Self-service luggage check-in control method and system based on code association

Abstract

The invention provides a self-service luggage check-in control method and system based on code association, and relates to the technical field of luggage self-service check-in. The method comprises the steps that the system receives information including passengers, flights, destinations, luggage weight and size and the like transmitted by a consignment station, an exclusive unique identification code is generated according to the information and is stored in association with luggage information, and the identification is used for accurately marking luggage. And then, capturing the real-time position of the luggage flowing in the airport by means of an Internet of Things sensor network, and updating the luggage state, the coverage position and the transportation and loading conditions associated with the unique identification code in the database according to the real-time position. Whether loading of the luggage is completed is judged based on the updated state, if yes, handover information containing a unique identification code is sent to the destination airport management end, and after the user takes away the luggage, the unique identification code state is marked as completion. By implementing the method, the dynamic state of the luggage can be accurately tracked, manual operation is reduced, and airport luggage consignment operation efficiency and passenger travel experience are effectively improved.

Description

Technical Field

[0001] This application relates to the technical field of self-service baggage check-in, and particularly to a self-service baggage check-in control method and system based on coding association. Background Art

[0002] With the rapid development of the air transportation industry, the volume of airport baggage check-in services has been continuously increasing. To improve service efficiency, major airports have launched self-service baggage check-in services, enabling passengers to complete the baggage check-in process independently and alleviating the pressure on the manual counters.

[0003] Currently, airport self-service baggage check-in systems usually adopt a combination of manual confirmation, barcode labels, and RFID tags. After the staff confirms the passenger's identity, a barcode label is printed and attached to the baggage. The system records the baggage information and assigns the baggage transportation route. During the baggage transportation process, the baggage location information is updated by means of regular manual inspections and scanning at fixed checkpoints, and the baggage handover is completed by manual label verification at the destination airport.

[0004] However, in the case where barcode labels are easily damaged or detached, the regular manual inspection method results in a lag in updating the baggage status information and cannot timely reflect the actual location of the baggage. At the same time, when the volume of baggage surges, the manual verification method is inefficient and easily causes baggage backlogs, affecting the operational efficiency of the airport. Summary of the Invention

[0005] This application provides a self-service baggage check-in control method and system based on coding association, which is used to improve the accuracy, safety, and overall operational efficiency of baggage transportation.

[0006] In a first aspect, the present application provides a self-service baggage check-in control method based on coding association, which is applied to an airport self-service baggage check-in control system. The method includes: receiving baggage-related information sent by the check-in desk end, where the baggage-related information includes passenger information, flight information, destination information, baggage weight and dimensions, and the check-in desk end is used to place the target checked baggage; generating a unique identification code according to the baggage-related information, and storing the unique identification code and the baggage-related information in a database to establish an association relationship, where the unique identification code is used to mark the corresponding target checked baggage; receiving real-time position data of the target checked baggage during the airport transportation process through an Internet of Things sensor network; updating the baggage status information associated with the unique identification code in the database according to the real-time position data, where the baggage status information includes the current position of the baggage, the transportation status and the loading status; judging whether the target checked baggage has been loaded according to the baggage status information; if so, sending baggage handover information including the unique identification code to the management end of the destination airport, where the baggage handover information is used for the destination airport to confirm and receive the target checked baggage; and after determining that the user picks up the target checked baggage at the destination airport, marking the unique identification code as the completed state.

[0007] By adopting the above technical solution, by using the unique identification code generated based on the baggage-related information, compared with the easily damaged and fallen-off barcode labels, it has higher stability and recognition, and is not prone to information reading obstacles, reducing the risk of information loss caused by identification problems from the root. Moreover, relying on the Internet of Things sensor network to receive the position data during the baggage transportation process in real time, abandoning the manual regular inspection method, it can immediately update the baggage status information in the database according to the position data, accurately reflecting the actual position of the baggage at all times and avoiding information lag. Also, when the number of baggages surges, relying on the system to automatically judge that the baggage loading is completed and send handover information to the destination airport, as well as automatically confirm that the user picks up the baggage and other processes, without manual verification, greatly improving the processing efficiency, effectively preventing baggage backlog, comprehensively ensuring the efficient and smooth operation of the airport, and effectively solving many drawbacks in the prior art.

[0008] Combined with some embodiments of the first aspect, in some embodiments, before the step of receiving the baggage-related information sent by the check-in desk end, it further includes: obtaining user identity authentication information sent by the check-in desk end; verifying the validity of the user identity authentication information according to a preset identity verification rule; when the verification result is valid, sending an identity verification passed information to the check-in desk end to trigger the check-in desk end to collect the baggage-related information.

[0009] By adopting the above technical solution, the identity authentication link can effectively prevent non-owners or illegal personnel from misusing the consignment permission, ensuring that the checked baggage process starts from a legitimate user. When the verification is successful, information is fed back to the consignment desk terminal, enabling it to collect baggage-related information, guaranteeing the rigor of the entire consignment process, reducing the probability of occurrence of errors and fraud in consignment events from the source, and making the airport self-service baggage consignment control system safer and more reliable.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating a unique identification code based on the baggage-related information, the following steps are further included: constructing an electronic file of the target checked baggage based on the baggage-related information, where the electronic file includes a consignment history record, an abnormal status mark, and customer feedback information; establishing an association relationship between the electronic file and the unique identification code; performing a risk level assessment on the target checked baggage according to the historical record in the electronic file to generate a risk assessment result; and adjusting the monitoring frequency of the Internet of Things sensor network according to the risk assessment result.

[0011] By adopting the above technical solution, on the one hand, the past consignment details of the baggage can be traced back through the associated file, quickly locating the root cause when problems occur; on the other hand, a risk level assessment is performed based on the historical record in the file, and the monitoring frequency of the Internet of Things sensor network is flexibly adjusted according to the assessment result, focusing on monitoring high-risk baggage and appropriately reducing the frequency for low-risk baggage, which not only ensures the safety of the baggage but also optimizes resource allocation, enhancing the intelligence and efficiency of the system.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining whether the target checked baggage has been loaded based on the baggage status information, the following steps are further included: obtaining the loading position information of the target checked baggage and the distribution status of the surrounding baggage; determining the unloading sequence requirement of the same-flight baggage based on the destination information of the target checked baggage; calculating the rationality score of the current loading position according to the unloading sequence requirement and the distribution status; when the rationality score is lower than a preset score threshold, generating target position information that optimizes the loading and unloading efficiency; and sending a position adjustment instruction containing the optimal target position information to the on-site operation terminal.

[0013] By adopting the above technical solution, after determining whether the target checked baggage has been loaded, the loading position information of the baggage and the distribution status of the surrounding baggage are comprehensively obtained, and the current loading rationality score is calculated in combination with the unloading sequence requirement of the same-flight baggage. If the score is lower than the threshold, optimized target position information is generated and a adjustment instruction is sent to the on-site operation terminal. In this way, the loading layout of the baggage can be planned in advance, avoiding time waste caused by unreasonable loading during unloading, enabling the unloading of the same-flight baggage to be orderly and efficient, reducing the operation obstacles for airport ground staff, overall improving the operation efficiency of the airport baggage handling link, and reducing labor and time costs.

[0014] In some embodiments in combination with some embodiments of the first aspect, the steps of determining that the user picks up the target checked baggage at the destination airport specifically include: obtaining the physical state information collected by a multiple sensor group arranged in the baggage claim area, where the physical state information includes gravity sensing data, infrared sensing data, and RFID reading data; collecting the facial features of the baggage pick-up personnel through a camera; when the physical state information changes, matching the facial features with the unique identification code of the corresponding baggage to obtain a matching result; judging whether the pick-up operation of the target checked baggage is reasonable according to the matching result; when the judgment result is unreasonable, triggering a on-site security prompt and warning.

[0015] By adopting the above technical solution, when determining that the user picks up the baggage at the destination airport, the physical state information is collected by means of a multiple sensor group in the baggage claim area, and at the same time, the facial features are collected through a camera. The two are combined to match the unique identification code to judge the rationality of the pick-up operation. Once an unreasonable pick-up is detected, a on-site security prompt and warning is immediately triggered. The multiple sensing data ensures accurate capture of the change in the baggage state, and the facial feature matching ensures the accuracy of the identity of the pick-up person. The two cooperate to effectively prevent risks such as wrong pick-up and theft of baggage, escort the safety of passengers' baggage at the destination end, and enhance the passengers' trust in the airport baggage check-in service.

[0016] In some embodiments in combination with some embodiments of the first aspect, after the step of generating a unique identification code according to the baggage-related information, the following steps are further included: determining that the target checked baggage has the characteristics of valuable items through the user terminal; determining the baggage risk level according to the characteristics of the valuable items; dynamically adjusting the sampling frequency of the Internet of Things sensors based on the risk level; when the risk level exceeds a preset threshold, automatically triggering an artificial review process.

[0017] By adopting the above technical solution, after generating the unique identification code, if the user terminal determines that the baggage has the characteristics of valuable items, the baggage risk level is immediately determined based on this characteristic, and then the sampling frequency of the Internet of Things sensors is dynamically adjusted. When the risk exceeds the threshold, the artificial review process is automatically triggered. Increasing the monitoring frequency for valuable items can keep track of their location dynamics in real time and reduce the risks of loss and damage; the artificial review process provides double protection. Professional personnel's review can timely detect potential hazards that the system may overlook, comprehensively protect the safety of valuable baggage, and highlight the system's fine management ability for special baggage.

[0018] In some embodiments in combination with some embodiments of the first aspect, after the step of receiving real-time location data of the target checked baggage during the airport transportation process through the Internet of Things sensor network, the method further includes: periodically detecting the communication status with the Internet of Things sensor network, and when a communication anomaly is detected, starting a data caching module to temporarily store newly received real-time location data, and at the same time triggering a data retransmission mechanism, where the data caching module is used to retransmit the cached location data in chronological order after the communication is restored.

[0019] By adopting the above technical solution, the data caching module can retransmit the cached location data in chronological order when the communication is restored, avoiding the interruption of baggage location tracking due to communication failures, ensuring that the entire baggage transportation trajectory is coherent and traceable, enabling airport staff to accurately master the baggage dynamics at any time and anywhere, maintaining the smoothness of the baggage check-in process, and improving the system stability and reliability.

[0020] In a second aspect, the present application provides an airport self-service baggage check-in control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the airport self-service baggage check-in control system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium, including instructions, which when running on the airport self-service baggage check-in control system, enable the airport self-service baggage check-in control system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product, which when running on the airport self-service baggage check-in control system, enables the airport self-service baggage check-in control system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Since the technical means of verifying the validity of the user identity authentication information before receiving the check-in information and triggering subsequent processes based on the verification result are adopted, the technical problem in the prior art that it is impossible to ensure the legality of the checked baggage personnel and it is easy to have the problem of misusing the check-in authority is effectively solved. Furthermore, the technical effect of ensuring the rigor of the airport self-service baggage check-in control system from the source, reducing the occurrence probability of errors and fraudulently checked baggage events, and improving the system security and reliability is achieved.

[0024] 2. Since the technical means of calculating the loading rationality score by comprehensively considering the loading position, the surrounding luggage distribution and the unloading sequence requirements of the same flight after determining that the luggage loading is completed, and generating and sending a position adjustment instruction according to the score are adopted, the technical problem in the prior art that the luggage loading lacks planning, resulting in low unloading efficiency and wasting manpower and time costs is effectively solved. Furthermore, the technical effect of optimizing the luggage loading layout in advance and improving the operation efficiency of the airport luggage handling link is achieved.

[0025] 3. Since the technical means of determining the luggage risk level according to the characteristics of valuable items feedback by the user terminal, dynamically adjusting the sampling frequency of the Internet of Things sensor and triggering the manual review process as needed are adopted, the technical problem in the prior art that there is a lack of targeted and refined control over valuable luggage and potential safety hazards are likely to occur is effectively solved. Furthermore, the technical effect of comprehensively protecting the safety of valuable luggage and highlighting the fine control ability of the system for special luggage is achieved. Brief Description of the Drawings

[0026] Figure 1 is a flowchart of a self-service luggage consignment control method based on coding association in an embodiment of the present application; Figure 2 is another flowchart of a self-service luggage consignment control method based on coding association in an embodiment of the present application; Figure 3 is a schematic structural diagram of an entity device of an airport self-service luggage consignment control system in an embodiment of the present application. Detailed Embodiments

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0029] For ease of understanding, the following describes the process of the method provided in this embodiment. Please refer to Figure 1, which is a schematic flowchart of a self-service baggage check-in control method based on coding association in an embodiment of the present application.

[0030] S101. Receive baggage-related information sent by the check-in desk end, where the baggage-related information includes passenger information, flight information, destination information, baggage weight, and dimensions, and the check-in desk end is used to place the target checked baggage. The airport self-service baggage check-in control system first needs to establish a stable and efficient data transmission channel with the check-in desk end. The system combines 5G communication technology and a wired network. When a passenger places the baggage on the check-in desk end, the high-precision weighing sensor and laser dimension measuring instrument equipped at the check-in desk end start to work. The weighing sensor uses the principle of pressure induction to convert the baggage weight into an electrical signal and transmit it to the control chip at the check-in desk end; the laser dimension measuring instrument measures the time from the emission of the laser beam to its reflection back to the receiver, and calculates the length, width, and height data of each side of the baggage in combination with the speed of light.

[0031] The control chip at the check-in desk end performs preliminary processing and integration on the collected data. At the same time, the passenger inputs passenger information, such as name, ID number, contact information, etc., through the interactive screen at the check-in desk end, and can also input flight information, including flight number, departure time, etc., and destination information by scanning the boarding pass or manually. The control chip packs these baggage weight, dimensions, passenger information, flight information, and destination information into a data packet in a specific format and sends it to the airport self-service baggage check-in control system through the established data transmission channel.

[0032] To ensure the accuracy and integrity of data transmission, the system adopts data verification technology, such as the CRC (Cyclic Redundancy Check) algorithm. Before the check-in desk end sends the data, a CRC check calculation is performed on the data packet, a check code is generated and appended to the end of the data packet. After the airport self-service baggage check-in control system receives the data packet, a CRC check calculation is performed again, and the calculation result is compared with the received check code. If the two are consistent, it means that no error has occurred during data transmission; if they are inconsistent, the system automatically sends a retransmission request to the check-in desk end, asking to resend the data packet until the data verification passes.

[0033] In addition, to ensure data security and prevent information leakage, the system adopts encryption transmission technology, such as the AES (Advanced Encryption Standard) encryption algorithm. At the check-in desk end, the data packet is encrypted using the AES algorithm, and the plaintext data is converted into ciphertext before transmission. After the airport self-service baggage check-in control system receives the ciphertext data, it uses the corresponding key to decrypt it and restore the original baggage-related information.

[0034] In some embodiments, when a passenger is ready to use the airport self-service baggage check-in service, operations are performed at the check-in counter end to input or submit relevant information that can prove their identity. This information is transmitted through the network and acquired by the airport self-service baggage check-in control system. For example, a passenger may complete the submission of identity verification information by swiping their ID card, entering their ID number, scanning their boarding pass, or manually entering information such as their name and contact information on the interactive screen at the check-in counter end. The check-in counter end packages and sends this information to the control system. After receiving the user identity verification information, the airport self-service baggage check-in control system verifies it according to the pre-set identity verification rules. These rules may include, but are not limited to, whether the information format is correct (such as the number of digits and format of the ID number conforming to the specification), whether the information content matches the existing passenger information in the system (such as comparing with the information reserved when the passenger purchased the ticket), and whether the passenger has any violation records, etc. The system compares and judges the input information with the internal database or relevant authentication systems to determine whether the identity verification information is true and valid. If, after verification, the user identity verification information is valid, the control system sends an identity verification passed message to the check-in counter end through the network. After receiving this information, the check-in counter end will start the operation process of collecting baggage-related information. At this time, the high-precision weighing sensor and laser size measuring instrument equipped at the check-in counter end start to work, measuring the weight and size data of the baggage respectively. At the same time, the passenger can also input other relevant information about the baggage at the check-in counter end, such as the number of baggage pieces and whether there are special items. These baggage-related information will be collected and prepared to be transmitted to the airport self-service baggage check-in control system, preparing for subsequent operations such as generating a unique identification code and establishing a baggage information association. This process effectively prevents non-genuine or illegal personnel from misusing the check-in authority by verifying the user's identity at the beginning stage of the check-in process, ensuring the rigor of the entire check-in process.

[0035] S102. Generate a unique identification code according to the baggage-related information, and store the unique identification code and the baggage-related information in the database to establish an association relationship, where the unique identification code is used to mark the corresponding target checked baggage; After receiving the baggage-related information, the airport self-service baggage check-in control system generates a unique identification code using a QR code generation algorithm. The unique identification code is not set on the baggage but is directly bound and associated with the corresponding baggage. The system adopts a unique identification code generation strategy based on time stamps and random numbers. The time stamp records the precise time when the information is generated, and the random number ensures the uniqueness of the unique identification codes generated at the same time. Package the baggage-related information, such as passenger information, flight information, destination information, baggage weight and size, according to specific coding rules, then combine the encoded information with the time stamp and random number, and generate a unique identification code through the unique identification code generation library.

[0036] In terms of storage, the system uses a distributed database such as CockroachDB. The distributed database has high scalability and fault tolerance, which can meet the needs of storing a large amount of luggage data at the airport. The generated unique identification code is used as the primary key in the database table, and the luggage-related information is used as the fields in the table to establish a one-to-one correspondence relationship. For example, create a table named "luggage_info" that contains fields such as "qr_code" (unique identification code), "passenger_info" (passenger information), "flight_info" (flight information), "destination_info" (destination information), "weight" (luggage weight), and "size" (luggage size). Insert the unique identification code and the corresponding luggage-related information into this table. Establish an association relationship between the unique identification code and the luggage-related information in the database. Through the unique identification code, the corresponding luggage-related information can be quickly queried, and vice versa. This association relationship provides convenience for subsequent luggage tracking, querying, and management.

[0037] In some embodiments, when the airport self-service baggage check-in control system receives baggage-related information, it constructs an electronic file based on this information. The check-in history records in detail the time of each baggage check-in, flight number, departure location, destination, checked weight and dimensions, etc. This data accumulates from the first check-in of the baggage to form a complete check-in trajectory. The abnormal status mark is used to record special situations that occur during the baggage check-in process. For example, problems such as baggage damage, detention, or misrouting during transportation will be promptly marked and recorded in the electronic file, including the time, location, and relevant situation description of the abnormality. The customer feedback information comes from the opinions and suggestions provided by passengers through various channels (such as the airport service desk, online feedback platforms, etc.) during the check-in process, including the experience of the check-in process and the satisfaction with baggage handling. The system integrates this information to form an electronic file of the target checked baggage, providing comprehensive data support for subsequent management. The system binds the electronic file to the unique identification code, establishing a close association between the two. In the distributed database, using the unique identification code as an index, the storage address or relevant data link of the electronic file is corresponding to it. In this way, by scanning or querying the unique identification code, the system can quickly and accurately obtain the associated electronic file, realizing the rapid traceability and management of baggage information. The system evaluates the risk level of the target checked baggage based on the historical records in the electronic file. The evaluation model will comprehensively consider multiple factors, such as the occurrence frequency and severity of abnormal status in the check-in history. If a piece of baggage has multiple occurrences of damage or misrouting, its risk level will increase accordingly; while if the check-in history is relatively good, the risk level will be relatively low. At the same time, customer feedback information will also be included in the evaluation scope. If customers repeatedly feedback problems with the baggage during the check-in process, it will also affect the risk level. The system conducts quantitative analysis on these factors through specific algorithms and finally generates a risk assessment result. The risk level can be divided into different levels such as low risk, medium risk, and high risk, and each level corresponds to different risk characteristics and management strategies. For different risk assessment results, the system will dynamically adjust the monitoring frequency of the Internet of Things sensor network. For high-risk baggage, in order to pay closer attention to its status and prevent problems such as loss or damage, the system will increase the monitoring frequency of the Internet of Things sensors. For example, originally, the location data was collected every 5 minutes, and for high-risk baggage, it may be adjusted to be collected every 1 minute to ensure real-time monitoring of its location changes and transportation status. For low-risk baggage, on the premise of ensuring basic monitoring, the monitoring frequency is appropriately reduced to optimize resource utilization and reduce the system data processing pressure. In this way, the system can reasonably allocate monitoring resources, improve the overall operation efficiency while ensuring the safety of the baggage.

[0038] In some embodiments, after this step, in certain implementation scenarios of the airport self-service baggage check-in process, when a series of previous baggage check-in operations are completed, the system can use user terminals (such as mobile phone apps, self-service check-in terminal devices, etc.) to determine whether the target checked baggage has characteristics of valuable items. When handling the check-in, passengers can perform relevant operations on the user terminal, such as checking the option that the baggage contains valuable items, or directly entering relevant information about the valuable items, such as the item name, approximate value, quantity, etc.

[0039] In addition, the system can also use technical means such as image recognition and weight detection to assist in the judgment. For example, if the user uploads photos of the items inside the baggage to the terminal, the system can use image recognition technology to identify whether there are valuable items, such as jewelry, high-end electronic products, etc. At the same time, considering the weight of the baggage, if it exceeds the weight range of ordinary baggage of the same type, it may also imply the presence of valuable items.

[0040] After determining that the target checked baggage has characteristics of valuable items, the system will determine the risk level of the baggage based on these characteristics. The specific determination method is as follows: Value of valuable items: The higher the value of the item, the higher the risk level of the baggage usually is. For example, if the checked baggage contains jewelry worth tens of thousands of yuan, its risk level will be significantly higher than that of a baggage containing only some ordinary clothes and daily necessities. Vulnerability of valuable items: If the valuable items are vulnerable items, such as fragile artworks, precision optical instruments, etc., then the risk level of this baggage will also increase accordingly. Because these items are more likely to be damaged during the check-in process. Importance of valuable items: For some valuable items with special significance or irreplaceability, such as ancestral cultural relics, important business documents, etc., their risk levels will be higher. The system will comprehensively consider these factors and use a preset algorithm model to classify different risk levels for the baggage, such as low risk, medium risk, and high risk.

[0041] IoT sensors play a crucial role in real-time monitoring of the status of checked luggage in the airport baggage check-in system. The system dynamically adjusts the sampling frequency of these sensors according to the previously determined luggage risk levels. Low-risk luggage: For luggage with a relatively low risk level, the sensors can collect data at a regular sampling frequency, which can not only meet the basic monitoring requirements but also reduce the energy consumption and data processing pressure of the system. For example, data such as the location, temperature, and humidity of the luggage are collected every 5 - 10 minutes. Medium-risk luggage: When medium-risk luggage is identified, the system moderately increases the sampling frequency of the sensors to more timely grasp the status changes of the luggage. For instance, the sampling frequency is adjusted to collect data every 2 - 5 minutes. High-risk luggage: For high-risk luggage, the system significantly increases the sampling frequency to achieve real-time or near-real-time monitoring of the luggage status. For example, data is collected every minute or even shorter intervals to ensure a rapid response in case of abnormal situations.

[0042] To further ensure the safety of high-risk luggage, the system sets a preset risk level threshold. When the risk level of the luggage exceeds this threshold, the system automatically triggers a manual review process. The system sends the detailed information of the luggage, including the characteristics of valuable items, the basis for risk assessment, the data collected by the sensors, etc., to the airport staff. The staff will conduct further inspections and confirmations based on this information, such as checking whether the packaging of the luggage meets the requirements and whether additional protective measures are needed. At the same time, the staff will also communicate with the passengers to verify the relevant information of the valuable items to ensure the safety and reliability of the check-in process.

[0043] Through the above series of operations, the airport self-service baggage check-in system can more accurately manage luggage of different risk levels, while ensuring the safety of passengers' valuable items, improving the overall check-in efficiency and service quality.

[0044] S103. Receive the real-time location data of the target checked luggage during its transportation in the airport through the IoT sensor network; Without relying on the devices inside the luggage, the existing airport facilities and advanced technologies can be utilized to locate the luggage. For example, with the help of the airport's surveillance cameras, computer vision technology, signal positioning technology, and geomagnetic positioning technology are used to accurately locate the luggage. At the same time, the accuracy and reliability of the positioning are improved through data fusion and analysis. Specifically, the airport has reasonably arranged and installed a large number of high-definition surveillance cameras in various key areas, such as the check-in area, sorting area, transportation channels, boarding gates, etc. In the check-in area, cameras are installed above the luggage placement platforms, at the starting ends of the conveyor belts, etc., to ensure that the images of passengers placing luggage and the luggage entering the conveyor belt can be clearly captured; the cameras in the sorting area are distributed around each sorting port and sorting equipment to comprehensively monitor the sorting process of the luggage; in the transportation channels, cameras are installed at certain intervals on the top or both side walls of the channels to ensure that the luggage is always within the monitoring range during the entire transportation process; the cameras at the boarding gates are aimed at the luggage loading area and the storage location of the luggage to be loaded. When using computer vision technology to analyze the surveillance video images in real time, the system will first identify the shape characteristics, color, size, etc. of the luggage. For the shape characteristics, the system will extract the contour information of the luggage, such as common shapes like cuboids and cylinders, and compare it with a pre-trained model. In terms of color recognition, the system will convert the color information of the luggage into digital features, such as RGB values or HSV values, to distinguish luggage of different colors. Size recognition is achieved by calculating the pixel area occupied by the luggage in the image and converting it into the actual size in combination with the parameters and installation positions of the cameras.

[0045] After identifying the characteristics of the luggage, the system will track it. By matching the characteristics of the luggage in consecutive video frames, the position of the luggage in each frame is determined, thus realizing continuous tracking of the luggage. For example, when the luggage moves on the conveyor belt, the system will continuously search for objects with the same characteristics in the subsequent video frames and update its position information.

[0046] To improve the recognition accuracy, the system uses deep learning algorithms to train a large number of luggage images. First, luggage images of various types, colors, and sizes are collected to build a large-scale image dataset. Then, deep learning models such as convolutional neural networks (CNNs) are used to train the dataset. During the training process, the model will continuously adjust its own parameters to improve the ability to recognize the characteristics of the luggage. For example, by learning a large number of luggage images under different angles and lighting conditions, the model can better adapt to various complex environments and accurately identify the luggage.

[0047] When the luggage moves within the field of view of multiple cameras, multi-camera collaborative positioning technology is adopted. The system will match and fuse the images captured by different cameras to achieve continuous tracking of the luggage position. Specifically, when the luggage enters the field of view of another camera from the field of view of one camera, the system will match the luggage based on its features in the images of the two cameras to determine its corresponding positions under different cameras. Then, combining the position and angle information of each camera, the actual position coordinates of the luggage are calculated through methods such as triangulation. For example, assume there are two cameras A and B with known positions and angles. When the luggage appears in the fields of view of both cameras simultaneously, the system will calculate the distances and angles of the luggage relative to the two cameras based on the positions of the luggage in the images of the two cameras and the parameters of the cameras, and then determine its position in the three-dimensional space.

[0048] S104. Update the luggage status information associated with the unique identification code in the database according to the real-time position data, where the luggage status information includes the current position of the luggage, the transportation status, and the loading status; After receiving the real-time position data of the target checked luggage transmitted by the Internet of Things sensor network, the airport self-service luggage check-in control system will quickly and accurately update the luggage status information associated with the unique identification code in the database.

[0049] When updating the current position of the luggage, the system will first preprocess the received position data. Since the sensor data may be subject to noise interference, the system uses the Kalman filter algorithm to denoise the position data. This algorithm can, based on the predicted value of the position at the previous moment and the measured value at the current moment, through continuous iterative optimization, obtain a more accurate position estimate value. For example, when the luggage moves in the transportation channel, even if the sensor occasionally has signal fluctuations, the Kalman filter algorithm can ensure the stability and accuracy of the position data. The processed position data will be updated into the database accurately according to the established data format and specifications. At the same time, the system will also combine the Geographic Information System (GIS) technology to mark the position information of the luggage on the electronic map in real time, facilitating the staff to intuitively view the specific position of the luggage.

[0050] For the update of the transportation status, the system sets multiple status tags, such as "Pending Transportation", "In Transportation", "Transportation Paused", "Transportation Abnormal", etc. The system determines the transportation status of the luggage based on the real-time location changes of the luggage and the preset transportation route planning. If the luggage moves along the preset route and passes through each key node within a reasonable time, the system will mark the transportation status as "In Transportation". On the contrary, if the luggage stays at a certain position for a long time, exceeding the normal transportation time interval, the system will interact with the transportation equipment control system to troubleshoot the reason. If it is found that the cause is a transportation equipment failure, such as the conveyor belt stopping running, the system will update the transportation status to "Transportation Paused" and send an alarm notification to the maintenance personnel in a timely manner, informing the failure location and the unique identification code of the involved luggage. If the luggage position shows abnormal jumps or deviates from the preset route, the system will mark the transportation status as "Transportation Abnormal", start the emergency handling process, and dispatch staff to the scene to check the situation.

[0051] In terms of updating the loading status, the system will conduct real-time data interaction with the loading equipment at the boarding gate. When the luggage arrives at the designated area near the boarding gate, the system updates the loading status to "Pending Loading". During the loading process, by reading the sensor data of the loading equipment, such as the cargo counter, position sensor, etc., the system can obtain the real-time loading progress of the luggage. For example, when the loading equipment shows that a certain number of luggage have been loaded and the target checked luggage moves forward step by step in the loading queue, the system will update the loading status to "Loading". When the loading equipment completes the loading operation of all luggage on the same flight and sends a loading completion signal to the system, the system will update the loading status of the target checked luggage to "Loading Completed".

[0052] S105. Judge whether the target checked luggage has been loaded according to the luggage status information; Based on the updated luggage status information, the airport self-service luggage check-in control system adopts various strategies to judge whether the target checked luggage has been loaded.

[0053] The system will focus on the loading status information of the luggage. When the loading status shows "Loading Completed", this is the primary basis for judgment, but the system will not rely solely on this information. To ensure the accuracy of the judgment, the system will verify from multiple dimensions. The system will conduct data comparison with the loading management system of the aircraft cargo hold. The loading management system of the aircraft cargo hold will record the luggage loading situation in each cargo hold area in real time, including information such as the number of luggage and the unique identification code. The system matches the unique identification code of the target checked luggage with the data in the cargo hold loading management system. If the match is successful and the cargo hold loading management system shows that the loading operation in this area has been completed, this will further confirm that the luggage has been loaded.

[0054] The system will also make a judgment in combination with the location information of the luggage. If the location of the target checked luggage shows that it is already in the aircraft cargo hold, and the loading status of the surrounding luggage is also completed, and at the same time the aircraft door has been closed, these pieces of information corroborate each other, which can strongly support the judgment that the luggage has been loaded. For example, by determining the precise location of the luggage in the cargo hold through the Internet of Things sensor network and combining the information fed back by the door closing sensor, the system can more accurately judge the loading situation of the luggage.

[0055] In addition, machine learning algorithms in artificial intelligence can be used to optimize the judgment of loading completion. The system collects a large amount of historical luggage loading data, including the type, weight, size, flight information, loading time, loading location, etc. of the luggage, and constructs a training data set. Machine learning algorithms such as decision trees and support vector machines are used to train the training data set to establish a loading completion judgment model. During the actual judgment process, the model will comprehensively consider multiple factors, such as the time when the luggage arrives at the boarding gate, the time interval during the loading process, the expected departure time of the flight, etc., to predict whether the luggage has been loaded. When the prediction result of the model is inconsistent with the current judgment result of the system, the system will issue a warning to prompt the staff to further verify the situation. This method can improve the accuracy and intelligence level of the judgment, discover potential loading problems in advance, such as loading delays and luggage omissions, and take timely measures to handle them to ensure the normal operation of the flight.

[0056] In some embodiments, after this step, the airport self-service baggage check-in control system needs to further understand the specific situation of the target checked baggage in the cargo hold. Through various sensors installed in the cargo hold, the system can accurately obtain the loading position information of the target checked baggage, specifying which area of the cargo hold, the specific shelf position, or the stacking level it is in. At the same time, these sensors can also detect the distribution status of the baggage surrounding the target checked baggage, including the number of surrounding baggage, the placement direction, and the spacing between them. Each piece of checked baggage has its corresponding destination information. After arriving at the destination airport, the baggage needs to be unloaded in a certain order to improve the loading and unloading efficiency and ensure the smooth progress of subsequent processes. The system identifies all the baggage on the same flight going to this destination based on the destination information of the target checked baggage. Then, in combination with the loading and unloading processes and rules of the airport, it determines the unloading order requirements for these baggage on the same flight. For example, for some baggage that needs to be transferred preferentially, or baggage that needs to be unloaded in advance according to the partition of the destination terminal building, there will be corresponding preferential unloading orders. After mastering the unloading order requirements and the distribution status of the surrounding baggage, the system will evaluate the rationality of the current loading position of the target checked baggage and calculate the corresponding score. The system will set a preset score threshold for judging whether the current loading position is reasonable. When the rationality score of the target checked baggage is lower than this threshold, it indicates that there is a problem with the current loading position and adjustment is needed. At this time, the system will use an optimization algorithm, comprehensively considering factors such as unloading order requirements, cargo hold space structure, and surrounding baggage distribution, to generate an optimal target position information that takes into account the best loading and unloading efficiency. This target position can maximize the satisfaction of the unloading order requirements, improve the loading and unloading efficiency, and at the same time ensure the reasonable use of the cargo hold space. Once the optimal target position information is determined, the system will send a position adjustment instruction to the on-site operation terminal (such as the handheld terminal device used by airport staff). This instruction contains the target position information that the target checked baggage needs to move to. After receiving the instruction, the on-site staff will adjust the position of the target checked baggage according to the instruction content, moving it from the current position to the optimal target position, thereby optimizing the loading layout of the baggage in the cargo hold and improving the overall loading and unloading efficiency.

[0057] S106. If so, send the baggage handover information containing the unique identification code to the management terminal of the destination airport, where the baggage handover information is used for the destination airport to confirm and receive the target checked baggage; When the airport self-service baggage check-in control system determines that the target checked baggage has been loaded, it will execute the key step of sending the baggage handover information to the destination airport management terminal. Before sending the information, the system will strictly sort and encrypt the baggage handover information. In addition to the unique identification code of the target checked baggage, the handover information will also integrate key data such as passenger information, flight information, destination information, and the weight and size of the baggage. To ensure the security and integrity of information transmission, the system uses an asymmetric encryption algorithm, such as the RSA algorithm. The handover information is encrypted using the public key of the destination airport management terminal, and only the management terminal with the corresponding private key can decrypt and read the information, effectively preventing the information from being stolen or tampered with during transmission.

[0058] The information sending relies on a stable and reliable communication network. The system comprehensively uses 5G networks and dedicated lines for data transmission. 5G networks have the characteristics of high speed and low latency, and can quickly send a large amount of baggage handover information; the dedicated line network provides a dedicated channel for data transmission, ensuring the stability and security of data transmission. During data transmission, the system will use data verification technologies, such as hash verification, to generate a hash value for the sent data and compare it with the hash value calculated by the receiving end. If the two are consistent, it indicates that the data transmission is correct; if not, the system will automatically trigger the retransmission mechanism to resend the data.

[0059] After reaching the destination airport management terminal, the management terminal system will decrypt and verify the received information. First, use the private key to decrypt the encrypted handover information to obtain the baggage-related data therein. Then, by comparing with the flight information, passenger information, etc. in its own database, confirm the accuracy and integrity of the information. If the verification passes, the management terminal system will update the status of the baggage to "awaiting receipt" and notify the relevant staff to make preparations for receipt.

[0060] S107. After determining that the user has picked up the target checked baggage at the destination airport, mark the unique identification code as the completed status.

[0061] This step will be described in detail in S201 to S205 and will not be elaborated here.

[0062] In the above embodiments, from receiving the baggage-related information at the check-in counter, generating a unique identification code and establishing an associated storage, to using the Internet of Things sensor network to track the baggage location in real time and update the status information, then to intelligently judge the completion of baggage loading, safely and efficiently complete the baggage handover and accurately confirm the pick-up, it realizes the digital, intelligent and efficient management of the entire process of baggage check-in, effectively solving the problems of easy damage and shedding of barcodes and cumbersome and inefficient manual operations in traditional check-in methods.

[0063] After combining the above content, the following is a more specific process description of the method provided in this embodiment. Please refer to Figure 2 , which is another process schematic diagram of the self-service baggage check-in control method based on coding association in the embodiment of the present application.

[0064] S201. Obtain the physical state information collected by the multiple sensor groups set in the baggage claim area. The physical state information includes gravity sensing data, infrared sensing data, and RFID reading data; The airport self-service baggage check-in control system deploys multiple sensor groups in the baggage claim area to obtain the physical state information during the baggage claim process in real time, providing key data support for subsequent judgment of the rationality of the baggage claim operation.

[0065] In terms of obtaining gravity sensing data, a high-precision gravity sensor is selected and installed on the baggage placement platform in the baggage claim area. The gravity sensor works based on the piezoelectric effect or strain gauge principle. When the baggage is placed on the platform, the gravity causes the sensitive element inside the sensor to deform, which in turn causes changes in electrical parameters such as resistance or capacitance. These changes are converted into standard electrical signals, such as voltage or current signals, through the signal conditioning circuit inside the sensor, and then transmitted to the control system in a wired or wireless manner.

[0066] The acquisition of infrared sensing data depends on infrared sensors. Multiple infrared sensors are installed at key positions in the baggage claim area, such as around the baggage and at the entrance of the passage. Infrared sensors are divided into active and passive types. The active infrared sensor emits infrared rays, and when the infrared rays are reflected back by an object, the sensor receives the reflected signal; the passive infrared sensor directly detects the infrared rays emitted by objects such as humans. No matter which type of sensor it is, when detecting changes in infrared rays, corresponding electrical signals will be generated. The system will amplify, filter, and process these electrical signals to remove noise interference and improve the signal quality. In addition, by using the array layout of infrared sensors, it is possible to achieve full-range monitoring of the baggage claim area and accurately judge the position and movement direction of personnel.

[0067] In addition, data can also be read through RFID. An RFID tag containing a unique identification code is pasted on each checked baggage, and multiple RFID readers are set up in the baggage claim area. The RFID reader communicates with the RFID tag through radio frequency signals. When the tag enters the working range of the reader, the reader emits a radio frequency signal of a specific frequency. After receiving the signal, the tag is activated and transmits information such as the stored unique identification code back to the reader. After receiving the response signal from the tag, the reader demodulates and decodes the signal, obtains the information therein, and transmits it to the control system through the network. To improve the accuracy and efficiency of RFID reading, a multi-reader collaborative working method is adopted to cover different positions in the baggage claim area and avoid reading blind spots. At the same time, the design of the RFID tag is optimized to improve its anti-interference ability and ensure stable data transmission in a complex environment.

[0068] S202. Collect the facial features of the baggage pick-up personnel through the camera; In the baggage claim area, the airport self-service baggage check-in control system uses a high-definition camera to collect the facial features of the baggage pick-up personnel, providing an important basis for subsequent identity matching and judgment of the rationality of operations.

[0069] The system selects a high-resolution and low-illumination high-definition camera to ensure that the facial images of personnel can be clearly captured under different lighting conditions. The camera is installed in a suitable position to ensure that the face of the baggage pick-up personnel can be completely photographed, avoiding occlusion or incomplete shooting. At the same time, automatic focusing and image anti-shake functions are configured to keep the captured images always clear and stable.

[0070] When someone approaches the baggage to prepare for pick-up, the camera starts to work and collects the video stream in real time. The system converts the analog video signal captured by the camera into a digital signal through an image acquisition card and transmits it to the computer for processing. In the image processing stage, first, image preprocessing techniques are used, including operations such as grayscale conversion, noise reduction, and contrast enhancement, to improve the quality of the image and highlight the facial features. Then, advanced face recognition algorithms are adopted, such as face recognition technology based on convolutional neural network (CNN). The CNN model is trained with a large number of face images and can automatically learn the feature representation of the face. By performing operations such as convolution and pooling on the preprocessed image, the key features of the face, such as the shapes, positions, and relative proportions of the eyes, nose, and mouth, are extracted to form a set of feature vectors.

[0071] To improve the accuracy and efficiency of face recognition, the system also adopts the method of collaborative multi-camera acquisition and distributed computing. Multiple cameras are installed in the baggage claim area to collect facial images of personnel from different angles. By integrating information from multiple perspectives, facial features can be obtained more comprehensively, reducing recognition errors caused by facial occlusion or pose changes. At the same time, using distributed computing technology, the face recognition task is assigned to multiple computing nodes for parallel processing, improving the processing speed and ensuring that facial feature acquisition and extraction can be completed quickly and accurately even in the case of a large passenger flow.

[0072] S203. When the physical state information changes, match the facial feature with the unique identification code of the corresponding baggage to obtain a matching result. When the airport self-service baggage check-in control system detects changes in the gravity sensing data, infrared sensing data, and RFID reading data in the baggage claim area, the system determines that a baggage may be being claimed, and then initiates the matching process of the facial feature with the unique identification code of the corresponding baggage.

[0073] The system first obtains the unique identification code of the currently operated baggage from the RFID reading data. Since the unique identification code of each baggage is generated at the beginning of the check-in process and is closely associated with the baggage information and stored in the database, through this identification code, the system can quickly locate the corresponding passenger information, which includes the pre-collected and stored passenger facial feature information.

[0074] In the facial feature matching link, the system uses an advanced face recognition algorithm. As mentioned above, when collecting facial features, the convolutional neural network (CNN) is used to extract the key features of the face and convert them into feature vectors. Now, the system compares the currently collected facial feature vector of the baggage claimant with the facial feature vector of the corresponding passenger in the database. Common similarity calculation methods such as cosine similarity measure the similarity by calculating the cosine value of the angle between two vectors. The closer the cosine value is to 1, the more similar the two facial feature vectors are, that is, the higher the matching degree of the facial features between the baggage claimant and the baggage owner.

[0075] S204. According to the matching result, judge whether the extraction operation of the target checked baggage is reasonable. Based on the matching result of the facial feature and the unique identification code obtained in S203, the airport self-service baggage check-in control system judges the rationality of the extraction operation of the target checked baggage.

[0076] If the facial features match the unique identification code of the corresponding luggage successfully and the similarity score exceeds a preset threshold (e.g., 80%, which can be adjusted according to actual security requirements), the system determines that the extraction operation is reasonable. This means that the person extracting the luggage is likely to be the owner of the luggage. The system will further confirm the subsequent steps of the luggage extraction process, such as updating the status of the luggage in the database and preparing to mark the unique identification code as completed.

[0077] If the matching result shows that the similarity is lower than the preset threshold, the system will not immediately determine that the extraction operation is unreasonable. Instead, it will initiate a secondary verification process. The system will check the accuracy of the sensor data again, such as rereading the gravity sensor data, infrared sensor data, and RFID reading data, to confirm whether the luggage is really being extracted and whether the extraction behavior is still in progress. At the same time, it will re-analyze the facial features and compare them with other similar facial features in the database to check for misjudgments caused by similar features.

[0078] If the matching result is still not satisfactory after the secondary verification, the system determines that the extraction operation is unreasonable. This situation may indicate abnormal behaviors such as misappropriating luggage or theft. The system will record the detailed information of this abnormal extraction event, including the extraction time, the unique identification code of the luggage, the facial image of the extracting person (even if the match is not successful, it is saved for subsequent investigation), the physical state information of the extraction area, etc. These information will be stored in a dedicated abnormal event database for subsequent analysis and processing.

[0079] S205, when the judgment result is unreasonable, trigger the on-site security prompt and warning.

[0080] When the airport self-service luggage check-in control system determines that the extraction operation of the target checked luggage is unreasonable, it will immediately initiate a series of security prompt and warning measures to ensure the safety of passengers' luggage and the order of the airport.

[0081] The system first triggers the sound and light alarms installed in the luggage extraction area. These alarms will emit loud alarm sounds and flashing lights to attract the attention of on-site staff and surrounding passengers, playing a role in deterring potential lawbreakers. At the same time, the system will push the detailed information of the abnormal situation, including the unique identification code of the luggage, the facial image of the extracting person, the time and location of the abnormality, etc., to the airport's security monitoring center and the handheld terminal devices of on-site staff.

[0082] In the security monitoring center, staff can view the relevant information of the abnormal extraction event in real time through the monitoring large screen and quickly locate the monitoring screen of the incident location to monitor the on-site situation in real time. Staff can take corresponding measures in a timely manner according to the on-site situation, such as dispatching security personnel to the scene for handling to prevent possible luggage theft.

[0083] In the embodiments of the present application, by deploying multiple sensors and cameras in the baggage claim area, physical state information and facial features are collected and subjected to matching analysis, realizing precise monitoring and risk prevention of the baggage claim operation, effectively solving the problems in the traditional baggage claim process such as difficulty in accurately judging the identity of the claimant and easy occurrence of wrong taking or theft of baggage, greatly improving the security of passengers' baggage claim and the reliability of airport baggage management, and enhancing passengers' trust in the airport baggage consignment service.

[0084] The airport self-service baggage consignment control system in the embodiments of the present invention will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the airport self-service baggage consignment control system in the embodiments of the present application.

[0085] It should be noted that Figure 3 the structure of the airport self-service baggage consignment control system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0086] As Figure 3 shown, the airport self-service baggage consignment control system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage section 308 into the random access memory (RAM) 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0087] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. The drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0088] Specifically, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are executed.

[0089] It should be noted that specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device.

[0090] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the block may occur in a different order from that marked in the accompanying drawings.

[0091] Specifically, the airport self-service baggage check-in control system of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, it implements the self-service baggage check-in control method based on encoding association provided in the above-mentioned embodiment.

[0092] On the other hand, the present invention also provides a computer-readable storage medium. This storage medium may be included in the airport self-service baggage check-in control system described in the above-mentioned embodiment; or it may exist alone and not be assembled into the airport self-service baggage check-in control system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the airport self-service baggage check-in control system, the airport self-service baggage check-in control system is enabled to implement the self-service baggage check-in control method based on encoding association provided in the above-mentioned embodiment.

[0093] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0094] As used in the above embodiments, depending on the context, the term "when..." may be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" may be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0095] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as ROM or random access memory RAM, magnetic disks, or optical discs.

Claims

1. A self-service baggage check-in control method based on coding association, which is applied to an airport self-service baggage check-in control system, and is characterized in that, The method includes: Receiving luggage-related information sent by the check-in desk side, where the luggage-related information includes passenger information, flight information, destination information, luggage weight and dimensions, and the check-in desk side is used to place the target checked luggage; Generating a unique identification code according to the luggage-related information, and storing the unique identification code and the luggage-related information in a database to establish an association relationship, where the unique identification code is used to mark the corresponding target checked luggage; Receiving real-time location data of the target checked luggage during the airport transportation process through the Internet of Things sensor network; Updating the luggage status information associated with the unique identification code in the database according to the real-time location data, where the luggage status information includes the current location of the luggage, transportation status and loading status; Judging whether the target checked luggage has been loaded according to the luggage status information; If so, sending luggage handover information including the unique identification code to the management side of the destination airport, where the luggage handover information is used for the destination airport to confirm and receive the target checked luggage; After determining that the user picks up the target checked luggage at the destination airport, marking the unique identification code as the completed status.

2. The method according to claim 1, wherein Before the step of receiving the luggage-related information sent by the check-in desk side, it further includes: Obtaining user identity authentication information sent by the check-in desk side; Verifying the validity of the user identity authentication information according to the preset identity verification rules; When the verification result is valid, sending an identity verification passed message to the check-in desk side to trigger the check-in desk side to collect luggage-related information.

3. The method according to claim 1, wherein After the step of generating a unique identification code according to the luggage-related information, it further includes: Based on the luggage-related information, constructing an electronic file of the target checked luggage, where the electronic file includes a check-in history record, an abnormal status mark and customer feedback information; Establishing an association relationship between the electronic file and the unique identification code; Conducting a risk level assessment on the target checked luggage according to the historical records in the electronic file to generate a risk assessment result; Adjusting the monitoring frequency of the Internet of Things sensor network according to the risk assessment result.

4. The method according to claim 1, wherein After the step of judging whether the target checked luggage has been loaded according to the luggage status information, it further includes: Obtaining the loading position information of the target checked luggage and the distribution status of the surrounding luggage; Based on the destination information of the target checked luggage, determining the unloading sequence requirement of the same flight luggage; Calculating the rationality score of the current loading position according to the unloading sequence requirement and the distribution status; When the rationality score is lower than the preset score threshold, generating target position information that considers the optimal handling efficiency; Sending a position adjustment instruction including the optimal target position information to the on-site operation terminal.

5. The method according to claim 1, wherein In the step of determining that the user picks up the target checked luggage at the destination airport, it specifically includes: Obtaining physical state information collected by a multiple sensor group set in the luggage claim area, where the physical state information includes gravity sensing data, infrared sensing data and RFID reading data; Collecting the facial features of the luggage pick-up personnel through a camera; When the physical state information changes, match the facial features with the unique identification code of the corresponding luggage to obtain a matching result; Judge whether the extraction operation of the target checked luggage is reasonable according to the matching result; When the judgment result is unreasonable, trigger a on-site security prompt warning.

6. The method according to claim 1, characterized in that, After the step of generating the unique identification code according to the luggage-related information, it further includes: Determine that the target checked luggage has the characteristics of valuable items through the user terminal; Determine the luggage risk level according to the characteristics of valuable items; Dynamically adjust the sampling frequency of the Internet of Things sensor based on the risk level; When the risk level exceeds the preset threshold, automatically trigger an artificial review process.

7. The method according to claim 1, wherein After the step of receiving the real-time position data of the target checked luggage during airport transportation through the Internet of Things sensor network, it further includes: Periodically detect the communication status with the Internet of Things sensor network. When communication anomalies are detected, start the data cache module to temporarily store the newly received real-time position data, and at the same time trigger a data retransmission mechanism, where the data cache module is used to resend the cached position data in chronological order after communication is restored.

8. An airport self-service baggage check-in control system, characterized in that, The airport self-service luggage check-in control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the airport self-service luggage check-in control system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the airport self-service luggage check-in control system, enable the airport self-service luggage check-in control system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the airport self-service luggage check-in control system, enable the airport self-service luggage check-in control system to execute the method according to any one of claims 1-7.

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