BSM message-based luggage data concurrent processing method

Abstract

The invention relates to a luggage data concurrent processing method based on a BSM message, and belongs to the technical field of data processing. Comprising the steps of executing intelligent prediction and thread resource scheduling based on multi-dimensional context sensing and adaptive learning; the luggage messages are received and analyzed, corresponding luggage data information is obtained, the luggage key data information at least comprises passenger information, luggage numbers, flight dates, flight numbers, leg information and luggage message types of passengers, the luggage message types are judged, and different types of luggage messages are sent to different thread pools; different types of luggage messages are processed based on a distributed lock technology, and luggage message data information is stored and updated to a database; according to the method, efficient and accurate processing of the luggage BSM message is realized through a multi-thread processing and distributed lock technology; and the problems of data conflict and dirty reading are effectively prevented.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a method for concurrent processing of baggage data based on BSM messages. Background Art

[0002] A Baggage Source Message (BSM) is a standard format message containing data items such as flight information, passenger information, and basic baggage information, which is sent by the departure control or check-in system when staff receive or perform business operations on passengers with baggage. During the message processing, operations such as adding, deleting, or modifying messages are involved. The message data is highly concurrent during the processing. In a high-concurrency scenario, different status messages of the same piece of baggage may arrive at the system simultaneously and need to be processed, and some shared resources (such as baggage data and flight segment data in the database) need to be accessed or modified simultaneously. If not controlled, it may lead to disorderly message processing, and further cause problems such as data loss, conflicts, or inconsistencies. Therefore, the present invention provides a method for concurrent processing of baggage data based on BSM messages to solve the above problems. Summary of the Invention

[0003] To overcome the problems mentioned in the background art, the present invention provides a method for concurrent processing of baggage data based on BSM messages. The present invention realizes efficient and accurate processing of baggage BSM messages through multi-threaded processing and distributed lock technology; effectively prevents data conflicts and dirty read problems.

[0004] To achieve the above object, the present invention is realized through the following technical solutions: A method for concurrent processing of baggage data based on BSM messages, characterized in that: the method includes the following steps: Step S101: Perform intelligent prediction and thread resource scheduling based on multi-dimensional context awareness and adaptive learning; specifically including the following sub-steps: Step S101a: Real-time aggregate multi-modal input data from multiple heterogeneous data sources, where the data sources at least include real-time baggage message data and real-time performance metrics within the system itself, and the real-time performance metrics include key data such as the number of CPU cores, available memory size, disk I / O performance, and network bandwidth; Step S101b: Process and embed the multi-modal input data within a predetermined time interval to generate a sequence of state vector representations that can characterize the current comprehensive state of the system; Step S101c: Use a sequence encoder including a self-attention mechanism to process the sequence of state vector representations, learn and capture the dynamic correlations and temporal dependencies between different time points and different data dimensions in the sequence of state vector representations, and generate a system state embedding containing rich context information; Step S101d: Process the system state embedding through the multi-head prediction module to perform feature extraction, fusion, and transformation to generate prediction data; and process the multi-modal input data in real time through the predefined system bottleneck to generate system bottleneck data; Step S101e: Process the state vector representation sequence through the self-attention mechanism in combination with the above prediction data and system bottleneck data to generate an intelligent resource allocation strategy and automatically and reasonably allocate threads; Step S102: Send different types of baggage messages to the corresponding thread pools according to the allocation strategy in Step S101e; and receive and parse the baggage messages in parallel through multiple threads to obtain the corresponding baggage data information, and determine the type of the baggage message. The baggage message processing method includes addition, deletion, and modification; Step S103: Set a reasonable normal processing time for the distributed lock in Step S102, and process different types of baggage messages based on the distributed lock technology, and store and update the baggage message data information in the database.

[0005] Further, the method for processing different types of baggage messages based on the distributed lock technology specifically includes the following steps: First, according to the complexity of the baggage message and the performance of the airport baggage handling system, in combination with historical data and the prediction results in Step S101d, dynamically evaluate the expected time for processing a single baggage message, and set a reasonable normal processing time for the distributed lock; Second, when processing the message, the thread to be processed first sends a request to the distributed lock system to obtain the lock. If the acquisition is successful, the thread accesses the data safely and processes the message. If the processing is successfully completed within the normal processing time, the thread notifies the distributed lock system to release the lock; If the lock acquisition fails, the thread needs to wait until the lock is released and then acquire the lock again; If the thread successfully acquires the lock but fails to release the lock normally due to an exception within the set normal processing time, it will be processed according to the preset exception rules.

[0006] Further, the sub-steps further include the following steps: Step S101f: Continuously monitor the real-time performance metrics inside the system itself, compare and analyze the real-time performance metrics with the prediction data of the multi-head prediction module, and use the analysis results through the closed-loop feedback mechanism to continuously update and optimize the sequence encoder, multi-head prediction module, and predefined system bottleneck.

[0007] Further, the multi-modal input data further includes flight status and schedule data provided by the Flight Information System (FIS) and airport operation status data provided by the Airport Operations Database (AODB).

[0008] Further, the method for processing newly added messages includes: Receive the data information of the newly added luggage message, and check whether the luggage data information exists in the database according to the key data information of the luggage message, and judge the message status, that is, whether it exists; If it does not exist, directly store the luggage message data information in the database and add new luggage information; If it exists, but some information is missing from the original luggage message data, update the missing data information in the original luggage message data according to the data rules in the newly added message for the missing information; If it exists, but has been soft-deleted, physically delete the original luggage message data information and then add the luggage message data information.

[0009] Further, the method for processing message modification is: Receive the data information for modifying the luggage message, and search for the corresponding luggage message data information in the database according to the key data information of the luggage message, that is, search for the luggage message; Search for the corresponding luggage message and update the changed fields of the corresponding luggage message data information.

[0010] Further, the method for processing message deletion is: Receive the data information for deleting the luggage message, and check whether the luggage message data information exists in the database according to the key data information of the luggage message, and judge the message status, that is, whether it exists; If it does not exist, ignore the message deletion; If it exists, soft-delete the luggage message data information.

[0011] Further, the processing of adding, modifying, and deleting the luggage message includes the processing of adding, modifying, and deleting the transit luggage message.

[0012] Further, the methods for processing the addition, modification, and deletion of the transit luggage message include: Split the data information of the transit luggage message by flight segment, and then perform addition, modification, and deletion.

[0013] Further, the database also includes a luggage statistics table, a flight statistics table, a flight segment statistics table, and a transit luggage statistics table.

[0014] The beneficial effects of the present invention: The present invention realizes the efficient and accurate processing of baggage messages through multi-threaded processing and distributed lock technology. It performs addition, deletion, and modification operations on baggage messages (including transit baggage messages), improving the automation and intelligent management. By using multi-threaded technology and distributed locks, it precisely controls concurrent access to data, effectively preventing data conflicts and dirty read problems. At the same time, in combination with the database, it realizes the fast access and real-time update of core information such as baggage status, flight statistics, and leg records. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the baggage message processing flow of the method of the present invention.

[0016] Figure 2 It is a schematic diagram of the specific processing flow of various types of baggage messages of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] In order to make the objectives, technical solutions, and beneficial effects of the present invention clearer, the preferred embodiments of the invention will be described in detail below with reference to the accompanying drawings for the convenience of those skilled in the art to understand.

[0018] As Figure 1-2 , the present invention discloses a method for concurrent processing of baggage data based on BSM messages. To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for concurrent processing of baggage data based on BSM messages, characterized in that: the method includes the following steps: Step S101: Execute intelligent prediction and thread resource scheduling based on multi-dimensional context awareness and adaptive learning; specifically including the following sub-steps: Step S101a: Aggregate multi-modal input data from multiple heterogeneous data sources in real time. The data sources at least include real-time baggage message data. For the baggage message data, a dedicated message middleware can be deployed, and a message queue (such as RabbitMQ, etc.) is used to listen for and obtain the latest baggage messages, receiving RFID tag identification data, baggage sorting status data, etc. from the baggage handling system. The multi-modal input data also includes flight status and schedule data provided by the Flight Information System (FIS), and airport operation status data provided by the Airport Operations Database (AODB). Through a secure data interface, key information such as flight departure and arrival times, gate assignment, and flight delay warnings is synchronized through the Flight Information System (FIS); the Airport Operations Database (AODB) obtains operation status data such as runway usage, ground traffic scheduling, and passenger flow density in the waiting area through database log real-time capture technology. And the real-time performance metrics within the system itself, the real-time performance metrics include key data such as the number of CPU cores, available memory size, disk I / O performance, and network bandwidth; for the real-time performance metrics within the system itself, through system call interfaces provided by the operating system (such as using the / proc file system in the Linux system to obtain information such as the number of CPU cores and available memory size), as well as disk I / O performance monitoring tools (such as iostat, etc.) and network bandwidth monitoring tools (such as nload, etc.) to collect key data on the number of CPU cores, available memory size, disk I / O performance (including read and write speeds, I / O request queue length, etc.), and network bandwidth (such as current bandwidth utilization, upload and download speeds, etc.) in real time.

[0019] For the collected baggage message data, flight status data, airport operation status data, and system performance metric data in different formats, use a data conversion tool to convert them into a unified data format (such as a standardized JSON format). Establish a data buffer to temporarily store the converted data in the buffer for subsequent real-time aggregation. Set an expiration time for the data in the buffer to ensure data timeliness. Use a stream processing framework (such as Apache Flink, etc.) to read data from the buffer in real time, sort it according to timestamps, and aggregate the multi-modal input data from different data sources to form a comprehensive data set containing baggage message data, flight status data, airport operation status data, and system performance metrics.

[0020] Step S101b: Process and embed the multi-modal input data at a predetermined time interval to generate a sequence of state vector representations that can characterize the current comprehensive state of the system.

[0021] According to the actual requirements of the system and the frequency of data changes, a predetermined time interval (such as 10 seconds, 30 seconds, etc.) is set as the time window. The time window mechanism is used to divide the comprehensive data set in the stream processing framework, and each time window contains multi-modal input data within that time period. For the baggage message data within each time window, key features are extracted, such as the weight, size, and consignment priority of the baggage. For flight status data, key features are extracted, such as the proximity of flight departure and arrival times, flight delay time, etc.; for airport operation status, key features are extracted, such as runway usage, ground traffic scheduling, etc.; for performance metric data, key features are extracted, such as the change rate of CPU utilization, the percentage of remaining memory space, etc. The extracted features are normalized to convert feature values with different ranges and magnitudes into a unified interval (such as [0, 1]) to improve the accuracy of subsequent processing. An embedding layer (such as the embedding layer implemented in deep learning frameworks TensorFlow or PyTorch) is used to convert the normalized feature data into a low-dimensional vector representation. Each feature corresponds to an embedding vector, and these embedding vectors are combined together to form a state vector representation that can characterize the current comprehensive state of the system. The state vector representations within each time window are arranged in chronological order to generate a sequence of state vector representations.

[0022] Step S101c: Process the sequence of state vector representations using a sequence encoder that includes a self-attention mechanism (Self-Attention Mechanism) to learn and capture the dynamic correlations and temporal dependencies between different time points and different data dimensions in the sequence of state vector representations, and generate a system state embedding containing rich context information.

[0023] Use a deep learning framework to construct a sequence encoder that includes a self-attention mechanism (such as a Transformer encoder). This encoder consists of multiple self-attention layers and feed-forward neural network layers. In the self-attention layer, by calculating the attention scores between each vector in the sequence of state vector representations and other vectors, the dynamic correlations between different time points and different data dimensions are learned. The attention scores reflect the degree of correlation between two vectors. The feed-forward neural network layer performs further non-linear transformations on the output of the self-attention layer to enhance the expressive power of the model. The sequence of state vector representations is input into the sequence encoder. The self-attention layer first calculates the attention scores for each state vector in the sequence, and then performs weighted summation on the state vectors according to the attention scores to obtain the context representation of each state vector. After being processed by multiple self-attention layers and feed-forward neural network layers, a system state embedding containing rich context information is generated.

[0024] Step S101d: Process the system state embedding through a multi-head prediction module for feature extraction, fusion, and transformation to generate prediction data for message payload prediction such as addition, modification, deletion, transfer, and urgency classification; and process the multi-modal input data in real time through predefined system bottlenecks to generate system bottleneck data.

[0025] Construct a multi-head prediction module composed of multiple parallel prediction heads. Each prediction head is responsible for extracting different feature information from the system state embedding. Inside each prediction head, there are components such as convolutional layers and fully connected layers for feature extraction, fusion, and transformation of the input system state embedding. Input the system state embedding into each prediction head respectively. Each prediction head extracts local features through convolutional layers and performs feature fusion and transformation through fully connected layers to generate different feature representations. Concatenate or weighted fuse the feature representations generated by multiple prediction heads to obtain the final prediction feature representation. Use a fully connected layer to process the final prediction feature representation to generate prediction data. The prediction data can include predictions of future system performance (such as changes in CPU utilization, memory usage, etc.) and predictions of luggage message processing (such as processing time, processing priority, etc.).

[0026] Predefine system bottlenecks such as insufficient CPU core count, memory exhaustion, disk I / O performance bottlenecks, etc. Monitor the performance metric data in the multi-modal input data in real time. When a certain performance metric reaches the predefined bottleneck threshold, record the relevant data and generate system bottleneck data in combination with the luggage message data. For example, when the CPU utilization reaches more than 85% and the luggage message processing speed decreases, generate system bottleneck data including the current CPU state and the backlog of luggage messages.

[0027] Step S101e: Process the state vector representation sequence through the self-attention mechanism combined with the above prediction data and system bottleneck data to generate an intelligent resource allocation strategy for automatically and reasonably allocating threads.

[0028] Fuse the prediction data and system bottleneck data with the sequence of state vector representations. The prediction data and system bottleneck data can be used as additional feature vectors and concatenated with each vector in the sequence of state vector representations to form a new sequence of state vector representations. Use the self-attention mechanism to process the new sequence of state vector representations. Calculate the attention scores between each state vector and other vectors, and perform weighted summation on the state vectors according to the attention scores to obtain the new context representation of each state vector. By analyzing the new context representation, identify the parts of the system with resource tension and possible optimization directions. Generate an intelligent resource allocation strategy based on the new context representation. For example, if it is found that the load of a certain thread pool is too high while other thread pools have idle resources, then allocate some baggage messages to the idle thread pools. During the strategy generation process, consider factors such as the type, priority of the baggage messages, and system performance metrics to ensure the rationality and efficiency of resource allocation. Send the generated intelligent resource allocation strategy to the thread management module for automatic and reasonable thread allocation. If it is predicted that the data volume will increase significantly, the number of threads can be increased in advance; conversely, if it is predicted that the data volume will decrease, the number of threads can be appropriately reduced. This can avoid situations where the system has insufficient processing power or resource waste when the data volume suddenly changes.

[0029] Step S101f: Continuously monitor the real-time performance metrics inside the system itself, compare and analyze the real-time performance metrics with the prediction data of the multi-head prediction module, and use the analysis results to continuously update and optimize the sequence encoder, multi-head prediction module, and predefined system bottleneck through a closed-loop feedback mechanism.

[0030] Step S102: Send different types of baggage messages to the corresponding thread pools according to the allocation strategy in step S101e; and receive and parse the baggage messages in parallel through multiple threads to obtain the corresponding baggage data information, and determine the type of baggage message. The baggage message processing methods include addition, deletion, and modification. Establish multiple thread pools, and each thread pool is responsible for processing specific types of baggage messages. Set a reasonable number of threads for each thread pool and dynamically adjust the number of threads according to the performance and load conditions of the system. Receive the intelligent resource allocation strategy generated in step S101e, and send different types of baggage messages to the corresponding thread pools according to the baggage message type and target thread pool specified in the strategy. During the sending process, the sending time and target thread pool information of the baggage messages can be recorded for subsequent tracking and management.

[0031] Threads in the thread pool receive and parse the baggage messages in parallel. The corresponding baggage data information is obtained, and the baggage data information includes at least the key information of the baggage data (passenger information, baggage number, flight date, flight number, flight segment information, and baggage source message type). During the parsing process, the legitimacy of the baggage message can be checked, such as checking whether the format of the baggage message is correct and whether the fields are complete. According to the parsed baggage data information, the type of the baggage message is judged. The baggage message processing methods include addition (such as adding a baggage consignment record), deletion (such as deleting an incorrect baggage record), and modification (such as modifying the destination information of the baggage). The judgment result can be recorded in the system log for subsequent auditing and querying.

[0032] Step S103: Set a reasonable normal processing time for the distributed lock in step S102, and process different types of baggage messages based on the distributed lock technology, and store and update the baggage message data information into the database, where the database includes multiple of MySQL database, Redis database, and MongoDB database, which is convenient for data processing and real-time statistics.

[0033] The method for processing different types of baggage messages based on the distributed lock technology specifically includes the following steps: First, according to the complexity of the baggage message and the performance of the airport baggage handling system, combined with historical data and the prediction result of step S101d, dynamically evaluate the expected time for processing a single baggage message, and set a reasonable normal processing time for the distributed lock; Second, when processing the message, the thread to be processed first sends a request to the distributed lock system to obtain the lock. If the acquisition is successful, the thread accesses the data safely and processes the message. If the processing is successfully completed within the normal processing time, the thread notifies the distributed lock system to release the lock; if the lock acquisition fails, the thread needs to wait until the lock is released and then acquire the lock again; if the thread successfully acquires the lock but fails to release the lock normally due to an exception within the set normal processing time range, it is processed according to the preset exception rules.

[0034] For the setting of the reasonable normal processing time of the distributed lock, it can be set according to the complexity of the baggage message and the performance of the airport baggage handling system, combined with historical data and the prediction results of step S101d. For messages containing only regular baggage information, the normal processing time can be set shorter, while for baggage messages involving special situations (such as oversize, fragile items, etc.) or having other special markings and with longer processing times, the normal processing time can be extended. To ensure that under normal circumstances, the message can be processed before the lock expires. For some baggage message processing tasks with long and uncertain processing times, a longer normal processing time can be set and combined with a heartbeat detection mechanism. For example, the thread holding the lock sends a heartbeat signal to the distributed lock service every few seconds, indicating that it is still processing the message and needs to continue holding the lock. If the lock service does not receive a heartbeat signal for a long time, it is considered that there is an abnormality in the message processing, and the lock is automatically released so that other threads can process it.

[0035] After the message is sent, the message status can be saved for each message: such as: "sent, pending processing", "processing", or "processing exception", etc. When processing the message, the pending thread first sends a request to the distributed lock system to obtain the lock. If the acquisition is successful, the thread accesses the data safely, processes the message, and updates the message status to "processing". And if it is successfully processed within the processing time range, the thread notifies the distributed lock system to release the lock and processes the next thread; when the thread fails to obtain the lock, that is, the pending message with the message status of "sent, pending processing", the thread needs to wait until the lock is released and then obtain the lock again to process the message; if the thread successfully obtains the lock, but there is an abnormality in the message processing and the lock is not released normally within the normal processing time range, it is marked as an exception, the message status is updated to "processing exception", and the lock is automatically released to process the next thread's message. For the message data with "processing exception", the message can be sent to the database for abnormal storage and wait for reprocessing. When reprocessing, manual intervention can be carried out. The source message data can be obtained from the database and compared with the message data with "processing exception" to check the reasons for the processing exception, such as whether the data information is lost, illegally added, illegally tampered with, or other reasons, which is convenient for analyzing the reasons for the exception and optimizing the system.

[0036] During the message processing process, the possible abnormal situations and processing rules are as follows: If the luggage message processing is abnormally interrupted, when an abnormal interruption occurs, the system can automatically retry, and set reasonable retry times and retry time intervals. The retry times can be set to 2 - 4 times, and the retry time interval can adopt an exponential back-off strategy, that is, the first retry interval is 1 second, the second is 2 seconds, the third is 4 seconds, and so on, to avoid excessive pressure on the system caused by a large number of repeated requests in a short period. At the same time, ensure that the luggage message processing operation is idempotent. For example, when updating the luggage status information, use a unique luggage identifier and version number to ensure that multiple processes will not produce duplicate or incorrect results. For luggage messages that still cannot be successfully processed after multiple retries, the system should mark them as "processing exception".

[0037] If the distributed lock operation fails due to a short network interruption, the system should automatically re-initiate the lock request and message processing operation after the network is restored. During the waiting period for the network to recover, the system can temporarily store the unprocessed luggage messages in the local cache and record the relevant status information. When the network is restored, attempt to acquire the lock and process the messages in sequence according to the records in the cache. At the same time, a reasonable waiting time should be set. If the network has not recovered within a certain period of time, the operation and maintenance personnel should be notified in a timely manner to troubleshoot and repair the network failure.

[0038] Distributed locks implement mutually exclusive access to shared resources in a distributed system to avoid data conflicts. Nodes obtain locks by creating unique temporary nodes. If successful, they hold the lock; if failed, they wait for the lock to be released. Distributed locks can solve the problem of concurrent message processing, ensuring that only one thread or process processes a specific task at the same time. In the high-concurrency scenario of luggage messages, different status messages of the same piece of luggage may arrive simultaneously and need to be processed. Multiple processing threads need to access or modify shared resources (such as luggage data and flight segment data in the database) at the same time. Without control, it may lead to disorderly message processing, and then cause problems such as data loss, conflicts, or inconsistencies. Introduce distributed locks to coordinate access conflicts between different processing threads, and use them to ensure that only one thread can access the shared resources at the same moment, thus avoiding data competition and deadlocks. Through distributed locks, ensure that only one thread can obtain the lock and process the message at the same time, so as to achieve orderly message processing and ensure data accuracy and consistency. At the same time, multi-threading technology ensures that the lock can be released in a timely manner after the message processing is completed, so that other threads can continue to process subsequent messages.

[0039] The types of the luggage messages described above include message addition, modification, and deletion. CHG represents a modification message, DEL represents a deletion message, and the absence of CHG and DEL represents an addition message. The following is a specific description: BSM Luggage Message Field Information and Format: Start Item: BSM Message status item: CHG means modifying the message, DEL means deleting the message, and the absence of this line means a new message is added.

[0040] .V / Version and Supplementary Data .F / Departure flight information .I / Transit arrival flight information .O / Transfer flight information .N / Luggage tag information .M / Globally Unique Identifier .D / Baggage check-in location information .S / Passenger baggage confirmation data .H / Operation location information .W / Data on number of pieces of luggage, weight, size, type, etc. .P / Passenger Name .G / Ground Shipping Information .Y / Frequent Traveler Number .C / Name of the company or group of the passenger who checked in the baggage .L / PNR record number .T / Luggage tag printer ID .E / Types of special baggage, including crew baggage / express baggage / urgent transfer baggage, etc. .R / Baggage Instructions .X / Baggage security information End item: ENDBSM Such as the following message: BSM .V / 1LKMG .F / GS5566T / 22NOV / LJG / Y .N / 3826678200001 .D / KMG / / 22NOV / 0346 / / KMG1969 .S / Y / 37A / C / 001 / / Y .W / K / 1 / 9 .P / 1TESA .T / 2130 ENDBSM If the BSM message does not have a message status item line, the baggage message is a newly added message.

[0041] The message new processing method includes: Receive the newly added baggage message data information, search the database for the baggage data information according to the key data information of the baggage message, and determine the baggage message status, that is, whether it exists; If it does not exist, it indicates that this is brand-new luggage information. Directly store the luggage message data information in the database, add the new luggage information, and complete the addition operation of the luggage information. At the same time, log information such as the time of the addition operation and the operator can be recorded for subsequent traceability.

[0042] If it exists, but some information is missing from the original luggage message data, update the missing data information in the original luggage message data according to the data rules in the new message; for example, if the luggage weight information is missing from the original luggage message data and the new luggage message contains this field, the system will update the missing weight information to the corresponding field in the original luggage message data according to the format, unit, etc. rules of the luggage weight field in the new luggage message. After the update is completed, operation logs can also be recorded, indicating the specific fields and contents updated.

[0043] If it exists, but has been soft-deleted (usually by setting a specific status field flag, such as "deleted = 1"), physically delete the original luggage message data information and then add the new luggage message data information. Completely remove the original luggage message data record from the database table. Subsequently, store the newly received luggage message data information in the database according to the normal addition process, complete the re-addition of the luggage information, and record a complete operation log, including the detailed information of the deletion and addition.

[0044] The method for processing message modification is as follows: Receive the modified luggage message data information, and find the corresponding luggage message data information in the database according to the key data information of the luggage message, that is, find the luggage message; Find the corresponding luggage message and update the changed fields of the corresponding luggage message data information. After successfully finding the corresponding luggage message, the system will check the changed fields in the modified luggage message one by one. For each field that needs to be modified, the original field value can be backed up first for data rollback in case of an exception. Then, update the changed fields of the corresponding luggage message data information in the database according to the new data provided in the modified luggage message. After the update is completed, update the operation time field and record log information such as the modifying operator and the modified content to ensure the traceability of data modification.

[0045] The method for processing message deletion is as follows: Receive the luggage message data information to be deleted, and find out whether there is such luggage message data information in the database according to the key data information of the luggage message, and judge the message status, that is, whether it exists; If it does not exist, ignore the baggage message deletion. If no record matching the key data information of the baggage message is found in the database, it indicates that the baggage message may have been deleted in advance or does not exist in the current system. At this time, the system ignores the baggage message deletion request and does not perform any operations, but can record the relevant information of the deletion request (such as request time, request source, etc.) to the operation log for subsequent analysis.

[0046] If it exists, perform a soft deletion on the baggage message data information. If the baggage message data information is found in the database, the system will perform a soft deletion operation. Usually, by updating the status field of the baggage message data record (such as changing the value of the "deleted" field from "0" to "1"), and recording the deletion time, deleting operator, etc. information to the corresponding fields in the database table, and at the same time, record the whole process of the deletion operation in detail in the operation log.

[0047] The processing of adding, modifying, and deleting the baggage message includes the processing of adding, modifying, and deleting the transit baggage message.

[0048] The methods for processing the addition, modification, and deletion of the transit baggage message include: Split the transit baggage message data information by flight segment and then perform addition, modification, and deletion. For the transit baggage message data information, the system splits it according to the flight segment information (such as key fields identifying the flight segment like flight number, departure and arrival airports, transit airport, etc.). The original transit baggage message data is split into multiple independent sub-message data according to different flight segments, and each sub-message data contains the baggage information of the corresponding flight segment. During the splitting process, ensure the integrity and accuracy of the data to avoid information loss or errors. After splitting, perform addition, modification, and deletion operations on each sub-message data respectively. Its operation process is the same as the above-mentioned methods for adding, modifying, and deleting ordinary baggage messages. During the operation process, record the detailed log of each sub-message operation, including operation type, operation time, flight segment information involved, etc., in order to comprehensively track the processing of transit baggage information in different flight segments and ensure the accuracy and efficiency of transit baggage information management.

[0049] The database also includes a luggage statistics table, a flight statistics table, a flight segment statistics table, and a transfer luggage statistics table. The luggage statistics table records luggage-related information, and can systematically count data such as the number, weight, size, destination, shipper, and recipient of each piece of luggage, so as to facilitate the tracking and management of the entire process of luggage transportation. The flight statistics table comprehensively covers various information of flights, such as flight number, flight name, departure time, arrival time, origin, destination, aircraft type, load factor, etc., and can help staff clearly understand the operation status and resource usage of flights. The flight segment statistics table carefully counts the flight segment information involved in each flight, including flight segment number, departure airport, stopover airport, arrival airport, flight distance, flight duration, etc., which helps to deeply analyze the flight route and operation efficiency of flights. In addition, there is a special transfer luggage statistics table, which mainly counts the relevant data of transfer luggage, such as the number of transfer luggage, original flight information, transfer flight information, transfer airport, time points of status changes of luggage during transfer, etc., providing accurate data basis for the efficient management and rapid transfer of transfer luggage.

[0050] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the invention and not to limit them. Although the 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 concurrent processing of baggage data based on BSM messages, characterized in that: The method described above includes the following steps: Step S101: Perform intelligent prediction and thread resource scheduling based on multi-dimensional context awareness and adaptive learning; specifically including the following sub-steps: Step S101a: Aggregate multi-modal input data from multiple heterogeneous data sources in real time. The data sources at least include real-time baggage message data and real-time performance metrics within the system itself. The real-time performance metrics include key data such as the number of CPU cores, available memory size, disk I / O performance, and network bandwidth. Step S101b: Process and embed the multi-modal input data within a predetermined time interval to generate a sequence of state vector representations that can characterize the current comprehensive state of the system. Step S101c: Use a sequence encoder containing a self-attention mechanism to process the sequence of state vector representations, learn and capture the dynamic correlations and temporal dependencies between different time points and different data dimensions in the sequence of state vector representations, and generate a system state embedding containing rich context information. Step S101d: Process the system state embedding through a multi-head prediction module to perform feature extraction, fusion, and transformation to generate prediction data; and process the multi-modal input data in real time through predefined system bottlenecks to generate system bottleneck data. Step S101e: Process the sequence of state vector representations through a self-attention mechanism combined with the above prediction data and system bottleneck data to generate an intelligent resource allocation strategy and automatically allocate threads reasonably. Step S102: Send different types of baggage messages to the corresponding thread pools according to the allocation strategy in Step S101e; and receive and parse the baggage messages in parallel through multiple threads to obtain corresponding baggage data information, and determine the types of baggage messages. The baggage message processing methods include addition, deletion, and modification. Step S103: Set a reasonable normal processing time for the distributed lock in Step S102, and process different types of baggage messages based on the distributed lock technology, and store and update the baggage message data information in the database.

2. The method for concurrent processing of baggage data based on BSM messages according to claim 1, wherein: The specific steps of the method for processing different types of baggage messages based on the distributed lock technology are as follows: First, based on the complexity of the baggage message and the performance of the airport baggage handling system, combined with historical data and the prediction results in Step S101d, dynamically evaluate the expected time for processing a single baggage message, and set a reasonable normal processing time for the distributed lock. Secondly, when processing the message, the thread to be processed first sends a request to the distributed lock system to obtain the lock. If the acquisition is successful, the thread can access the data safely and process the message. If the processing is successfully completed within the normal processing time, the thread notifies the distributed lock system to release the lock. If the lock acquisition fails, the thread needs to wait until the lock is released and then acquire the lock again. If the thread successfully acquires the lock but fails to release the lock normally due to an exception within the set normal processing time, it will be processed according to the preset exception rules.

3. The method for concurrent processing of baggage data based on BSM messages according to claim 1, wherein: The sub-steps also include the following steps: Step S101f: Continuously monitor the real-time performance metrics within the system itself, compare and analyze the real-time performance metrics with the prediction data of the multi-head prediction module, and continuously update and optimize the sequence encoder, multi-head prediction module, and predefined system bottlenecks through a closed-loop feedback mechanism using the analysis results.

4. The method for concurrent processing of baggage data based on BSM messages according to claim 1, wherein: The multi-modal input data further includes flight status and schedule data provided by the Flight Information System (FIS) and airport operation status data provided by the Airport Operations Database (AODB).

5. The method for concurrent processing of baggage data based on BSM messages according to claim 2, wherein: The method for processing newly added messages includes: Receiving newly added baggage message data information, searching the database for the existence of such baggage data information based on the key data information of the baggage message, and judging the message status, i.e., whether it exists. If it does not exist, directly store the baggage message data information into the database to add new baggage information. If it exists, but some information is missing from the original baggage message data, update the missing data information in the original baggage message data according to the data rules in the newly added message for the missing information. If it exists, but has been soft-deleted, physically delete the original baggage message data information and then add new baggage message data information.

6. The method for concurrent processing of baggage data based on BSM messages according to claim 2, wherein: The method for processing modified messages is: Receiving modified baggage message data information, searching the database for the corresponding baggage message data information based on the key data information of the baggage message, i.e., searching for the baggage message. Search for the corresponding baggage message and update the changed fields of the corresponding baggage message data information.

7. The method for concurrent processing of baggage data based on BSM messages according to claim 2, wherein: The method for processing deleted messages is: Receiving deleted baggage message data information, searching the database for the existence of such baggage message data information based on the key data information of the baggage message, and judging the message status, i.e., whether it exists. If it does not exist, ignore the message deletion. If it exists, soft-delete the baggage message data information.

8. The method for concurrent processing of baggage data based on BSM messages according to claim 2, characterized in that: The processing of adding, modifying, and deleting baggage messages includes the processing of adding, modifying, and deleting transit baggage messages.

9. The method for concurrent processing of baggage data based on BSM messages according to claim 8, wherein: The methods for processing the addition, modification, and deletion of transit baggage messages include: Splitting the transit baggage message data information by flight segment and then performing addition, modification, and deletion.

10. The method for concurrent processing of baggage data based on BSM messages according to claim 1, wherein: The database also includes a baggage statistics table, a flight statistics table, a flight segment statistics table, and a transit baggage statistics table.

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