Cargo loss prevention method and device in unmanned vehicle connection process
By collecting and comparing cargo information and retrieving monitoring data to determine the differences in cargo images, the problem of cargo loss during unmanned vehicle docking was solved, enabling traceability and loss prevention in an unattended environment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 北京云迹科技股份有限公司
- Filing Date
- 2023-06-21
- Publication Date
- 2026-04-24
AI Technical Summary
During the unmanned vehicle shuttle process, cargo compartments or goods may be taken by mistake or stolen, leaving no traceable evidence, resulting in the loss of goods.
By collecting environmental information to determine monitoring data, the target cargo information is compared with the actual cargo information. When there is a discrepancy, the monitoring data is retrieved to determine the image information of the different cargo, leaving evidence for traceability.
In an unattended environment, traceability evidence can be left to find the cause of goods loss and prevent goods from being lost in the future.
Smart Images
Figure CN116758478B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of robotics technology, and in particular to a method and device for preventing cargo loss during unmanned vehicle shuttle operations. Background Technology
[0002] With the booming development of the internet economy, advanced technologies such as automation and information are increasingly being applied in smart logistics warehousing centers. Among them, cooperative transportation between unmanned vehicles is currently a relatively advanced collaborative method in smart logistics transportation.
[0003] The cooperation between unmanned vehicles (UAVs) and shuttle robots is achieved through the transfer of cargo compartments. The cargo compartment is a detachable, universal component that can be attached to either the UAV or the shuttle robot. The transfer of the cargo compartment from the UAV to the shuttle robot enables the docking process. Another cooperative transportation method between UAVs and shuttle robots is UAV-to-UAV, where the shuttle robot acts as an intermediary, transferring cargo compartments from one UAV to another to facilitate the transfer of goods.
[0004] However, during the process of unmanned vehicles connecting to the cargo hold, the cargo hold or the goods in the cargo hold may be taken by mistake or stolen in an unattended environment. How to retain traceability evidence in the event of lost goods and prevent the loss of goods is an urgent problem to be solved. Summary of the Invention
[0005] In view of this, embodiments of this application provide a method and device for preventing cargo loss during unmanned vehicle shuttle operations, in order to solve the problem in the prior art that, in an unmanned vehicle operating in an unattended environment, the cargo compartment or the cargo in the cargo compartment may be taken by mistake or stolen, and there is no way to leave traceability evidence.
[0006] A first aspect of this application provides a method for preventing cargo loss during unmanned vehicle shuttle operations. This method is applied to a control system and includes:
[0007] The first unmanned vehicle is used to determine the environmental information surrounding it.
[0008] When the environmental information meets the collection conditions, the monitoring data determined based on the environmental information is collected.
[0009] Determine the target cargo information;
[0010] When the first unmanned vehicle connects with the second unmanned vehicle, the actual cargo information unloaded by the first unmanned vehicle is determined;
[0011] If the target cargo information is inconsistent with the actual cargo information, identify the discrepancy cargo and retrieve the monitoring data;
[0012] Identify image information of discrepancies in goods from monitoring data.
[0013] A second aspect of this application provides a cargo anti-loss device during unmanned vehicle shuttle operations, comprising:
[0014] Environmental information determination module: used to determine the environmental information around the first unmanned vehicle;
[0015] Monitoring data determination module: used to collect monitoring data determined based on environmental information when the environmental information meets the collection conditions;
[0016] Target cargo information determination module: used to determine target cargo information;
[0017] Actual cargo information determination module: used to determine the actual cargo information unloaded by the first unmanned vehicle when the first unmanned vehicle docks with the second unmanned vehicle;
[0018] Monitoring data retrieval module: used to identify the discrepancy between the target cargo information and the actual cargo information, and retrieve the monitoring data;
[0019] Image information determination module: used to determine image information of different goods from monitoring data.
[0020] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.
[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.
[0022] The beneficial effects of this application embodiment compared to the prior art are as follows: By collecting monitoring data determined based on environmental information, when the first unmanned vehicle connects with the second unmanned vehicle, if the target cargo information is inconsistent with the actual cargo information, the monitoring data can be retrieved to determine the image information of the discrepancies. This ensures that during the unmanned vehicle's cargo docking process, even in an unattended environment, evidence of cargo loss can be preserved, allowing the cause of loss to be identified and preventing further loss. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 This is a schematic diagram illustrating an application scenario of this application embodiment;
[0025] Figure 2 This is a flowchart illustrating a method for preventing cargo loss during unmanned vehicle shuttle operations provided in an embodiment of this application.
[0026] Figure 3 This is a schematic diagram of a cargo anti-loss device provided in an embodiment of this application during unmanned vehicle shuttle process;
[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0028] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0029] With the booming development of the internet economy, advanced technologies such as automation and information are increasingly being applied in smart logistics warehousing centers. Among them, cooperative transportation between unmanned vehicles is currently a relatively advanced collaborative method in smart logistics transportation.
[0030] The cooperation between unmanned vehicles (UAVs) and shuttle robots is achieved through the transfer of cargo compartments. The cargo compartment is a detachable, universal component that can be attached to either the UAV or the shuttle robot. The transfer of the cargo compartment from the UAV to the shuttle robot enables the docking process. Another cooperative transportation method between UAVs and shuttle robots is UAV-to-UAV, where the shuttle robot acts as an intermediary, transferring cargo compartments from one UAV to another to facilitate the transfer of goods.
[0031] However, during the process of unmanned vehicles connecting to the cargo hold, the cargo hold or the goods in the cargo hold may be taken by mistake or stolen in an unattended environment. How to retain traceability evidence in the event of lost goods and prevent the loss of goods is an urgent problem to be solved.
[0032] In view of the problems in the prior art, this disclosure provides a novel method and apparatus for preventing cargo loss during unmanned vehicle (UAV) docking. By collecting monitoring data determined based on environmental information, when the first UAV docks with the second UAV, if the target cargo information is inconsistent with the actual cargo information, the monitoring data can be retrieved to determine the image information of the discrepancy. This ensures that during the UAV docking process, even in an unattended environment, evidence of cargo loss can be preserved, allowing for the identification of the cause of loss and preventing further cargo loss.
[0033] The following describes in detail, with reference to the accompanying drawings, a method and apparatus for preventing cargo loss during unmanned vehicle shuttle operations according to an embodiment of this application.
[0034] Figure 1 This is a schematic diagram illustrating an application scenario according to an embodiment of this application. The application scenario may include terminal devices 101, 102, and 103, server 104, and network 105.
[0035] Terminal devices 101, 102, and 103 can be hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays that support communication with server 104, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. Terminal devices 101, 102, and 103 can be implemented as multiple software programs or software modules, or as a single software program or software module; this application embodiment does not impose any limitations on this. Furthermore, various applications can be installed on terminal devices 101, 102, and 103, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.
[0036] Server 104 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 104 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This application embodiment does not limit this.
[0037] It should be noted that server 104 can be either hardware or software. When server 104 is hardware, it can be various electronic devices that provide various services to terminal devices 101, 102, and 103. When server 104 is software, it can be multiple software programs or software modules that provide various services to terminal devices 101, 102, and 103, or it can be a single software program or software module that provides various services to terminal devices 101, 102, and 103. This application embodiment does not impose any limitations on this.
[0038] Network 105 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), and Infrared. This application embodiment does not limit this.
[0039] Users can establish a communication connection with server 104 via network 105 through terminal devices 101, 102, and 103 to receive or send information. Specifically, server 104 integrates a control system. Server 104 uses the first unmanned vehicle to determine the environmental information around the first unmanned vehicle; when the environmental information meets the collection conditions, it collects monitoring data determined based on the environmental information; server 104 determines the target cargo information; when the first unmanned vehicle connects with the second unmanned vehicle, it determines the actual cargo information unloaded by the first unmanned vehicle; if the target cargo information is inconsistent with the actual cargo information, it identifies the discrepancy cargo and retrieves the monitoring data; server 104 determines the image information of the discrepancy cargo from the monitoring data.
[0040] It should be noted that the specific types, quantities, and combinations of terminal devices 101, 102, and 103, server 104, and network 105 can be adjusted according to the actual needs of the application scenario, and this application embodiment does not impose any restrictions on this.
[0041] Figure 2 This is a flowchart illustrating a method for preventing cargo loss during unmanned vehicle shuttle operations, as provided in an embodiment of this application. Figure 2 Methods to prevent cargo loss during driverless vehicle shuttle operations can be developed by... Figure 1 The terminal device, server, or control system integrated into the server executes the commands. For example... Figure 2 As shown, the methods for preventing cargo loss during the unmanned vehicle shuttle process include:
[0042] S201, Use the first unmanned vehicle to determine the environmental information around the first unmanned vehicle;
[0043] S202, When the environmental information meets the collection conditions, collect the monitoring data determined based on the environmental information;
[0044] S203, Determine the target cargo information;
[0045] S204, When the first unmanned vehicle connects with the second unmanned vehicle, determine the actual cargo information unloaded by the first unmanned vehicle;
[0046] S205, If the target cargo information is inconsistent with the actual cargo information, identify the discrepancy cargo and retrieve the monitoring data;
[0047] S206, Identify image information of discrepancies in goods from monitoring data.
[0048] Specifically, in this embodiment, the unmanned vehicle is a type of intelligent mobile robot used for loading goods. The docking robot is a robot used to dock with the unmanned vehicle and unload goods. Typically, the unmanned vehicle's cargo carrier is a cargo compartment, and the unmanned vehicle can have several cargo compartments. The cargo compartment is adapted to both the unmanned vehicle and the docking robot. The docking robot moves to the bottom of the cargo compartment and uses an operating component to unload the cargo compartment from the unmanned vehicle and place it on the docking robot. Each docking robot can dock with one cargo compartment at a time, and the cargo compartment can be transferred between the unmanned vehicle and the docking robot to complete the docking. The first unmanned vehicle is the unmanned vehicle already loaded with goods. The environmental information surrounding the first unmanned vehicle is determined. Environmental information refers to the conditions around the first unmanned vehicle, which can specifically include the surrounding scene, real-time conditions, etc. This environmental information can be obtained through functions provided by the first unmanned vehicle itself. For example, the first unmanned vehicle can determine environmental information through cameras around its body; or through a thermal imaging device; or through some kind of positioning and navigation software, etc.
[0049] Furthermore, when the environmental information meets the collection conditions, monitoring data determined based on the environmental information is collected. The collection conditions are the standards for collecting monitoring data. Monitoring data refers to camera data that should be stored in the control system or server. In this embodiment, the monitoring data is crucial evidence for tracing the cause of cargo loss. Generally, the environmental information is monitored 24 / 7 by the first unmanned vehicle. When the collection conditions are met, the environmental information under those conditions is collected, identified as monitoring data, and stored for later retrieval. The collection conditions could be when personnel pass near the first unmanned vehicle.
[0050] Further, the target cargo information is determined. Target cargo information refers to the cargo that the first unmanned vehicle should unload according to the mission requirements. This includes the total quantity of cargo, which can be either total weight or total number.
[0051] Furthermore, when the first unmanned vehicle (UAV) docks with the second UAV, the actual cargo information unloaded by the first UAV is determined. The second UAV is the one that docks with the first UAV and receives the cargo from it. The docking of the first and second UAVs is essentially the process of transferring the cargo hold from the first UAV to the second UAV. During this transfer, a weighing device can determine the actual cargo information unloaded from the first UAV, that is, the total amount of cargo actually unloaded.
[0052] Furthermore, if the target cargo information differs from the actual cargo information, the discrepancy is identified, and monitoring data is retrieved. If the target cargo information and the actual cargo information are inconsistent, it indicates that the total cargo quantity is inconsistent, and the quantity or weight of the cargo actually unloaded does not meet the requirements of the task's unloading quantity or weight. This missing quantity or weight of cargo is the discrepancy cargo.
[0053] Furthermore, image information of the discrepancies can be identified from the monitoring data. The presence of discrepancies indicates a possible loss of goods. This allows us to retrieve monitoring data to determine the whereabouts and cause of the discrepancies. Since monitoring data can be collected when personnel pass near the unmanned vehicle, there is a possibility of loss when personnel do so. Multiple personnel can pass near the unmanned vehicle, resulting in multiple video recordings on the server. These recordings may contain image information of the discrepancies. This image information could be detailed footage showing what the discrepancies were, when they were lost, how they were lost, and who might have taken them.
[0054] According to the technical solution provided in this disclosure, by collecting monitoring data determined based on environmental information, when the first unmanned vehicle connects with the second unmanned vehicle, if the target cargo information is inconsistent with the actual cargo information, the monitoring data can be retrieved to determine the image information of the differing cargo. This ensures that during the unmanned vehicle's cargo docking process, even in an unattended environment, evidence of cargo loss can be preserved, allowing the cause of loss to be identified and preventing further loss.
[0055] In some embodiments, if the first autonomous vehicle is equipped with a camera, then determining the environmental information surrounding the first autonomous vehicle using the first autonomous vehicle includes:
[0056] The scanning data is determined by using a camera to perform a coverage scan around the first unmanned vehicle;
[0057] Environmental information is determined based on the scan data.
[0058] Specifically, cameras generally have basic functions such as video recording, transmission, and still image capture. They acquire images through a lens, and then the camera's internal photosensitive components and control components process the images and convert them into digital signals that a computer can recognize. These signals are then input to a computer via a parallel port or USB connection, where software reconstructs the image. By equipping the first unmanned vehicle with a camera, a comprehensive scan can be performed around the vehicle to obtain real-time images of the entire vicinity.
[0059] In some embodiments, when environmental information meets the collection conditions, collecting monitoring data determined based on the environmental information includes:
[0060] When the environmental information shows that a person to be identified has passed by, the distance between the person to be identified and the first unmanned vehicle is determined.
[0061] When the spacing meets the preset distance threshold, the environmental information meets the collection conditions, and the monitoring data determined based on the environmental information is collected.
[0062] Specifically, the "person to be identified" refers to the individual who may have caused the loss of goods. Since the goods are inside the cargo compartment of the unmanned vehicle and cannot simply disappear, human error is a significant factor in the loss. Real-time images from the environmental information system can determine the distance between the person to be identified and the unmanned vehicle when they are nearby. This distance can be obtained through analysis of camera scanning data or through a distance measuring device. A distance threshold is a standard for judging whether the person to be identified could have caused the loss of goods, and it is determined based on experience. When the distance meets the pre-set threshold, it indicates that the person to be identified is relatively close to the unmanned vehicle, and if the goods are lost, it is likely due to the person to be identified. They may have mistakenly taken or stolen the goods. In this case, it is necessary to collect monitoring data determined based on the environmental information; that is, to store images of the person to be identified when they are near the unmanned vehicle, as evidence to trace the cause of the loss.
[0063] In some embodiments, the monitoring data includes effective monitoring time and effective monitoring images; therefore, the monitoring data collected based on environmental information includes:
[0064] Determine the initial and end times when the person to be identified passes the first unmanned vehicle within a distance threshold;
[0065] The effective monitoring time is determined based on the initial and end times.
[0066] Collect environmental information within the effective monitoring period to determine the effective monitoring images.
[0067] Specifically, monitoring data includes effective monitoring time and effective monitoring images. In other words, monitoring data is a specific monitoring image within a certain time period. In this embodiment, the monitoring data is the monitoring image within a distance threshold when the person to be identified passes the first unmanned vehicle. Therefore, the process of collecting monitoring data determined based on environmental information can be as follows: determine the initial and final times when the person to be identified passes the first unmanned vehicle within the distance threshold. That is, the initial and final times when the person to be identified might pick up goods. The time period between the initial and final times is the effective monitoring time. Environmental information within the effective monitoring time is collected, and the images in this environmental information include the person to be identified. The environmental information within the effective monitoring time is the effective monitoring image. Each effective monitoring image contains a series of action images of the person to be identified within the effective monitoring time. The effective monitoring image is the monitoring data to be stored in the server or control system.
[0068] In some embodiments, the first unmanned vehicle includes at least one cargo compartment; then determining the target cargo information includes:
[0069] Determine the scheduled unloading cargo information for the first unmanned vehicle;
[0070] The target cargo information is determined based on the pre-arranged unloading cargo information of the first unmanned vehicle;
[0071] The scheduled unloading cargo information includes the number of cargo holds scheduled for unloading and the total weight of the scheduled unloading cargo.
[0072] Specifically, the first unmanned vehicle can have multiple cargo compartments to carry goods. The planned unloading cargo information consists of the number of cargo compartments to be unloaded onto the second unmanned vehicle according to the mission requirements and the total weight of the cargo. That is, the planned unloading cargo compartment number and the planned unloading cargo total weight. The target cargo information is determined based on the planned unloading cargo compartment number and the planned unloading cargo total weight.
[0073] In some embodiments, if the target cargo information is inconsistent with the actual cargo information, retrieving monitoring data includes:
[0074] Actual cargo information includes the number of cargo holds currently being unloaded and the total weight of cargo currently being unloaded;
[0075] If either the current number of unloaded cargo holds or the total weight of unloaded cargo is inconsistent with the target cargo information, retrieve the monitoring data.
[0076] Specifically, the actual cargo information includes the current number of cargo holds being unloaded and the total weight of the cargo being unloaded. If either the current number of cargo holds being unloaded or the total weight of the cargo being unloaded does not match the target cargo information, it indicates that the cargo is lost. A discrepancy between the current number of cargo holds being unloaded and the planned number of cargo holds being unloaded indicates that the cargo holds are missing, which means the cargo is lost. Similarly, a discrepancy between the current total weight of the cargo being unloaded and the planned total weight of the cargo being unloaded also indicates that the cargo is missing. It is possible that unidentified personnel may directly take cargo holds or remove cargo from them, thus causing a discrepancy between the target cargo information and the actual cargo information. In this case, it is necessary to retrieve monitoring data and analyze it to determine the cause.
[0077] In some embodiments, determining image information of differing goods from monitoring data includes:
[0078] The surveillance data includes at least one valid surveillance video clip;
[0079] Determine the target surveillance image based on valid surveillance images;
[0080] Image information of different goods is determined based on target surveillance footage.
[0081] Specifically, because the number of people to be identified is uncertain, and the frequency and timing of their passage near the first unmanned vehicle are also uncertain, the number or number of effective surveillance video segments is also uncertain. There may be several segments. When the target cargo information does not match the actual cargo information, the surveillance data is retrieved. Suspicious individuals are identified one by one from these effective surveillance videos. When the target individual is finally determined, they are the person who took the cargo. The effective surveillance video containing this target individual is called the target surveillance video. From this target surveillance video, detailed information can be obtained regarding what the discrepancy was, when it was lost, how it was lost, and who the target individual is, etc.
[0082] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.
[0083] The following are embodiments of the apparatus of this application, which can be used to execute the embodiments of the method of this application. For details not disclosed in the embodiments of the apparatus of this application, please refer to the embodiments of the method of this application.
[0084] Figure 3 This is a schematic diagram of a cargo anti-loss device provided in an embodiment of this application during unmanned vehicle shuttle operations. Figure 3 As shown, the cargo anti-loss device during the unmanned vehicle shuttle process includes:
[0085] The environmental information determination module 301 is configured to determine the environmental information around the first unmanned vehicle using the first unmanned vehicle.
[0086] The monitoring data determination module 302 is configured to collect monitoring data determined based on the environmental information when the environmental information meets the collection conditions.
[0087] The target cargo information determination module 303 is configured to determine target cargo information;
[0088] The actual cargo information determination module 304 is configured to determine the actual cargo information unloaded by the first unmanned vehicle when the first unmanned vehicle connects with the second unmanned vehicle.
[0089] The monitoring data retrieval module 305 is configured to identify the discrepancy between the target cargo information and the actual cargo information, and retrieve the monitoring data if the target cargo information is inconsistent with the actual cargo information.
[0090] The image information determination module 306 is configured to determine image information of the different goods from the monitoring data.
[0091] In some embodiments, the first driverless vehicle is equipped with a camera. Figure 3 The environmental information determination module 301 includes:
[0092] The scanning data is determined by using a camera to perform a coverage scan around the first unmanned vehicle;
[0093] Environmental information is determined based on the scan data.
[0094] In some embodiments, Figure 3 The monitoring data determination module 302 includes:
[0095] When the environmental information shows that a person to be identified has passed by, the distance between the person to be identified and the first unmanned vehicle is determined.
[0096] When the spacing meets the preset distance threshold, the environmental information meets the collection conditions, and the monitoring data determined based on the environmental information is collected.
[0097] In some embodiments, the monitoring data includes valid monitoring time and valid monitoring images. Figure 3 The monitoring data determination module 302 includes:
[0098] Determine the initial and end times when the person to be identified passes the first unmanned vehicle within a distance threshold;
[0099] The effective monitoring time is determined based on the initial and end times.
[0100] Collect environmental information within the effective monitoring period to determine the effective monitoring images.
[0101] In some embodiments, the first unmanned vehicle includes at least one cargo compartment; then Figure 3 The target cargo information determination module 303 includes:
[0102] Determine the scheduled unloading cargo information for the first unmanned vehicle;
[0103] The target cargo information is determined based on the pre-arranged unloading cargo information of the first unmanned vehicle;
[0104] The scheduled unloading cargo information includes the number of cargo holds scheduled for unloading and the total weight of the scheduled unloading cargo.
[0105] In some embodiments, Figure 3 The monitoring data retrieval module 305 includes:
[0106] Actual cargo information includes the number of cargo holds currently being unloaded and the total weight of cargo currently being unloaded;
[0107] If either the current number of unloaded cargo holds or the total weight of unloaded cargo is inconsistent with the target cargo information, retrieve the monitoring data.
[0108] In some embodiments, Figure 3 Image information determination module 306 includes:
[0109] The surveillance data includes at least one valid surveillance video clip;
[0110] Determine the target surveillance image based on valid surveillance images;
[0111] Image information of different goods is determined based on target surveillance footage.
[0112] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0113] Figure 4 This is a schematic diagram of the electronic device 4 provided in an embodiment of this application. Figure 4 As shown, the electronic device 4 of this embodiment includes a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, it implements the steps in the various method embodiments described above. Alternatively, when the processor 401 executes the computer program 403, it implements the functions of each module / unit in the various device embodiments described above.
[0114] Electronic device 4 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 4 may include, but is not limited to, processor 401 and memory 402. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or different components.
[0115] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
[0116] The memory 402 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM of the electronic device 4. The memory 402 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 4. The memory 402 can also include both internal and external storage units of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device.
[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0118] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.
[0119] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for preventing cargo loss during unmanned vehicle shuttle operations, characterized in that, The method is applied to a control system, and the method includes: The first unmanned vehicle is used to determine the environmental information surrounding the first unmanned vehicle; When the environmental information meets the collection conditions, the monitoring data determined based on the environmental information is collected; Determine the target cargo information; When the first unmanned vehicle connects with the second unmanned vehicle, the actual cargo information unloaded by the first unmanned vehicle is determined; If the target cargo information is inconsistent with the actual cargo information, the discrepancy cargo is identified, and the monitoring data is retrieved. Determine the image information of the differentiated goods from the monitoring data; If the first unmanned vehicle is equipped with a camera, then determining the environmental information around the first unmanned vehicle using the first unmanned vehicle includes: The camera performs a coverage scan around the first unmanned vehicle to determine the scan data. The environmental information is determined based on the scan data; When the environmental information meets the collection conditions, the collection of monitoring data determined based on the environmental information includes: When the environmental information shows that a person to be identified has passed by, the distance between the person to be identified and the first unmanned vehicle is determined. When the spacing meets a preset distance threshold, the environmental information meets the collection conditions, and monitoring data determined based on the environmental information is collected.
2. The method according to claim 1, characterized in that, The monitoring data includes valid monitoring time and valid monitoring images. Therefore, the collection of monitoring data determined based on the environmental information includes: Determine the initial and end times within the distance threshold when the person to be identified passes the first unmanned vehicle; The effective monitoring time is determined based on the initial time and the end time. The effective monitoring images are determined by collecting environmental information within the effective monitoring time period.
3. The method according to claim 1, characterized in that, The first unmanned vehicle includes at least one cargo compartment; therefore, the information on the determined target cargo includes: Determine the scheduled unloading cargo information for the first unmanned vehicle; The target cargo information is determined based on the pre-arranged unloading cargo information of the first unmanned vehicle; The scheduled unloading cargo information includes the number of cargo holds scheduled for unloading and the total weight of the scheduled unloading cargo.
4. The method according to claim 1, characterized in that, If the target cargo information is inconsistent with the actual cargo information, retrieving the monitoring data includes: The actual cargo information includes the number of cargo holds currently being unloaded and the total weight of the cargo currently being unloaded. If either the current number of unloaded cargo holds or the total weight of unloaded cargo is inconsistent with the target cargo information, the monitoring data is retrieved.
5. The method according to claim 1 or 2, characterized in that, The step of determining the image information of the different goods from the monitoring data includes: The monitoring data includes at least one valid monitoring video segment; The target surveillance image is determined based on the effective surveillance images; The image information of the differentiated goods is determined based on the target surveillance image.
6. A cargo anti-loss device during unmanned vehicle shuttle operation, characterized in that, include: Environmental information determination module: used to determine the environmental information around the first unmanned vehicle; Monitoring data determination module: used to collect monitoring data determined based on the environmental information when the environmental information meets the collection conditions; Target cargo information determination module: used to determine target cargo information; Actual cargo information determination module: used to determine the actual cargo information unloaded by the first unmanned vehicle when the first unmanned vehicle docks with the second unmanned vehicle; Monitoring data retrieval module: used to identify the discrepancy between the target cargo information and the actual cargo information, and retrieve the monitoring data if the target cargo information is inconsistent with the actual cargo information; Image information determination module: used to determine the image information of the differentiated goods from the monitoring data; The first unmanned vehicle is equipped with a camera, and the environmental information determination module is specifically used to: perform a coverage scan around the first unmanned vehicle using the camera to determine scan data; and determine the environmental information based on the scan data. The monitoring data determination module is specifically used for: when a person to be determined passes through the environmental information, determining the distance between the person to be determined and the first unmanned vehicle; when the distance meets a preset distance threshold, the environmental information meets the collection conditions, and monitoring data determined based on the environmental information is collected.
7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.
8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.
Citation Information
Patent Citations
Unmanned storage intelligent integrated monitoring and scheduling system and control method thereof
CN108427423A
Article loss warning method and device, storage medium and electronic device
CN110853278A