Statistical method and device of carry-on luggage data of passengers, medium and equipment

Abstract

The embodiment of the invention provides a statistical method and device of carry-on luggage data of passengers, a medium and equipment. The method comprises the following steps: acquiring three-dimensional data of passengers and carry-on luggage thereof in a target area based on a 3D vision module; processing the three-dimensional data by using a pre-trained identification model to obtain parameter information corresponding to the carry-on luggage, the parameter information including the size, volume and total number of the carry-on luggage; comparing the parameter information with a preset airline luggage standard to obtain a corresponding detection result; and according to the detection result, controlling the opening or the pause of the target gate. According to the technical scheme of the embodiment of the invention, the identification efficiency and the identification accuracy of the passenger carry-on luggage data can be improved, the airport and airline ground workers are effectively helped to intercept the passenger over-standard luggage, and the passing efficiency of passengers is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of security inspection equipment, and more specifically, to a statistical method, apparatus, medium, and equipment for passenger carry-on luggage data. Background Art

[0002] Currently, airport security and boarding gates rely mainly on manual checks for size detection and piece counts of passengers' carry-on luggage to ensure that the luggage meets the standards set by the airlines, avoiding problems such as insufficient cabin luggage rack capacity and extended boarding times due to excessive or oversized luggage. However, manual checks require a large amount of manpower and are inefficient, especially during peak passenger flow periods, which can easily cause channel congestion. Secondly, the accuracy of manual judgment is easily affected by subjective factors, and there is a risk of missed inspections or misjudgments, which may cause non-compliant luggage to enter the cabin and affect flight order. Furthermore, when a passenger's luggage exceeds the standard and needs to be transferred for check-in, the manual interception and communication process is cumbersome, which can easily cause passenger dissatisfaction and reduce the service experience. Therefore, how to improve the recognition efficiency and accuracy of passenger carry-on luggage data, effectively help airport and airline ground staff intercept passengers' oversized luggage, and ensure passenger passage efficiency has become a technical problem that needs to be solved urgently. Summary of the Invention

[0003] The embodiments of the present application provide a statistical method, device, medium, and equipment for passenger carry-on baggage data, which can improve the recognition efficiency and accuracy of passenger carry-on baggage data, at least to a certain extent, and effectively help airport and airline ground staff intercept passengers' oversized baggage and ensure passenger passage efficiency.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a method for collecting statistics of passenger carry-on baggage data is provided, comprising:

[0006] Acquire three-dimensional data of passengers and their carry-on luggage in the target area based on the 3D vision module;

[0007] Processing the three-dimensional data using a pre-trained recognition model to obtain parameter information corresponding to the carry-on luggage, the parameter information including the size, volume, and total number of pieces of carry-on luggage;

[0008] Comparing the parameter information with the preset airline baggage standards to obtain corresponding test results;

[0009] According to the detection result, the target gate is controlled to be opened or paused.

[0010] According to one aspect of an embodiment of the present application, a device for collecting statistics of passenger carry-on baggage data is provided, comprising:

[0011] An acquisition module, configured to acquire three-dimensional data of passengers and their carry-on luggage in a target area based on a 3D vision module;

[0012] a parameter calculation module, configured to process the three-dimensional data using a pre-trained recognition model to obtain parameter information corresponding to the carry-on baggage, the parameter information including the size, volume, and total number of pieces of carry-on baggage;

[0013] A comparison module, configured to compare the parameter information with preset airline baggage standards to obtain corresponding test results;

[0014] The processing module is used to control the opening or suspension of the target gate according to the detection result.

[0015] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for collecting statistics on passenger carry-on baggage data as described in the above embodiment is implemented.

[0016] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for collecting statistics on passenger carry-on baggage data as described in the above embodiments.

[0017] According to one aspect of an embodiment of the present application, a computer program product or computer program is provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for collecting statistics on passenger carry-on baggage data provided in the above-described embodiment.

[0018] In the technical solutions provided in some embodiments of the present application, a 3D vision module is used to obtain three-dimensional data of passengers and their carry-on luggage within a target area. This data is then processed using a pre-trained recognition model to obtain parameter information corresponding to the carry-on luggage, including the size, volume, and total number of pieces. This parameter information is then compared with preset airline baggage standards to obtain corresponding detection results. Based on these detection results, the target gate is then controlled to open or pause. In this way, through the coordination of the 3D vision module and the recognition model, passengers' carry-on luggage is identified and counted, improving both recognition efficiency and the accuracy of the recognition results, thereby ensuring efficient passage for passengers.

[0019] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, explaining the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0021] Figure 1 A schematic diagram showing a flow chart of a method for collecting statistics on passenger carry-on baggage data according to an embodiment of the present application is shown;

[0022] Figure 2 A block diagram showing a device for collecting statistics of passenger carry-on baggage data according to an embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0025] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0026] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0027] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0028] Figure 1 A flow chart of a method for collecting statistics on passenger carry-on baggage data according to an embodiment of the present application is shown.

[0029] It should be noted that the method can be applied to a terminal device or a server, wherein the terminal device may include but is not limited to one or more of a smart phone, a tablet computer, a portable computer, and a desktop computer; the server may be a physical server or a cloud server.

[0030] The following is an example of how this method is applied to a terminal device (hereinafter referred to as a "terminal"). Figure 1 As shown, the method for collecting passenger carry-on baggage data includes at least steps S110 to S140, which are described in detail as follows:

[0031] In step S110 , three-dimensional data of passengers and their carry-on luggage in the target area are acquired based on the 3D vision module.

[0032] Among them, the 3D vision module can be a hardware system composed of a multi-array high-precision 3D depth camera, which may include but is not limited to lidar, visible light, structured light / TOF (Time of Flight) technology, etc., so that the depth information of passengers and carry-on luggage in the target area can be collected from multiple perspectives.

[0033] The target area can be the 3D data collection range set in the airport security or check-in channel, which can cover the passenger passage and luggage placement area.

[0034] Three-dimensional data can be digital information containing spatial coordinates and surface features of an object, which is used to reconstruct a three-dimensional model of the target object.

[0035] In this embodiment, the terminal can communicate with the 3D vision module to receive three-dimensional data transmitted by the 3D vision module. It should be understood that the three-dimensional data is obtained by scanning the target area by the 3D vision module, including spatial information of passengers and carry-on luggage.

[0036] In one example, the terminal can pre-process the received 3D data, including but not limited to noise filtering (e.g., removing ambient light interference points), distortion correction (to eliminate the effects of lens deformation), and time synchronization alignment. This ensures the quality of the processed data, reduces the computational complexity of subsequent models, improves system response speed, and avoids 3D reconstruction errors caused by environmental interference.

[0037] In some embodiments of the present application, the 3D vision module may include a multi-array 3D camera to collect depth data from multiple perspectives and generate corresponding three-dimensional point clouds through input fusion.

[0038] In this embodiment, the 3D vision module may include a multi-array 3D camera, which can obtain depth information of the target area from multiple perspectives to achieve coverage of the target area and avoid blind spots. When the passenger enters the target area, the multi-array 3D camera is triggered synchronously to collect the original depth image of the passenger and his / her carry-on luggage. The terminal can receive the original depth image and perform the above-mentioned preprocessing steps on it. The depth data of multiple perspectives are then spliced ​​through coordinate system conversion and alignment algorithms (such as ICP algorithm, etc.) to form a unified three-dimensional point cloud. In this way, multi-source data fusion can significantly improve the integrity and accuracy of the three-dimensional model. For example, the side information of the luggage from the top perspective can be supplemented by the side perspective.

[0039] In one example, after acquiring the three-dimensional point cloud data, the terminal may use a point cloud optimization algorithm (such as voxel filtering, etc.) to downsample redundant points and retain key feature points to reduce the amount of data.

[0040] In some embodiments of the present application, the 3D vision module further includes a structured light auxiliary module for optimizing the accuracy of depth data acquisition of the edge of carry-on luggage.

[0041] Among them, the structured light auxiliary module can be a hardware component that projects a specific coded optical pattern (such as stripes or dots) onto the surface of the target object, uses a camera to capture the pattern deformation, and combines the triangulation principle to calculate the depth information of the object surface.

[0042] In this embodiment, a structured light projector can be deployed on the top or side of the target area to project a high-frequency stripe pattern or pseudo-random dot matrix code onto the surface of the luggage in the channel. It should be noted that time-division multiplexing technology can be used to switch between different coding modes (e.g., horizontal stripes, vertical stripes, etc.) to adapt to the reflective characteristics of different surface materials.

[0043] In actual use, a high-speed global shutter camera and a structured light projector are triggered in strict synchronization to capture an image of the deformed pattern projected onto the luggage surface. The original image is then preprocessed, including removing ambient light interference and correcting distortion. The initial depth image is then generated by analyzing the phase shift of the fringe pattern using a phase shift method or calculating the displacement of the dot matrix code using a feature matching algorithm.

[0044] The terminal fuses this initial depth image with the point cloud data captured by the multi-array 3D camera, optimizing noise points using a Kalman filter algorithm to generate a high-precision 3D point cloud. Next, an edge enhancement algorithm is used to refine the fused point cloud, filling in missing points and smoothing jagged outlines at the identified baggage edges.

[0045] Please continue to refer to Figure 1 In step S120, the three-dimensional data is processed using a pre-trained recognition model to obtain parameter information corresponding to the carry-on luggage, wherein the parameter information includes the size, volume, and total number of pieces of carry-on luggage.

[0046] In this embodiment, those skilled in the art can pre-build and train a recognition model that can be used to identify the aforementioned 3D point cloud and thereby determine parameter information related to the passenger's carry-on luggage. In one example, this parameter information may include, but is not limited to, the size, volume, and total number of pieces of carry-on luggage. Upon acquiring 3D data (i.e., the aforementioned 3D point cloud) of the passenger and their carry-on luggage in the target area, the terminal can input this data into the pre-trained recognition model, causing the recognition model to output the parameter information of the carry-on luggage contained therein based on the 3D data.

[0047] In some embodiments of the present application, the three-dimensional data is processed using a pre-trained recognition model to obtain parameter information corresponding to the carry-on luggage, including:

[0048] Performing semantic segmentation on the three-dimensional data using a pre-trained recognition model to separate passengers and carry-on luggage;

[0049] Based on the semantic segmentation results, a 3D model of the carry-on luggage is generated, and its size, volume, and total number of pieces are calculated.

[0050] In this embodiment, the terminal can input the three-dimensional data into the recognition model, and the recognition model can call the semantic segmentation algorithm to perform semantic segmentation on the three-dimensional data, thereby accurately distinguishing between passengers and their carry-on luggage and avoiding interference of human body movements in luggage detection.

[0051] The recognition model then performs a 3D reconstruction of the segmented carry-on baggage point cloud to produce a 3D model that includes dimensions (length, width, height), volume, and surface features. Based on this 3D model, the dimensions and volume of the carry-on baggage can be calculated. The number of identified carry-on baggage pieces can then be counted to determine the total number of carry-on baggage items for the passenger. It should be understood that the 3D model generated through 3D reconstruction provides an accurate data foundation for subsequent dimension calculations and compliance assessments, significantly reducing the error rate compared to traditional manual recognition methods.

[0052] In one embodiment, a deep learning algorithm (e.g., YOLO, Mask R-CNN, etc.) can be used to separate passenger and baggage data and generate a 3D baggage model. This can be pre-trained using a scene dataset and fine-tuned with actual scene data. The training data can cover passengers of varying heights, baggage sizes and styles, and diverse lighting and occlusion conditions to ensure model robustness.

[0053] In addition, edge computing platforms or high-performance, high-computing-power embedded devices can be used for real-time reasoning to improve the recognition and prediction accuracy of carry-on baggage parameter information, ensuring that the recognition delay is within the millisecond range.

[0054] Please continue to refer to Figure 1 In step S130, the parameter information is compared with the preset airline baggage standard to obtain the corresponding detection result.

[0055] Among them, the airline baggage standards can be the carry-on baggage restriction rules pre-set by the airline of the corresponding flight based on the flight type, cabin registration, etc., which may include the maximum allowed size, volume and weight thresholds, etc.

[0056] The test result can be a judgment conclusion after comparing the parameter information with the standard, for example, it can include "compliant" or "exceeding the standard" status, as well as specific exceeding items (such as exceeding the size limit, exceeding the volume limit, etc.).

[0057] In this embodiment, after obtaining the parameter information of the passenger's carry-on luggage, the terminal can retrieve the airline baggage standards set by the airline of the corresponding flight, and then compare the parameter information with the airline baggage standards.

[0058] In one example, the terminal can first unify the units of the parameter information (for example, converting inches to centimeters, etc.) and standardize the format (for example, retaining two decimal places, etc.) to ensure the comparability of the parameter information with the airline's baggage standards. Then, the terminal can compare the parameter information of the carry-on baggage item by item with the standard threshold of the corresponding item in the airline's baggage standards. If all parameters do not exceed the standard threshold, it can be marked as "compliant". If any parameter exceeds the standard, it can be marked as "exceeding the standard" and the specific exceeding item can be recorded. In this way, the misjudgment rate can be reduced through accurate item-by-item comparison, avoiding the overall conclusion error due to the misjudgment of a single parameter, and recording the specific exceeding items can help staff quickly locate the problem and improve the efficiency of handling.

[0059] In step S140, the target gate is controlled to be opened or paused according to the detection result.

[0060] Among them, the target gate can be a controllable gate device installed in the security inspection channel or boarding channel, which may include physical blocking devices (such as door wings, railings) and a drive control system for automatically opening or pausing passenger passage according to instructions.

[0061] In this embodiment, the terminal can analyze the detection results to determine the compliance status of the passenger's carry-on luggage. If the status is compliant, the terminal can generate an "open" instruction; if the status is "exceeding the standard", it can generate a "pause" instruction and can also attach an exceeding item code to facilitate subsequent problem tracing.

[0062] The terminal can then send the generated command to the controller of the target gate, causing the controller to perform the corresponding action according to the command. For example, if it receives an "open" command, the controller of the target gate can drive the motor or pneumatic device to open the gate door and allow passengers to pass; if it receives a "pause" command, the controller can maintain the gate's closed state and can also trigger an audible and visual alarm device (such as a red warning light, buzzer, etc.). In this way, the gate's action and alarm are linked to achieve automated control, reducing the need for manual operation.

[0063] So, based on Figure 1In the illustrated embodiment, a 3D vision module is used to acquire three-dimensional data of passengers and their carry-on baggage within a target area. This data is then processed using a pre-trained recognition model to obtain parameter information corresponding to the carry-on baggage, including its size, volume, and total number of pieces. This parameter information is then compared with preset airline baggage standards to obtain a corresponding detection result. Based on this detection result, the target gate is then controlled to open or pause. In this way, the 3D vision module and recognition model work together to identify and count passengers' carry-on baggage, improving both recognition efficiency and accuracy, thereby ensuring efficient passenger passage.

[0064] In some embodiments of the present application, after obtaining the corresponding detection result, the method further includes:

[0065] The detection results are displayed within the sight of passengers and / or staff based on augmented reality technology.

[0066] Among them, augmented reality technology can refer to the technology of integrating virtual information (such as text, icons, color marks, etc.) with real scenes through optical projection equipment or AR display devices (such as head-mounted glasses, transparent screens, etc.) to achieve an interactive effect of combining virtual and real.

[0067] The visual range of passengers and / or staff may refer to the physical space area that passengers or staff can directly observe through visual observation or AR equipment, such as the surface of luggage, the ground of the passage, or a specific marked area.

[0068] In this embodiment, after determining the corresponding test results, in order to avoid the problems of traditional manual prompts being non-intuitive and low information transmission efficiency, the terminal can use augmented reality technology to display the test results so that passengers or staff can obtain the information. For example, the test results can be projected onto the ground or the surface of carry-on luggage.

[0069] In some embodiments of the present application, the detection results are displayed within the sight of passengers and / or staff based on augmented reality technology, including:

[0070] Superimposing the detection result as a virtual mark on the surface of the carry-on luggage or within the user's field of view;

[0071] If the detection result shows that the luggage exceeds the standard, a real-time prompt will be given through sound and light signals and AR tags.

[0072] In this embodiment, the terminal can convert the detection result into a rendering instruction compatible with the AR device. If the detection result is an excess, the terminal can also match the preset virtual identification template according to the type of excess (for example, a red flashing border indicates that the size exceeds the standard, a yellow exclamation mark indicates that the weight exceeds the standard, etc.). In one example, an AR projection device can be pre-deployed at the top or side of the channel, or an AR display function can be integrated into the staff's handheld terminal (such as a tablet computer). The AR device and the target area are matched in real time through spatial positioning technology to ensure that the virtual identification is accurately superimposed on the predetermined position.

[0073] Therefore, based on the rendering instruction, the AR device can project the corresponding detection results into a specific area. For example, for compliant carry-on luggage, a green check mark can be projected on the surface of the luggage or on the ground in front of the passenger, and a voice prompt can be played simultaneously (for example, "The luggage is compliant, please pass"); for oversized luggage, a red flashing border can be superimposed on the surface of the luggage. In one example, adjustment instructions can also be projected around it, such as "Please check in the oversized luggage" or "Please transfer to the check-in counter", etc.

[0074] In one example, staff can access a 3D model of the baggage and details of the items exceeding the limit by touching the AR interface (e.g., clicking a virtual icon). Furthermore, content can be displayed hierarchically based on role permissions, so for example, passengers can see a brief reminder, while staff can view detailed parameters and handling suggestions.

[0075] In some embodiments of the present application, before comparing the parameter information with preset airline baggage standards, the method further includes:

[0076] Obtain the ticket information of the current flight to dynamically adjust the airline's baggage standards based on the ticket information.

[0077] Among them, the ticket information can be flight-related data obtained through the airport management system or airline database, including but not limited to flight number, cabin class (such as economy class, business class, etc.), seat occupancy rate, remaining capacity of luggage racks, and flight punctuality status.

[0078] In this embodiment, the terminal can obtain ticket information for the current flight, such as seat occupancy and remaining overhead bin capacity, from the airport management system or airline database. The terminal can then dynamically adjust the airline's baggage allowance based on this ticket information according to pre-set adjustment rules. For example, if the seat occupancy rate exceeds a certain threshold, the upper limit for carry-on luggage will be tightened by 5%. If the seat occupancy rate is less than a certain threshold, the original standard will be restored, and so on. The terminal can store the dynamically adjusted airline baggage allowance for subsequent use. This dynamic correction mechanism can optimize overhead bin resource allocation and avoid the problem of luggage being unable to be accommodated due to full luggage.

[0079] In one example, if ticket information acquisition fails (e.g., interface timeout), the terminal can automatically switch to the default baggage standard and record an exception log for subsequent investigation. This ensures that the terminal can still operate normally in the event of a partial failure, avoiding service failures.

[0080] The terminal can also verify the legality of the dynamically adjusted standards (for example, the lower limit of the size must not be less than the airline's minimum requirements, etc.). If the verification fails, it will roll back to the previous valid version to prevent unreasonable standard adjustments due to algorithm errors and ensure compliance.

[0081] The following describes an embodiment of the device of the present application, which can be used to implement the method for collecting passenger carry-on baggage data in the above-mentioned embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for collecting passenger carry-on baggage data in the above-mentioned embodiment of the present application.

[0082] Figure 2 A block diagram of a device for collecting statistics of passenger carry-on luggage data according to an embodiment of the present application is shown.

[0083] Reference Figure 2 As shown, a device for collecting statistics of passenger carry-on baggage data according to one embodiment of the present application includes:

[0084] An acquisition module, configured to acquire three-dimensional data of passengers and their carry-on luggage in a target area based on a 3D vision module;

[0085] a parameter calculation module, configured to process the three-dimensional data using a pre-trained recognition model to obtain parameter information corresponding to the carry-on baggage, the parameter information including the size, volume, and total number of pieces of carry-on baggage;

[0086] A comparison module, configured to compare the parameter information with preset airline baggage standards to obtain corresponding test results;

[0087] The processing module is used to control the opening or suspension of the target gate according to the detection result.

[0088] In some embodiments of the present application, after obtaining the corresponding detection result, the processing module is further configured to:

[0089] The detection results are displayed within the sight of passengers and / or staff based on augmented reality technology.

[0090] In some embodiments of the present application, the detection results are displayed within the sight of passengers and / or staff based on augmented reality technology, including:

[0091] Superimposing the detection result as a virtual mark on the surface of the carry-on luggage or within the user's field of view;

[0092] If the detection result shows that the luggage exceeds the standard, a real-time prompt will be given through sound and light signals and AR tags.

[0093] In some embodiments of the present application, before comparing the parameter information with the preset airline baggage standards, the comparison module is further configured to:

[0094] Obtain the ticket information of the current flight to dynamically adjust the airline's baggage standards based on the ticket information.

[0095] In some embodiments of the present application, the three-dimensional data is processed using a pre-trained recognition model to obtain parameter information corresponding to the carry-on luggage, including:

[0096] Performing semantic segmentation on the three-dimensional data using a pre-trained recognition model to separate passengers and carry-on luggage;

[0097] Based on the semantic segmentation results, a 3D model of the carry-on luggage is generated, and its size, volume, and total number of pieces are calculated.

[0098] In some embodiments of the present application, the 3D vision module includes a multi-array 3D camera to collect depth data from multiple perspectives and generate corresponding three-dimensional point clouds through data fusion.

[0099] In some embodiments of the present application, the 3D vision module further includes a structured light auxiliary module for optimizing the accuracy of depth data acquisition of the edge of carry-on luggage.

[0100] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0101] It should be noted that Figure 3 The computer system of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0102] like Figure 3 As shown, the computer 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 part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 303. The CPU 301, ROM 302 and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0103] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output section 307 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0104] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the various functions defined in the system of the present application are executed.

[0105] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the 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 box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0107] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0108] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0109] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0110] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0111] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0112] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A statistical method for passenger carry-on baggage data, characterized in that: include: Acquire three-dimensional data of passengers and their carry-on luggage in the target area based on the 3D vision module; Processing the three-dimensional data using a pre-trained recognition model to obtain parameter information corresponding to the carry-on luggage, the parameter information including the size, volume, and total number of pieces of carry-on luggage; Comparing the parameter information with the preset airline baggage standards to obtain corresponding test results; According to the detection result, the target gate is controlled to be opened or paused.

2. The solution according to claim 1, characterized in that After obtaining the corresponding detection result, the method further includes: The detection results are displayed within the sight of passengers and / or staff based on augmented reality technology.

3. The solution according to claim 2, characterized in that: The detection results are displayed within the sight of passengers and / or staff based on augmented reality technology, including: Superimposing the detection result as a virtual mark on the surface of the carry-on luggage or within the user's field of view; If the detection result shows that the luggage exceeds the standard, a real-time prompt will be given through sound and light signals and AR tags.

4. The method according to claim 1, wherein Before comparing the parameter information with the preset airline baggage standards, the method further includes: Obtain the ticket information of the current flight to dynamically adjust the airline's baggage standards based on the ticket information.

5. The method according to claim 1, wherein The three-dimensional data is processed using a pre-trained recognition model to obtain parameter information corresponding to the carry-on luggage, including: Performing semantic segmentation on the three-dimensional data using a pre-trained recognition model to separate passengers and carry-on luggage; Based on the semantic segmentation results, a 3D model of the carry-on luggage is generated, and its size, volume, and total number of pieces are calculated.

6. The method according to any one of claims 1 to 5, characterized in that The 3D vision module includes a multi-array 3D camera to collect depth data from multiple perspectives and generate corresponding three-dimensional point clouds through data fusion.

7. The method according to claim 6, characterized in that The 3D vision module also includes a structured light auxiliary module to optimize the depth data collection accuracy of the edge of the carry-on luggage.

8. A statistical device for passenger carry-on baggage data, characterized in that: include: An acquisition module, configured to acquire three-dimensional data of passengers and their carry-on luggage in a target area based on a 3D vision module; a parameter calculation module, configured to process the three-dimensional data using a pre-trained recognition model to obtain parameter information corresponding to the carry-on baggage, the parameter information including the size, volume, and total number of pieces of carry-on baggage; A comparison module, configured to compare the parameter information with preset airline baggage standards to obtain corresponding test results; The processing module is used to control the opening or suspension of the target gate according to the detection result.

9. A computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for collecting statistics on passenger carry-on baggage data according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the method for collecting statistics on passenger carry-on baggage data according to any one of claims 1 to 7.

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