multi-airport resources when a warning is triggered, formulating and implementing baggage transfer adjustment plans, and ensuring the stability and efficiency of the baggage transportation network.

[0155] Risk early warning unit: Utilize big data analysis technology to monitor weather, equipment and flight dynamics at airports around the world in real time.

[0156] Establishing and continuously updating risk warning models can provide early insight into potential operational crises.

[0157] Monitoring indicators include severe weather (such as heavy rain precipitation and strong wind speed), equipment failure (such as conveyor belt downtime), flight delays (such as the proportion of large-scale flight delays), etc.

[0158] When the monitoring indicators of an airport in a certain area exceed the set threshold, a potential operational crisis warning is automatically triggered.

[0159] Emergency linkage unit: When an early warning is triggered, coordinate multi-airport resources to develop and implement baggage transfer adjustment plans.

[0160] Notify alternative airports around the affected airport to start reception preparations to ensure that luggage can be transferred in a timely manner.

[0161] Re-plan the multi-level transfer routes for the affected baggage, reasonably allocate baggage transfer tasks based on the resource reserves of each airport, and ensure stable and efficient baggage transportation.

[0162] Execution supervision unit: Continuously monitor the implementation status of each airport during the emergency handling process to ensure the effective implementation of the baggage transfer adjustment plan.

[0163] If it is found that an airport is lagging behind in execution progress, additional resource support will be deployed in a timely manner, such as adding personnel and deploying emergency equipment, to ensure the efficiency and smoothness of the entire emergency response process.

[0164] Through real-time data monitoring and feedback mechanisms, the transparency and traceability of the emergency response process are ensured, and the overall responsiveness and reliability of the system are improved.

[0165] Working process The smart tag and blockchain collaborative module analyzes the environmental changes that may be encountered during luggage transportation through the environmental prediction unit, dynamically optimizes the smart tag sensor parameters, uses blockchain technology to record luggage status information, and provides personalized feedback of the luggage status information to passengers through the information feedback unit.

[0166] The automatic identification and dynamic path planning module uses the tag recognition model to identify baggage tags in real time, and combines the multi-head attention mechanism to generate and dynamically optimize the baggage transfer path to ensure efficient transportation.

[0167] The cross-airport collaborative management module monitors the operational dynamics of airports around the world in real time through the risk warning unit, establishes a risk warning model, coordinates multi-airport resources when the warning is triggered, formulates and implements baggage transfer adjustment plans, and ensures the stability and efficiency of the baggage transportation network.

[0168] Baggage transfer nodes at airports around the world collect environmental data and image data through smart tags and high-definition cameras. The system encodes and fuses multi-source data through a data conversion module to generate a sequence token.

[0169] The large model processing module uses a multi-head attention mechanism to analyze sequence tokens, focus on key parameters of luggage, and generate optimized decisions.

[0170] The smart tag and blockchain collaborative module dynamically adjusts sensor parameters based on the analysis results of the large model, records luggage status information, and pushes personalized information to passengers through the information feedback unit.

[0171] The automatic identification and dynamic path planning module optimizes the transfer path by real-time identification of baggage tags and combining the multi-head attention mechanism to ensure efficient transportation of baggage.

[0172] The cross-airport collaborative management module monitors the operational dynamics of airports around the world in real time through the risk warning unit, establishes a risk warning model, coordinates multi-airport resources when the warning is triggered, formulates and implements baggage transfer adjustment plans, and ensures the stability and efficiency of the baggage transportation network.

[0173] Through the environmental prediction unit, the system can predict in advance the environmental changes that the luggage may encounter during transportation, dynamically adjust the parameters of the smart tag sensor, and ensure the accuracy and real-time performance of luggage status monitoring.

[0174] The information feedback unit pushes personalized baggage status information through multiple channels (such as mobile applications and display screens) based on passenger portraits, thereby improving passengers' service experience and satisfaction.

[0175] The tag recognition model that combines the multi-head attention mechanism with deep neural network technology can significantly improve the recognition accuracy of baggage tags in complex environments and ensure accurate tracking of baggage at each transfer node.

[0176] The path planning unit generates the optimal transfer path based on real-time data and multi-head attention mechanism, dynamically adjusts the baggage transfer order, ensures priority processing of high-priority baggage, and significantly improves baggage transportation efficiency.

[0177] Measure the operational dynamics of airports around the world, establish and update risk warning models, and gain early insight into potential operational crises. When the warning is triggered, the emergency linkage unit can quickly coordinate multi-airport resources, formulate and implement baggage transfer adjustment plans, and ensure the stable and efficient operation of the baggage transportation network under abnormal circumstances. The execution supervision unit continuously monitors the execution of each airport during the emergency handling process, allocates resources in a timely manner, ensures the efficiency and smoothness of the emergency handling process, and reduces the risk of baggage delays and losses.

[0178] Blockchain technology is used to record the key status information of luggage, ensuring the immutability and transparency of data, and enhancing the trust of the system and data security.

[0179] Through intelligent interactive services and immersive experiences, personalized baggage status information and emotional connections are provided to enhance passengers' service experience and satisfaction.

[0180] Through the advanced functions and optimization mechanisms of the multi-airport baggage full-process tracking system described in this embodiment, intelligent prediction, personalized information feedback, accurate identification and efficient transfer of baggage on a global scale, as well as efficient risk warning and emergency response can be achieved, which significantly improves the intelligence, automation and safety of baggage management, and has significant practical value and broad application prospects.

[0181] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for tracking baggage throughout the entire process at multiple airports, characterized by: The following steps are involved: S1: Data collection, including: Collect sensor data from smart tags and baggage tag images; collect flight dynamic information and real-time operation data; collect baggage query, feedback and personalized demand information submitted by terminals; S2: Data conversion, converting the collected multi-source data into sequence tokens that can be processed by large models, including: converting the baggage tag text identified by computer vision into fixed-dimensional text tokens through natural language processing technology; converting flight take-off and landing times into timestamp sequence tokens, encoding the equipment status in the real-time airport operation data in binary, and encoding the baggage flow according to the number of baggage passing through key monitoring points; classifying and encoding passenger interaction information according to the interaction type, and encoding query information into sequence tokens containing keywords and time; S3: Use multi-head attention mechanism to focus on key parameters of luggage, including: Use the timeliness focus to focus on the timeliness requirements of baggage, use the equipment load focus to pay attention to the airport equipment load, use the resource focus to pay attention to the resource reserves and deployment capabilities of different airports, and use the weather focus to monitor the impact of weather changes on airports in different regions; S4: Smart label and blockchain collaborative processing, including: Analyze the converted sequence tokens based on the big model and dynamically optimize the operating parameters of the smart tag sensor; use blockchain technology to record the key status information of the luggage to ensure the immutability and transparency of the data; and feedback the luggage status information to passengers in a personalized and easy-to-understand way through the blockchain; S5: Automatic identification and dynamic path planning optimization, including: The computer vision system can be used to identify baggage tags in real time to improve recognition accuracy. The multi-head attention mechanism is combined with real-time data to generate and dynamically optimize the baggage transfer path to ensure efficient transportation. S6: Virtual luggage accompaniment service, including: Provide intelligent interactive services based on passenger needs, respond to passenger inquiries and feedback, and provide an immersive experience; S7: Cross-airport collaborative management execution, including: Real-time monitoring of weather, equipment and flight dynamics at airports around the world to establish risk warning models; when warnings are triggered, coordinate multi-airport resources, formulate and implement baggage transfer adjustment plans to ensure the stability and efficiency of the baggage transportation network.

2. The multi-airport baggage full-process tracking method according to claim 1 is characterized by: In the S2 data conversion step, the data encoding of the smart tag includes encoding the temperature data in Celsius with one decimal place, encoding the humidity data in percentage form, and encoding the acceleration data according to the directional components of the three-dimensional coordinate axes.

3. The multi-airport baggage full-process tracking method according to claim 1 is characterized by: The S4 smart tag and blockchain collaborative processing steps include: dynamically adjusting the collection frequency and accuracy of the smart tag sensor according to the transportation stage of the luggage; recording the key status information of the luggage through blockchain technology, and customizing the information feedback strategy according to the passenger type.

4. The multi-airport baggage full-process tracking method according to claim 1 is characterized by: The S5 automatic identification and dynamic path planning optimization steps include: using a multi-head attention mechanism to improve the recognition accuracy of baggage tags under different lighting, angles and stacking scenarios; dynamically generating and optimizing baggage transfer routes based on real-time flight dynamics and airport operation data. 5.Multi-airport baggage full-process tracking system, characterized by: include: A data acquisition module, used to execute the S1 step; A data conversion module, used to execute the step S2; Large model processing module with built-in multi-head attention mechanism for executing the S3 step; A smart label and blockchain collaboration module, used to execute the S4 step; An automatic identification and dynamic path planning module, used to execute the S5 step; A virtual baggage companion service module, used to execute the step S6; The cross-airport collaborative management module is used to execute the S7 step.

6. The multi-airport baggage full-process tracking system according to claim 5 is characterized by: The data conversion module includes: an encoding rule unit for encoding multi-source data; an embedding combination unit for semantically fusing the encoded data to generate sequence tokens suitable for large model processing.

7. The multi-airport baggage full-process tracking system according to claim 5 is characterized by: The large model processing module includes: a multi-head attention mechanism, which is used to focus on key parameters such as baggage timeliness, equipment load, resource reserves and weather impact; a model training unit, which uses federated learning and incremental learning methods to continuously optimize model performance to adapt to the dynamically changing airport operating environment.

8. The multi-airport baggage full-process tracking system according to claim 5 is characterized by: The smart tag and blockchain collaborative module includes: an environmental prediction unit, which predicts environmental changes that luggage may encounter based on machine learning and deep learning algorithms; an information feedback unit, which pushes luggage status information through multiple channels based on passenger portraits.

9. The multi-airport baggage full-process tracking system according to claim 5, characterized in that: The automatic identification and dynamic path planning module includes: a tag recognition model, which combines multi-head attention and deep neural network technology to improve the accuracy of luggage tag recognition; a path planning unit, which generates the optimal transfer path based on real-time data and dynamically adjusts the luggage transfer order to ensure priority processing of high-priority luggage.

10. The multi-airport baggage full-process tracking system according to claim 5, characterized in that: The cross-airport collaborative management module includes: a risk warning unit, which monitors the weather, equipment and flight dynamics of airports around the world in real time, and establishes and updates risk warning models; an emergency linkage unit, which coordinates multi-airport resources when the warning is triggered, formulates and implements baggage transfer adjustment plans; an execution supervision unit, which continuously monitors the execution status of each airport during the emergency handling process, allocates resources in a timely manner, and ensures the efficiency and smoothness of the emergency handling process.

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