Airport self-service luggage check-in intelligent identification system

Abstract

The invention relates to the technical field of intelligent transportation, and discloses an airport self-service baggage check-in intelligent identification system, which comprises a sensor module, a data processing module, a data processing module and a storage module, and is characterized in that the sensor module is used for acquiring physical state data of baggage; the data processing module is used for receiving the signal from the sensor and executing data preprocessing; the control module is used for generating position feedback according to the real-time luggage tracking data and providing a visual interface and abnormal alarm information; and the feedback module is used for transmitting feedback information to the control module and ensuring automatic processing of luggage tracking and abnormal management, and the data processing module predicts and updates the state of the luggage through Kalman filtering, Bayesian filtering and particle filtering algorithms. The luggage state is estimated in real time by adopting multi-sensor data fusion and advanced algorithms such as Kalman filtering, Bayesian filtering and particle filtering, and the system can accurately predict the position, speed and acceleration of luggage by integrating data of various sensors.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation, and specifically to an intelligent identification system for self-service baggage check-in at airports. Background Art

[0002] With the continuous increase in the demand for baggage check-in at modern airports, traditional baggage handling systems face many challenges. Existing technologies usually rely on RFID tags, X-ray scanners, and barcode scanners to track and security-check baggage. Although these technologies have improved the processing efficiency to a certain extent, there are still significant limitations.

[0003] Traditional RFID systems only rely on the information of baggage tags for identification. Generally, RFID tags can effectively identify the identity information of baggage. However, due to the possible occlusion, loss, or damage of the tags, the identification accuracy decreases, which makes the RFID system unable to provide highly reliable baggage tracking in complex environments. Especially when the position of the baggage is relatively concealed, traditional technologies are prone to misjudgment.

[0004] Existing X-ray scanning technologies are mainly used for the security check of baggage. Although it can detect internal items, it can only give a general overview and cannot accurately identify all potential threats. Moreover, the scanning efficiency and accuracy of X-ray machines are often affected by factors such as the type and density of items. For complex multi-layer items, X-ray scanners often produce false alarms or missed alarms, resulting in a huge workload for manual review and affecting the overall work efficiency. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent identification system for self-service baggage check-in at airports, which solves the problems of inaccurate baggage identification, isolated information, and lagged exception handling in the existing technology.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent identification system for self-service baggage check-in at airports, comprising: A sensor module for real-time collection of physical state data of baggage; A data processing module that receives signals from the sensors and performs data preprocessing; A control module for generating position feedback based on real-time baggage tracking data, providing a visualization interface and exception alarm information; A feedback module for transmitting feedback information to the control module to ensure the automated processing of baggage tracking and exception management.

[0007] Preferably, the data processing module predicts and updates the state of the luggage through Kalman filtering, Bayesian filtering, and particle filtering algorithms to estimate the real-time position, speed, and acceleration of the luggage. The data processing module includes a data fusion module for Kalman filtering, Bayesian filtering, and particle filtering, which is used to process the luggage state data collected by multiple sensors to generate an optimal estimated value.

[0008] Preferably, the Kalman filtering algorithm is used to make a real-time prediction of the current position, speed, and acceleration of the luggage based on the state transition matrix, control matrix, and control input of the luggage, in combination with the process noise matrix and the observation noise matrix, and to update the state based on the observation data.

[0009] Furthermore, the Bayesian filtering algorithm calculates the posterior probability distribution of the luggage state recursively. This algorithm is particularly suitable for dealing with non-linear and high-noise data environments. Bayesian filtering is used to continuously optimize the estimation of the luggage state based on multiple observation data and historical state information to ensure accurate prediction results even in an environment with high uncertainty.

[0010] Preferably, the Bayesian filtering algorithm calculates the posterior probability distribution of the luggage recursively using the following formula: p(x k |z 1:k )∝p(z k |x k )·p(x k |x k-1 ) where x k is the state vector of the luggage, z k is the observation data at time k, p(x k |x k-1 ) is the state transition probability, and p(z k |x k ) is the observation model.

[0011] Furthermore, the particle filtering algorithm has significant advantages in dealing with complex non-linear systems and high-noise environments. By using importance sampling and weighted sampling techniques, the particle filtering can generate multiple particles and adjust them according to their weights to effectively handle sensor noise and non-linear problems.

[0012] Preferably, the particle filtering algorithm is used to handle the non-linear and high-noise problems of the luggage state, generate a particle set through importance sampling and weighted sampling, and calculate the state distribution of the luggage.

[0013] Preferably, the sensor module includes an RFID reader, a lidar, and an accelerometer. The RFID reader is used for luggage identification. The lidar is used to measure the relative position between the luggage and the conveyor belt or other obstacles. The accelerometer is used to monitor the acceleration of the luggage. The temperature and humidity sensor is used to monitor the impact of the environment on luggage tracking.

[0014] Furthermore, by integrating multiple sensors, the sensor module provides comprehensive luggage status data. The RFID reader ensures the unique identification of the luggage. The lidar provides high-precision spatial positioning data. The accelerometer provides the motion information of the luggage. The temperature and humidity sensor can monitor the impact of environmental factors on the sensor performance. Through the collaborative work of these sensors, it can effectively solve the problem that it is difficult for a single sensor to comprehensively collect luggage status data in the prior art.

[0015] Preferably, the data processing module includes a luggage status prediction unit and a status update unit. The luggage status prediction unit uses the Kalman filter or particle filter algorithm to predict the position, speed, and acceleration of the luggage. The status update unit corrects the luggage status according to the real-time observation data and the predicted values.

[0016] Furthermore, the prediction unit of the data processing module predicts the status of the luggage through the Kalman filter or particle filter algorithm, and adjusts the status estimation of the luggage in real time. The status update unit corrects according to the real-time sensor data, ensuring the system's real-time response ability and accuracy to the luggage status, ensuring the continuity and accuracy of the luggage status, and avoiding inaccurate estimation caused by data lag.

[0017] Preferably, the control module includes a display unit and an alarm unit for generating luggage tracking information in real time. The display unit displays information such as the current position, speed, and acceleration of the luggage through a graphical interface. The alarm unit triggers an alarm prompt when the luggage deviates or is abnormal.

[0018] Furthermore, the control module displays the status information of the luggage in real time through the display unit, providing a clear and intuitive view for the operator. Through the alarm unit, when the luggage deviates or other abnormal situations occur, the system can timely notify the operator for handling, ensuring the smooth progress of the consignment process.

[0019] Preferably, the feedback module conducts data interaction with other airport automation systems to ensure the coordination and consistency of the tracking information of the luggage in the consignment process with other links. The system includes security inspection and luggage sorting systems.

[0020] Furthermore, the integration of the feedback module with other automated systems at the airport ensures the smooth transfer of luggage between various links. When the luggage passes through the security check system, the feedback module can transmit information to the luggage sorting system in real time to ensure information synchronization throughout the check-in process.

[0021] Preferably, the data processing module uses multi-sensor data fusion technology. Combining the real-time update of sensor data, it adopts a comprehensive strategy of Kalman filtering, Bayesian filtering, and particle filtering to dynamically optimize the accuracy of luggage tracking.

[0022] Furthermore, the multi-sensor data fusion technology of the data processing module can effectively integrate data from different sensors, adopt multiple filtering algorithms to improve the accuracy of luggage tracking, solve the problem of data incompleteness caused by a single sensor, and ensure the stability and accuracy of the system in different environments through a dynamic optimization strategy.

[0023] The present invention provides an intelligent identification system for self-service luggage check-in at the airport. It has the following beneficial effects: 1. By adopting multi-sensor data fusion and advanced algorithms such as Kalman filtering, Bayesian filtering, and particle filtering, the present invention estimates the luggage status in real time. By integrating data from multiple sensors, the system can accurately predict the position, speed, and acceleration of the luggage. Compared with the existing technology that only relies on RFID or single-sensor data, the present invention greatly improves the accuracy of status estimation in complex environments and solves the deficiency that traditional technologies are difficult to cope with interference and occlusion problems.

[0024] 2. Through the collaborative work of the control module and the feedback module, the present invention realizes the functions of real-time luggage status feedback and automatic abnormal alarm. Different from the traditional technology that relies on manual judgment and has a slow response, the present invention can alarm immediately when the luggage deviates from the track or other abnormalities occur, ensuring that the processing personnel can respond quickly. This automated response mechanism reduces manual intervention, avoids human errors, and improves the efficiency and safety of the entire luggage handling process.

[0025] 3. By interacting with other automated systems at the airport (such as the security check system and the luggage sorting system), the present invention ensures the accurate tracking of luggage and cross-system information synchronization. Compared with the isolated information situation in the existing technology, this innovative solution effectively solves the problem of lack of coordination between different systems, enables seamless transfer of luggage between multiple links, and greatly improves the coordination and working efficiency of the system.

[0026] 4. In terms of the abnormal detection of luggage in the present invention, by comprehensively using a variety of algorithms and sensor data, it is possible to achieve automatic monitoring and alarming of problems such as luggage deviating from the path and stagnation. Different from the traditional technology that only relies on visual inspection or a single sensor solution, the present invention provides a more efficient and reliable abnormal detection mechanism, reduces the risk of luggage loss or misdelivery, and ensures the safety and accuracy of the consignment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a system framework diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0029] Please refer to the appended Figure 1 , the embodiments of the present invention provide an intelligent identification system for airport self-service luggage consignment, including: A sensor module for real-time collection of physical state data of luggage; A data processing module that receives signals from sensors and performs data preprocessing; A control module for generating position feedback based on real-time luggage tracking data, providing a visualization interface and abnormal alarm information; a feedback module for transmitting feedback information to the control module to ensure automatic processing of luggage tracking and abnormal management.

[0030] System Composition This system mainly consists of four core modules: a sensor module, a data processing module, a control module, and a feedback module.

[0031] Sensor module: This module is responsible for collecting various status information of luggage. The sensors include: RFID reader: Responsible for reading luggage tag information to ensure that each piece of luggage has a unique identifier.

[0032] LiDAR: Measures the relative position between the luggage and surrounding objects (such as conveyor belts, other luggage).

[0033] Accelerometer: Monitors the acceleration of the luggage during transportation to help estimate the moving direction and speed of the luggage.

[0034] Temperature and humidity sensor: Used to monitor the impact of environmental factors on luggage consignment and avoid equipment malfunction caused by temperature and humidity changes.

[0035] Data Processing Module: This module is the core of the system, responsible for processing data from sensors and estimating the status of the luggage through Kalman filtering, Bayesian filtering, and particle filtering algorithms. Through data fusion, this module can estimate the position, speed, and acceleration of the luggage in real time and effectively reduce the impact of sensor errors and environmental interference.

[0036] Control Module: This module generates the current status of the luggage by receiving feedback information from the data processing module in real time and displays it on the display interface. These status information include: the current position, speed, acceleration, etc. of the luggage. If the luggage has an abnormality (such as deviating from the predetermined path), the system will automatically trigger an alarm.

[0037] Feedback Module: Responsible for feeding back the real-time status information of the system to other airport automation systems, such as the security inspection system, the luggage sorting system, etc. Through data sharing, it ensures that the luggage tracking information can be circulated and updated throughout the airport.

[0038] Working Principle When the luggage consignment system starts from the passenger submitting the luggage, the system monitors and tracks the luggage in real time through multiple sensors. First of all, the RFID tag provides a unique identifier for each piece of luggage, and the RFID reader reads the tag information through a wireless signal to identify the luggage identity in real time.

[0039] The lidar measures the position of the luggage on the conveyor belt and updates the position of the luggage in real time by calculating the distance between the luggage and surrounding obstacles. The accelerometer monitors the acceleration of the luggage to calculate the speed and trajectory of the luggage. The function of the temperature and humidity sensor is to monitor environmental changes. Especially under specific weather conditions, it can provide additional data references for the system.

[0040] After receiving data from each sensor, the data processing module performs comprehensive processing. The Kalman filtering algorithm is used to predict the movement trajectory of the luggage. At the same time, the Bayesian filtering algorithm is used to update the status estimation of the luggage. Especially in the presence of environmental noise and measurement errors, the Bayesian filtering can provide a more accurate estimation through recursive calculation, and the particle filtering deals with nonlinear systems.

[0041] Based on the latest luggage status information transmitted back by the data processing module, the control module displays the current luggage position, speed, acceleration, etc. on the user interface for the operator to monitor in real time. When the system detects that the luggage has an abnormality (for example, deviating from the track, standing still, or being misdelivered), the alarm module immediately triggers an alarm, and the operator can take measures quickly.

[0042] The feedback module ensures that the luggage status is synchronized with other airport automation systems. For example, when the luggage arrives at the consignment area from the security inspection area, the real-time status information of the luggage can be transmitted to the luggage sorting system through the feedback module to ensure seamless connection of the luggage in different links.

[0043] Key Technologies and Principles Kalman Filter: Kalman filter is an optimal linear filtering algorithm that can perform minimum mean square error estimation based on noisy data. In the present invention, the Kalman filter is used to predict the state of the luggage and correct it after each new observation data is obtained. While ensuring data accuracy, this algorithm effectively reduces the interference of sensor errors and noise.

[0044] Bayesian Filter: Bayesian filter updates the system state through probabilistic inference and is applicable to dynamically changing environments. During the luggage tracking process, the Bayesian filter can recursively update the state estimate of the luggage based on known observation data and previous estimation results. It can handle the non-linear characteristics of the system, especially in complex environments such as occlusion, interference, and rapidly changing environments.

[0045] Particle Filter: Particle filter is a non-linear filtering technique based on the Monte Carlo method and is applicable to situations with high noise and non-Gaussian distributions. Through importance sampling and weighted sampling, the particle filter estimates the state of the luggage. Especially during multi-sensor data fusion, the particle filter can efficiently process sensor information from different sources.

[0046] Data Fusion: The data fusion technology adopted in the present invention integrates information from multiple sensors to obtain a more accurate estimation of the luggage position and state.

[0047] Module 1: Sensor Module The sensor module is a key component of the intelligent identification system for self-service luggage check-in at the airport in the present invention. Its main function is to collect and monitor the physical state information of the luggage in real time. Through the collaborative work of multiple sensors, this module provides accurate real-time data for the system and transmits this data to the subsequent processing module for analysis and estimation. The accuracy and stability of the sensor module directly affect the luggage tracking effect of the entire system.

[0048] In this embodiment, the sensor module includes multiple sensors, mainly including RFID readers, lidar, accelerometers, and temperature and humidity sensors. Each sensor has a different working principle and application scenario, but their common goal is to ensure the accuracy and reliability of the luggage state data.

[0049] The RFID reader is one of the basic devices in the sensor module and is mainly used for the unique identification of luggage. Generally, luggage is affixed with a uniquely identified RFID tag. The RFID reader uses radio frequency identification technology to identify and read the information of the luggage. The working mode of the RFID reader is not affected by environmental light or viewing angle, ensuring that the luggage identification can be accurately read under various conditions. By connecting to the data processing module, the RFID reader can transmit the identification information of the luggage to the system, providing basic data for subsequent luggage status tracking.

[0050] As an option, the lidar sensor is used to measure the relative position of the luggage and surrounding obstacles. In the airport environment, luggage is often in a dense conveyor belt area and may come into contact with or be blocked by other luggage or facilities. The lidar emits laser light and receives the reflected signal, enabling accurate measurement of the distance between the luggage and other objects, generating high-precision spatial position data. These data are not only used to determine the current position information of the luggage but also to detect possible obstacles to avoid collisions during luggage transportation.

[0051] In a possible implementation, the accelerometer is used to monitor the acceleration changes of the luggage. The movement of the luggage on the conveyor belt is often affected by different external factors and may exhibit acceleration or deceleration phenomena. By measuring the acceleration of the luggage, the accelerometer can help the data processing module accurately calculate the speed changes of the luggage, further optimizing the estimation of the luggage position. The data of this sensor can provide a more accurate real-time state estimate during acceleration, deceleration, or turning processes, avoiding errors caused by environmental changes.

[0052] The main function of the temperature and humidity sensor is to monitor the changes in environmental temperature and humidity. Especially under certain special weather conditions, the changes in temperature and humidity may affect the sensor data and thus the accuracy of the entire system. For example, in extremely high or low temperature environments, some sensors may experience response delays or errors. The temperature and humidity sensor can provide auxiliary information for the data processing module by monitoring the environmental changes in real time, ensuring the stable operation of the system under different environmental conditions.

[0053] In this embodiment, multiple sensors in the sensor module work together, and the role of each sensor in the system cannot be ignored. The data processing module will provide an accurate estimation of the luggage status based on the data types of different sensors and by combining their respective output results.

[0054] Collection and Transmission of Sensor Data: All sensors transmit the collected data to the data processing module in real time via wired or wireless means. The stability and security of the data transmission channels are of great importance. Therefore, an efficient data communication protocol is adopted in the design to ensure high-speed and low-latency data transmission. The sensor module needs to be calibrated regularly to maintain stability and accuracy during long-term operation.

[0055] Specifically, after the sensor module obtains the baggage tag information through the RFID reader, the lidar and accelerometer synchronously provide real-time data on the position and motion state of the baggage, while the temperature and humidity sensor provides environmental data for the system. All sensor data is aggregated through the transmission channel and provided to the data processing module, which processes it using Kalman filtering, Bayesian filtering, and particle filtering algorithms according to different data types for state estimation and prediction.

[0056] In this structure, the sensor module realizes all-round data collection of baggage identity recognition, position tracking, acceleration monitoring, and environmental perception, ensuring that the system can accurately and real-time obtain multi-dimensional information of the baggage. This series of data supports subsequent baggage tracking, positioning, and anomaly monitoring.

[0057] In this embodiment, the sensor module transmits the multi-dimensional state data of the baggage to the data processing module through the data output of different sensors. Each sensor data generates a unified state space estimation after mapping, and the relationship between the baggage identification read by RFID and the baggage identity; z rfid =f rfid (x rfid ) where x rfid is the baggage RFID tag information, and the relationship between the baggage position data generated by the lidar and the current position of the baggage; z lidar =f lidar (p k ,obstructions) where obstructions represents the relevant information of obstacles, and p k is the current position of the baggage, and the relationship between the acceleration data measured by the accelerometer and the acceleration of the baggage; z acc =f acc (a k ) The relationship between the output data of the temperature and humidity sensor and the environmental temperature and humidity; z env =f env (T,H) After being processed by the above formula, each sensor data provides high-precision luggage position, speed, acceleration, and environmental data for the data processing module; these data provide basic support for the subsequent luggage state estimation and prediction of the system, ensuring the accuracy of luggage tracking.

[0058] Module 2: Data Processing Module In the intelligent identification system for self-service luggage check-in at the airport of the present invention, the data processing module is one of the core components, responsible for receiving data from the sensor module and processing and optimizing it through various algorithms, so as to achieve accurate estimation of the luggage state; the work of the data processing module directly affects the real-time performance and accuracy of the system. It combines different algorithms (such as Kalman filtering, Bayesian filtering, and particle filtering) to process the position, speed, and acceleration information of the luggage, ensuring high-precision estimation of the luggage state. Through this module, the system can achieve real-time tracking and anomaly detection of the luggage, providing strong technical support for the entire check-in process.

[0059] In this embodiment, the main task of the data processing module is to receive various sensor data from the sensor module and perform data fusion and state estimation using Kalman filtering, Bayesian filtering, and particle filtering algorithms; the data processing module not only needs to process single-sensor data but also fuse data from different sensors to improve the overall accuracy of the system; during this process, the data processing module needs to perform preprocessing, state prediction, and state update on the output of each sensor, and its main workflow includes preliminary processing of data, application of filtering algorithms, and final state estimation.

[0060] The Kalman filtering algorithm is used in the data processing module to predict and correct the state of the luggage. Kalman filtering is an optimal estimation method based on the minimum mean square error and is applicable to linear systems. In the present invention, the application of Kalman filtering is mainly to dynamically predict the position, speed, and acceleration of the luggage and update it in combination with sensor measurement data. Through the prediction-update cycle, Kalman filtering can effectively process the noise and errors of the luggage state and provide high-precision estimation.

[0061] Specifically, the prediction step of Kalman filtering predicts the state of the luggage according to the state transition matrix A, control input matrix B, and control input u k of the luggage: where, is the predicted luggage state, A is the state transition matrix, which describes the change of the luggage from time k - 1 to time k, B is the control matrix, indicating the influence of the control input on the luggage state, and u k is the external control input (such as the acceleration of the conveyor belt).

[0062] At the arrival of the observation data z k the Kalman filter corrects the predicted value through an update step that uses the Kalman gain K k to weight the predicted value and the observation data: where is the predicted covariance matrix, representing the uncertainty of the prediction, and R k is the observation noise covariance matrix, and H is the observation matrix that describes how the luggage state is mapped to the observation space.

[0063] The corrected luggage state is calculated through the Kalman gain K k as follows: The updated state estimate is the final estimate of the luggage state, and through this process, the Kalman filter achieves the optimal correction of the luggage state.

[0064] Bayesian filtering estimates the system state by recursively calculating the posterior probability and is applicable to dynamic environments and situations where the system state changes. The core is to update the posterior distribution of the luggage state through the prior probability and the likelihood function; in the present invention, Bayesian filtering recursively updates the state of the luggage through the following formula: p(x k |z 1:k ) ∝ p(z k |x k ) · p(x k |x k-1 ) where p(x k |z 1:k ) is the state probability at time k given the observation data z 1:k , p(z k |x k ) is the observation model, representing the probability of observing z k when the given state is x k , and p(x k |x k-1 ) is the state transition probability, describing the change of the state from time k - 1 to time k.

[0065] Bayesian filtering corrects the luggage state through this recursive formula by combining historical data and current observation data, improving the accuracy of state estimation, especially in environments with noise and uncertainty. In one possible implementation, the particle filter algorithm is used to handle the non - linear problems in luggage state estimation. Different from the Kalman filter and Bayesian filtering, the particle filter uses importance sampling and resampling techniques and a set of particles (i.e., random samples of the state) to estimate the state distribution of the system.

[0066] The particle filter proceeds through the following steps: Initialization: Randomly draw N particles from the possible state space according to the initial state distribution Each particle represents a possible state of the luggage.

[0067] Prediction: Use the state transition model to predict each particle and calculate the possible state of the luggage at the next moment.

[0068] Update: According to the current observation data z k Update the weights of the particles. The updated particle weights are calculated by the following formula: where, is the weight of the i-th particle at time k, is the observation likelihood function given the particle state.

[0069] Resampling: Resample according to the particle weights to generate a new set of particles, thereby enhancing the representativeness of the particles.

[0070] The advantage of the particle filter is that it can handle state estimation problems with highly nonlinear and non-Gaussian distributions. Therefore, in complex environmental conditions, the particle filter can provide a more accurate estimation of the luggage state.

[0071] One of the core functions of the data processing module is to perform multi-sensor data fusion. The data collected by different types of sensors (such as RFID, lidar, accelerometers, and temperature and humidity sensors) in the sensor module are combined by the data processing module. The Kalman filter, Bayesian filter, and particle filter algorithms use different data fusion strategies to synthesize the data from each sensor to achieve the optimal estimation of the luggage state.

[0072] All the processed data will ultimately be transmitted to the control module for display and subsequent management by the luggage tracking system; the core task of the data processing module is to ensure the synchronous fusion of the data output by different sensors and provide high-precision luggage state information.

[0073] Module 3: Control Module The control module plays a crucial role in the intelligent identification system for airport self-service luggage check-in of the present invention. It is responsible for receiving the real-time luggage state estimation results output by the data processing module and performing corresponding operations based on this information. This module ensures that the system can efficiently and accurately track the luggage and can respond to abnormal situations in a timely manner through control algorithms, visualization interfaces, and alarm mechanisms. The control module is not only the display center for luggage tracking information but also responsible for handling emergency events and coordinating with other airport automation systems to ensure the smooth progress of the check-in process.

[0074] In this embodiment, the main function of the control module is to generate real-time luggage status feedback based on information such as the position, speed, and acceleration of the luggage provided by the data processing module. By combining with the display module and the alarm module, this module provides comprehensive real-time luggage tracking services. In addition, the control module also conducts data interaction with other automated systems at the airport to ensure the coordination and effective management of luggage among different links. By integrating these functions, the control module can achieve efficient and automated luggage handling, improving the overall operation efficiency of the airport.

[0075] Real-time Data Display and Luggage Status Feedback Generally, after receiving the output from the data processing module, the control module generates real-time luggage status feedback based on the real-time status information of the luggage. Specifically, the control module displays real-time data such as the current position information, speed, and acceleration of the luggage to the operator through a graphical user interface (GUI). Through the interface, the operator can intuitively see the current status of each piece of luggage, which helps with management and monitoring. The display interface of the system not only shows the luggage position but also can display the key links (such as security check, conveyor belt, etc.) that the luggage passes through during the consignment process, providing real-time tracking functionality for the operator.

[0076] As an option, the luggage status feedback information usually includes the current positioning information, movement trajectory, and possible abnormal situations of the luggage. For example, the control module can display whether the luggage deviates from the predetermined path, whether there are obstacles or other potential problems. These information are not only displayed through the graphical interface but also can be used for automated alarm and abnormal handling, providing data support for subsequent links.

[0077] Alarm Mechanism and Abnormal Handling In this embodiment, the control module also undertakes the functions of abnormal monitoring and alarm. When the luggage deviates from the track or other abnormal situations occur, the system will automatically trigger the alarm mechanism. The types of alarms can be divided into different levels, specifically including: mild warning (such as short-term deviation), severe warning (such as the luggage staying still), and emergency alarm (such as the luggage colliding or getting lost). The alarm information will be fed back to the operator through sound, vision, and system logs, etc., to ensure that problems can be handled in a timely manner. Specifically, the control module uses the following formula for abnormal detection and alarm decision-making: where, represents the current actual position of the luggage, represents the predetermined target position, and Δp k represents the deviation between the current position and the target position. When the deviation value exceeds the preset threshold, the system automatically triggers the alarm mechanism and feeds this information back to the operator.

[0078] In a possible implementation, the system can not only trigger an alarm based on the deviation amount, but also make predictions based on the changing trend of the luggage status. By combining the speed and acceleration information of the luggage, the system can determine whether there is a potential risk of the luggage deviating from its path. For example, if the speed of the luggage is close to zero and the acceleration remains at a low level, the system will infer from this information that the luggage may be stagnant and then trigger an alarm.

[0079] Integration and data sharing with other systems The control module is also responsible for data interaction with other airport automation systems (such as security inspection systems, luggage sorting systems, etc.). By connecting to these systems, the control module can receive the status information of other links in real time to ensure the smooth transition of the luggage between various links. For example, when the luggage passes through security inspection, the control module will automatically receive the confirmation signal from the security inspection system, mark the luggage as having passed the security inspection, and continue with the subsequent consignment process. Through data sharing with the luggage sorting system, the control module ensures that the luggage can be correctly sorted at the appropriate time and location, preventing misplacement or loss.

[0080] In this implementation, the control module interacts with other automation systems by defining a unified data interface. For example, the control module shares the identity information and location data of the luggage through the data transmission protocol with the security inspection system, thereby updating the status of the luggage in real time. When the luggage passes through security inspection and passes the verification, the control module will automatically update the status of the luggage and send the information to the luggage sorting system, indicating that the luggage is ready for sorting. The synchronous update of the data improves the automation level and accuracy of the system.

[0081] Control algorithms and logical decisions During the data processing process, the control module not only displays the real-time status of the luggage, but also makes intelligent decisions based on the prediction model and historical data. Specifically, the control module can use intelligent algorithms to predict the future trajectory and status of the luggage based on information such as the luggage position, speed, and acceleration provided by the data processing module. For example, the system predicts the possible future position of the luggage based on its current status and adjusts the sorting priority of the luggage in real time to ensure the reasonable allocation of resources between different links.

[0082] In addition, the control module also includes dynamic optimization strategies to handle emergencies. For example, when the load of a certain link is too high, the control module can adjust the priority of luggage processing according to the real-time status to ensure the smoothness and efficiency of the entire consignment system.

[0083] Module 4: Feedback module In the intelligent identification system for self-service baggage check-in at the airport of the present invention, the feedback module plays a bridging role in the overall system, connecting the core data processing part of the system with the external control and management system. The main task of this module is to feedback the real-time baggage status generated by the data processing module to relevant operators, the display system, and conduct data interaction with other automated systems (such as security inspection, baggage sorting system, etc.). Through real-time feedback, the feedback module not only improves the accuracy of baggage tracking but also can respond to anomalies in a timely manner and optimize the baggage handling process.

[0084] In this embodiment, the feedback module collaborates with the data processing module and the control module to collect real-time status data of the baggage and generate corresponding feedback based on this information. Specifically, the feedback module outputs status data such as the position information, speed, and acceleration of the baggage to the display module for the operator to view and shares information with other automated systems to ensure seamless connection between each link of the baggage. At the same time, the feedback module is also responsible for reporting abnormal situations and notifying the operator to handle them through the alarm system.

[0085] Real-time data output and display Generally, the feedback module is responsible for the real-time output of the baggage status data from the data processing module. The output content includes status information such as the current position, speed, and acceleration of the baggage for use by system monitoring, display, and operators. The feedback module closely collaborates with the display module to ensure that the real-time status of each piece of baggage can be clearly displayed on the operator's working interface. In this way, the operator can monitor the processing status of each piece of baggage through the graphical interface to ensure there are no errors.

[0086] Specifically, after receiving the output information from the data processing module, the feedback module will display the status information of each piece of baggage to the operator through the graphical user interface (GUI). These information can be presented in the form of charts, lists, or other suitable forms. The operator can directly see the current position, transportation speed, and acceleration of the baggage on the interface and respond to abnormal situations (such as the baggage deviating from the track or stagnating). The display module can also track the historical trajectory of the baggage in real time to ensure the traceability and transparency of the system.

[0087] Integration and data sharing with other systems As an option, the feedback module also conducts data interaction with other automated systems at the airport (such as the security inspection system, baggage sorting system, etc.). Through integration with these systems, the feedback module can transmit the processing status of the baggage to relevant systems in real time. For example, when the baggage passes through security inspection, the feedback module will send the identity information and its processing status of the baggage to the security inspection system to ensure the synchronization of baggage information between each link. In this way, the feedback module can help coordinate the work between different systems and ensure seamless docking of the entire baggage check-in process.

[0088] In a possible implementation, the feedback module is also responsible for transmitting data to other airport operation-related systems, such as flight scheduling systems, transportation systems, etc. Through this data sharing, the system can dynamically optimize the baggage handling process. For example, during certain peak periods, the feedback module can adjust the baggage handling priority based on the real-time status of the baggage and optimize resource allocation according to the load conditions of other systems.

[0089] Alarm and Exception Handling The feedback module plays a key role in anomaly detection and alarm. After the data processing module generates the baggage status estimate, the feedback module determines whether there is an abnormal situation according to the preset alarm rules. When the baggage deviates from the track, stalls, or has other abnormalities, the feedback module will immediately trigger the alarm mechanism and notify the operator or relevant management personnel in various ways.

[0090] For the further handling of anomalies, the feedback module not only provides an alarm but may also provide anomaly diagnosis information to help the operator understand and solve the problem. For example, when the baggage stalls, the feedback module can prompt the operator through the display interface that the baggage may not be able to continue due to path blockage or other problems, thus initiating the manual handling procedure.

[0091] Reporting and Recording Functions In some embodiments, the feedback module also includes a recording function for detailed recording of the baggage status data, anomaly information, handling process, etc. These records can be used for subsequent analysis and auditing to ensure the transparency and traceability of the system. These recorded information can be stored in a database for subsequent query and operation.

[0092] The reporting function of the feedback module can also automatically generate reports for managers or operators. The report content includes the baggage handling time, handling path, occurred abnormal situations and their solutions, etc. These reports not only help improve operational efficiency but also provide data support for future optimization. Control and Adjustment Functions In certain cases, the feedback module can also cooperate with the control module to adjust the baggage handling process. When an anomaly occurs in baggage handling or when the resource utilization rate of a certain link is too high, the feedback module can transmit data to the control module, which will adjust the handling process according to the real-time status. For example, the control module may decide to prioritize the handling of certain baggage or allocate the baggage handling to other sorting systems during high load. Through this mechanism, the feedback module not only provides real-time monitoring but also dynamically optimizes the efficiency and smoothness of baggage handling.

[0093] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An airport self-service baggage check-in intelligent identification system, characterized in that: include: Sensor module, used to collect physical status data of luggage in real time; A data processing module receives signals from sensors and performs data preprocessing; A control module, which is used to generate location feedback based on real-time baggage tracking data, and provide a visual interface and abnormal alarm information; The feedback module is used to transmit feedback information to the control module to ensure the automated processing of baggage tracking and exception management.

2. The airport self-service baggage check-in intelligent identification system according to claim 1, characterized in that: The data processing module predicts and updates the state of the luggage through Kalman filtering, Bayesian filtering and particle filtering algorithms to achieve real-time estimation of the position, speed and acceleration of the luggage. The data processing module includes a data fusion module for Kalman filtering, Bayesian filtering and particle filtering, which is used to process the luggage state data collected by multiple sensors to generate an optimal estimation value.

3. The airport self-service baggage check-in intelligent identification system according to claim 2, characterized in that: The Kalman filter algorithm is used to calculate the state transfer matrix A, control matrix B and control input u of the baggage. k , combined with the process noise matrix Q k and the observation noise matrix R k , make real-time predictions of the baggage’s current position, speed, and acceleration, and update the status based on the observed data.

4. The airport self-service baggage check-in intelligent identification system according to claim 2, characterized in that: The Bayesian filtering algorithm recursively calculates the posterior probability distribution of the baggage using the following formula: p(x k |z 1:k )∝p(z k |x k )·p(x k |x k-1 ) Among them, x k is the state vector of the luggage, z k is the observed data at time k, p(x k |x k-1 ) is the state transition probability, p(z k |x k ) is the observation model.

5. The airport self-service baggage check-in intelligent identification system according to claim 2, characterized in that: The particle filter algorithm is used to deal with the nonlinear and high noise problems of the baggage status, generates a particle set through importance sampling and weighted sampling, and calculates the status distribution of the baggage.

6. The airport self-service baggage check-in intelligent identification system according to claim 1, characterized in that: The sensor module includes an RFID reader, a laser radar and an accelerometer. The RFID reader is used for baggage identification, the laser radar is used to measure the relative position between the baggage and the conveyor belt or other obstacles, the accelerometer is used to monitor the acceleration of the baggage, and the temperature and humidity sensor is used to monitor the impact of the environment on baggage tracking.

7. The airport self-service baggage check-in intelligent identification system according to claim 1, characterized in that: The data processing module includes a baggage status prediction unit and a status update unit. The baggage status prediction unit uses a Kalman filter or a particle filter algorithm to predict the position, speed, and acceleration of the baggage. The status update unit corrects the baggage status according to real-time observation data and the predicted value.

8. The airport self-service baggage check-in intelligent identification system according to claim 1, characterized in that: The control module includes a display unit and an alarm unit for generating baggage tracking information in real time. The display unit displays the current position, speed, and acceleration information of the baggage through a graphical interface. The alarm unit triggers an alarm prompt when the baggage is offset or abnormal.

9. The airport self-service baggage check-in intelligent identification system according to claim 1, characterized in that: The feedback module exchanges data with other automated systems at the airport to ensure that the tracking information of the luggage in the check-in process is coordinated with other links. The system includes security inspection and luggage sorting systems.

10. The airport self-service baggage check-in intelligent identification system according to claim 6, characterized in that: The data processing module uses multi-sensor data fusion technology, combined with real-time update of sensor data, and adopts a comprehensive strategy of Kalman filtering, Bayesian filtering and particle filtering to dynamically optimize the accuracy of baggage tracking.

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