Cargo position updating method and device, program product and electronic equipment

Abstract

The invention discloses a cargo position updating method and device, a program product and electronic equipment, and relates to the technical field of augmented reality, and the method comprises the steps: controlling preset equipment to collect a first identifier and L second identifiers, the first identifier is an identifier of a target position, the target position is used for storing L cargoes, and the L second identifiers are L; each second identifier is the identifier of one cargo in the target position, then L verification results are determined according to the first identifier and the L second identifiers, the L verification results are in one-to-one correspondence with the L cargos, the L verification results are comparison results between the preset positions of the L cargos and the target position, and then the L cargos are verified according to the L verification results. The position of the cargo corresponding to a first verification result is updated according to the first verification result in the L verification results, and the first verification result is used for representing that the preset position of the cargo is different from the target position. The technical problem that in the prior art, the efficiency of detecting and adjusting the cargo position in a manual mode is low is solved.

Description

Technical Field

[0001] The present application relates to the field of augmented reality technology, and in particular to a method, device, program product and electronic device for updating the location of goods. Background Art

[0002] In application scenarios such as passenger aircraft luggage loading, warehouse cargo management, library book management, and supermarket merchandise warehousing, there is a link involved in confirming cargo identification and cargo storage locations. That is, after multiple cargoes are stored in a designated location, it is necessary to verify the correctness of each cargo and the storage location of the cargo. In the prior art, the expected storage location of the cargo is usually printed on an assembly confirmation sheet, and then each cargo and the storage location of the cargo are checked based on the assembly confirmation sheet by manual observation. However, when a large number of cargoes need to be checked, the manual observation method will result in low efficiency in detecting and adjusting the storage location of the cargoes.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The present application provides a method, device, program product and electronic device for updating the location of goods, so as to at least solve the technical problem of low efficiency in detecting and adjusting the location of goods manually in the prior art.

[0005] According to one aspect of the present application, a method for updating the location of goods is provided, comprising: controlling a preset device to collect a first identifier and L second identifiers, wherein the preset device is an augmented reality device, the first identifier is an identifier of a target location, the target location is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a goods in the target location; determining L verification results based on the first identifier and the L second identifiers, wherein the L verification results correspond to the L goods one-to-one, and the L verification results are comparison results between the preset locations of the L goods and the target locations; updating the location of the goods corresponding to the first verification result based on a first verification result among the L verification results, wherein the first verification result is used to characterize that the preset location of the goods is different from the target location.

[0006] Optionally, in the process of controlling the preset device to collect the first identification and L second identifications, the method for updating the cargo location also includes: controlling the preset device to collect a first picture through voice commands, wherein the first picture is a picture including the target location; inputting the first picture into the preset model to obtain first information, wherein the first information is the position information of the first nameplate in the first picture, the first nameplate is used to record the first identification, and the preset model is a neural network model obtained by training N training pictures, N is a positive integer, and each training picture includes at least one cargo; cutting the first picture based on the first information to obtain the first identification corresponding to the first nameplate.

[0007] Optionally, in the process of controlling the preset device to collect the first identifier and L second identifiers, the method for updating the cargo location also includes: after obtaining the first identifier, verifying the first identifier according to a first preset table, wherein the first preset table at least includes identifiers of locations where cargo is allowed to be stored; when the first identifier does not exist in the first preset table, generating a first warning message, and transmitting the first warning data to a visualization terminal of the preset device, wherein the first warning message is used to prompt the user to re-collect the first identifier; when the first identifier exists in the first preset table, controlling the preset device to collect a second picture through voice commands, wherein the second picture is a picture including L cargoes; and determining the L second identifiers based on the second picture.

[0008] Optionally, in the process of determining L verification results based on the first identifier and L second identifiers, the method for updating the cargo location also includes: generating L request data based on the first identifier and L second identifiers, wherein each of the L request data includes at least a first identifier and a second identifier; verifying the L request data according to a second preset table to obtain L verification results, wherein the second preset table includes at least a correspondence between an identifier of a location where cargo is allowed to be stored and an identifier of cargo that is allowed to be stored at the location.

[0009] Optionally, in the process of verifying L request data according to the second preset table to obtain L verification results, the method for updating the cargo location also includes: scanning the second preset table row by row to obtain M row data, where M is a positive integer; when each row data in the M row data does not simultaneously include the first identifier and the second identifier corresponding to the i-th request data, determining the i-th verification result as the first verification result, where i is a positive integer less than or equal to L; when any one row data in the M row data simultaneously includes the first identifier and the second identifier corresponding to the i-th request data, determining the i-th verification result as the second verification result, where the second verification result is used to characterize that the preset position of the cargo is the same as the target position.

[0010] Optionally, after determining L verification results based on the first identifier and L second identifiers, the method for updating the cargo location also includes: determining a marking color for each of the L cargoes based on the L verification results, wherein the marking color corresponding to the first verification result is different from the marking color corresponding to the second verification result; drawing the second picture onto a blank canvas to obtain a first canvas; drawing a marking box for each cargo in the first canvas based on the marking color of the cargo to obtain a target canvas; and transmitting the target canvas to a visualization terminal of a preset device.

[0011] Optionally, after updating the location of the goods corresponding to the first verification result according to the first verification result among the L verification results, the method for updating the location of the goods also includes: updating the first verification result among the L verification results according to the updated location of the goods; and storing the updated L verification results in a preset database.

[0012] According to another aspect of the present application, a device for updating the location of goods is also provided, including: a collection unit, used to control a preset device to collect a first identifier and L second identifiers, wherein the preset device is an augmented reality device, the first identifier is an identifier of a target location, the target location is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a cargo in the target location; a first determination unit, used to determine L verification results based on the first identifier and the L second identifiers, wherein the L verification results correspond one-to-one to the L goods, and the L verification results are comparison results between the preset locations of the L goods and the target locations; a first update unit, used to update the location of the cargo corresponding to the first verification result based on a first verification result among the L verification results, wherein the first verification result is used to characterize that the preset location of the cargo is different from the target location.

[0013] According to another aspect of the present application, a computer program product is provided, in which a computer program is stored, wherein when the computer program is run, the computer program product is controlled to execute any one of the above methods for updating the location of the cargo.

[0014] According to another aspect of the present application, an electronic device is also provided, wherein the electronic device includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement any of the above-mentioned methods for updating the cargo location.

[0015] In the present application, firstly, a preset device is controlled to collect a first identifier and L second identifiers, wherein the preset device is an augmented reality device, the first identifier is an identifier of a target position, the target position is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a good in the target position. Afterwards, the present application determines L verification results based on the first identifier and the L second identifiers, wherein the L verification results correspond one-to-one to the L goods, and the L verification results are comparison results between the preset positions and the target positions of the L goods. Then, the present application updates the position of the goods corresponding to the first verification result based on the first verification result among the L verification results, wherein the first verification result is used to characterize that the preset position of the goods is different from the target position.

[0016] From the above content, it can be seen that the present application collects the location identification (i.e., the first identification) of multiple storage goods and the identification (i.e., the second identification) of each stored goods through an AR device (i.e., the preset device), without the need for manual observation, thereby improving the efficiency of subsequent detection of the storage location of the goods. Afterwards, the present application determines whether the storage location of each goods meets expectations through the matching relationship between the location identification and the identification of each goods that need to be confirmed (i.e., L verification results). Then, the present application readjusts the storage location of the goods whose verification result is the first verification result (i.e., the goods whose storage location does not meet expectations), thereby omitting the step of screening the goods whose storage location does not meet expectations through manual observation, thereby improving the efficiency of subsequent adjustment of the storage location of the goods.

[0017] It can be seen that the present application achieves the technical effect of improving the efficiency of detecting and adjusting the position of goods by determining the matching relationship between the location identification collected by the AR device and the identification of each goods, thereby solving the technical problem of low efficiency in detecting and adjusting the position of goods manually in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a flow chart of an optional method for updating the location of goods according to an embodiment of the present application;

[0020] Figure 2 is a flowchart of another optional method for updating the location of goods according to an embodiment of the present application;

[0021] Figure 3 is a workflow diagram of an optional cargo location update system according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of an optional device for updating the location of goods according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] It should also be noted that the relevant information (including the location information of the stored goods and the information of the stored goods) and data (including but not limited to the data for display and analysis) involved in this application are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant users or institutions. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or institution through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or institution.

[0027] In addition, the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant information and data involved in this application comply with the relevant laws, regulations and standards of the relevant regions, and necessary confidentiality measures have been taken and do not violate public order and good morals. In addition, this application provides corresponding operation entrances for users to choose to agree to authorization or refuse authorization. If the user chooses to refuse authorization, he / she will enter the corresponding expert decision-making process.

[0028] According to an embodiment of the present application, an embodiment of a method for updating the location of goods is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] The present application provides a cargo location update system (hereinafter referred to as the update system) for executing the cargo location update method in the present application. The update system can be implemented in software or a combination of software and hardware.

[0030] In an optional embodiment, Figure 1 is a flow chart of an optional method for updating the location of goods according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0031] Step S101, controlling a preset device to collect a first identifier and L second identifiers.

[0032] In step S101, the preset device is an augmented reality device, the first identifier is an identifier of a target location, the target location is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a good in the target location.

[0033] Optionally, the preset device is an AR (Augmented Reality) device, where augmented reality technology is a technology that superimposes virtual information onto the real world. The AR device can combine virtual digital information such as images, sounds, and touch with the real world through computer technology, thereby enhancing the user's perception and understanding of the real world.

[0034] Optionally, the update system can recognize the user voice of the AR device, that is, the user of the AR device can control the AR device through voice to collect the first identifier and L second identifiers. The voice control device can allow the user to complete the collection operation through simple verbal instructions, thereby eliminating tedious operating steps and improving the use efficiency of the AR device and user experience.

[0035] Step S102: determine L verification results according to the first identifier and L second identifiers.

[0036] In step S102, L verification results correspond to the L goods one by one, and the L verification results are comparison results between the preset positions and the target positions of the L goods.

[0037] Optionally, the verification result is a first verification result or a second verification result, wherein the first verification result is used to characterize that the preset position of the goods is different from the target position, and the second verification result is used to characterize that the preset position of the goods is the same as the target position.

[0038] Optionally, the update system verifies the storage locations of L goods based on the first identifier and L second identifiers, thereby avoiding the step of proofreading the storage locations of L goods through manual observation in the prior art, thereby avoiding observation errors caused by human eye observation, and further improving the accuracy of detecting the storage locations of the L goods.

[0039] Step S103: updating the location of the goods corresponding to the first verification result according to the first verification result among the L verification results.

[0040] In step S103, the first verification result is used to indicate that the preset position of the goods is different from the target position.

[0041] Optionally, after the update system updates the position of the goods corresponding to the first verification result, the update system controls the AR device to re-collect the identification of the goods and the new location where the goods are stored, and then, the update system controls the AR device to re-detect whether the new location where the goods are stored is the preset location corresponding to the goods based on the matching relationship between the identification of the goods and the new location where the goods are stored.

[0042] From the above content, it can be seen that the present application collects the location identification (i.e., the first identification) of multiple storage goods and the identification (i.e., the second identification) of each stored goods through an AR device (i.e., the preset device), without the need for manual observation, thereby improving the efficiency of subsequent detection of the storage location of the goods. Afterwards, the present application determines whether the storage location of each goods meets expectations through the matching relationship between the location identification and the identification of each goods that need to be confirmed (i.e., L verification results). Then, the present application readjusts the storage location of the goods whose verification result is the first verification result (i.e., the goods whose storage location does not meet expectations), thereby omitting the step of screening the goods whose storage location does not meet expectations through manual observation, thereby improving the efficiency of subsequent adjustment of the storage location of the goods.

[0043] It can be seen that the present application achieves the technical effect of improving the efficiency of detecting and adjusting the position of goods by determining the matching relationship between the location identification collected by the AR device and the identification of each goods, thereby solving the technical problem of low efficiency in detecting and adjusting the position of goods manually in the prior art.

[0044] In an optional embodiment, the update system first controls a preset device through voice commands to capture a first picture, wherein the first picture is a picture including a target location. Afterwards, the update system inputs the first picture into a preset model to obtain first information, wherein the first information is position information of a first nameplate in the first picture, the first nameplate is used to record a first identification, the preset model is a neural network model obtained by training N training pictures, N is a positive integer, and each training picture includes at least one cargo. Then, the update system cuts the first picture based on the first information to obtain a first identification corresponding to the first nameplate.

[0045] Optionally, the first image includes at least a first nameplate, and the update system obtains the position information of the first nameplate in the first image (i.e., the first information) through a preset model detection. Afterwards, the update system can obtain the identification of the target position recorded in the first nameplate based on the first information detection through OCR (Optical Character Recognition) technology, thereby avoiding the step of obtaining the first identification through manual observation in the prior art, thereby improving the collection efficiency of the first identification.

[0046] Optionally, the updating system preferentially selects Paddle OCR (an open source optical character recognition tool) to perform OCR recognition on the first image.

[0047] Optionally, the preset model is trained by the following steps:

[0048] (1) Collecting images required for model training: According to the actual scenario of updating the system, take photos of multiple locations where goods are stored and the goods stored in each location to obtain N training images, where N is preferably set to a positive integer greater than 200.

[0049] In addition, to ensure the training effect, it is recommended that the number of locations included in the N training images should be equal to the number of goods.

[0050] Optionally, the N training images contain at least nameplate information of multiple locations and nameplate information of multiple goods. In the process of collecting training images, there is no need to align the camera of the image acquisition device in advance before taking pictures. Instead, the update system controls the camera of the image acquisition device to take pictures of the locations and goods from different angles, thereby obtaining training images of different angles, different inclinations and different clarity levels, thereby improving the recognition accuracy of the first image by the preset model obtained by subsequent training.

[0051] Optionally, the updating system can also achieve the purpose of increasing the sample size of the training images by rotating, reversing, scaling, blurring, etc. the images acquired at the initial collection.

[0052] (2) Image preprocessing: First, install the Python (a programming language) development environment, open the CMD (Command Prompt) command tool, enter the "pip install labelimg" command to install the image annotation tool, and then enter the "labelimg" command in the CMD command tool and press Enter to start the image annotation tool.

[0053] Then, click the "open dir" option in the image annotation tool to create a directory where the N training images are stored. The file format can be selected as the "yolo" format. After that, click the "Create RectBox" option to annotate the first and second identifiers corresponding to each training image. The identifier type of the first identifier can be set to "location" and the identifier type of the second identifier can be set to "thing".

[0054] (3) Model training: The update system uses the YOLO (You Only Look Once, a model based on a real-time target detection algorithm) model as the initial model, divides the N labeled training images into a training set and a test set, and then trains the initial model based on the training set and the test set to obtain the preset model.

[0055] Optionally, the V8 version of the YOLO model is preferentially selected as the initial model. The YOLOV8 model can provide a variety of model files for the update system to choose from. The update system can use the NVIDIA independent graphics card to train the initial model. Finally, the update system uses the trained best.pt model as the preset model.

[0056] (4) Model deployment and application: The update system can deploy the preset model through Django (a framework for quickly building reliable and scalable websites and applications), thereby obtaining an interface for file upload, which can save the uploaded file (i.e., the first image to be identified) to the disk. Then, the update system uses UUID (Universally Unique Identifier) ​​to rename the uploaded file and pass the file path to the preset model for identification. The data obtained by the preset model identification is then parsed and reassembled, and encapsulated to obtain the data structure corresponding to the front-end display of the AR device. Finally, the data structure is returned to the front-end of the AR device for display.

[0057] Optionally, the update system can also convert the best.pt model into the "onnx" format. After that, the update system encapsulates the best.pt model in the "onnx" format through Unity (a cross-platform development engine) based on the onnx runtime (an inference library that provides cross-platform support), thereby obtaining an interface for file uploading.

[0058] In an optional embodiment, after obtaining the first identifier, the update system first verifies the first identifier according to a first preset table, wherein the first preset table includes at least identifiers of locations where goods are allowed to be stored, and then, if the first identifier does not exist in the first preset table, the update system generates a first warning message and transmits the first warning data to a visualization terminal of a preset device, wherein the first warning message is used to prompt the user to re-collect the first identifier.

[0059] In addition, when there is a first identifier in the first preset table, the update system controls the preset device to collect a second picture through voice commands, wherein the second picture is a picture including L goods, and then the update system determines L second identifiers based on the second picture.

[0060] Optionally, after the update system acquires the first identifier, the update system verifies the first identifier according to the first preset table by calling the backend API interface.

[0061] Optionally, an indicator light is provided on the AR device. When the first identifier does not exist in the first preset table, the indicator light of the AR device is displayed in red to prompt the user that the target position needs to be rescanned, thereby improving the collection accuracy of the first identifier.

[0062] Optionally, after the preset device captures the second image, the update system also inputs the second image into the preset model, and obtains L second identifiers corresponding to the second image through the preset model and OCR technology.

[0063] In an optional embodiment, the update system first generates L request data based on the first identifier and L second identifiers, wherein each of the L request data includes at least the first identifier and one second identifier. Thereafter, the update system verifies the L request data based on the second preset table to obtain L verification results, wherein the second preset table includes at least the correspondence between the identifier of the location where goods are allowed to be stored and the identifier of the goods allowed to be stored at the location.

[0064] Optionally, after the update system obtains the first identifier and L second identifiers, the update system encapsulates the obtained first identifier and second identifier into a data structure of an HTTP (Hypertext Transfer Protocol) request (i.e., request data). Thereafter, the update system submits the HTTP request to a preset interface, wherein the preset interface is at least used to verify the HTTP request based on the second preset table.

[0065] Optionally, the first preset table and the second preset table are both stored in a preset database. In addition, a third preset table is also stored in the preset database, wherein the third preset table is used to store information of goods to be stored.

[0066] Optionally, the first preset table is used to manage the location information of each location, and the location information includes at least the primary key, name, code and location of the location. The update system can also add other management fields to the first preset table based on the actual application scenario, such as the belonging area, new creation time and modification time.

[0067] Optionally, the second preset table is used to manage the correspondence between the cargo storage location and the cargo, and the correspondence between the cargo storage location and the cargo can be determined according to the specific application scenario. For example, in the book management application scenario, the location and the cargo can be directly set to a one-to-many correspondence, and there is no need to create a second preset table of entities separately; in the scenario of cargo hold loading of civil aviation passenger aircraft, the correspondence between the location and the cargo needs to be determined based on business information, that is, the flight number + flight date of the civil aviation passenger aircraft, so it is necessary to create a second preset table of entities separately. At this time, the fields in the second preset table include at least the primary key, the location unique code (that is, the first identifier), the cargo unique code (that is, the second identifier), and the batch (that is, the flight number + date).

[0068] Optionally, the third preset table is used to manage cargo information of each cargo, and the cargo information includes at least the primary key, name, code, size and weight of the cargo. Similarly, the update system can also add other management fields to the third preset table based on actual application scenarios.

[0069] In an optional embodiment, the update system first scans the second preset table row by row to obtain M row data, where M is a positive integer. Then, when each row data in the M row data does not simultaneously include the first identifier and the second identifier corresponding to the i-th request data, the update system determines that the i-th verification result is the first verification result, where i is a positive integer less than or equal to L. In addition, when any one row data in the M row data simultaneously includes the first identifier and the second identifier corresponding to the i-th request data, the update system determines that the i-th verification result is the second verification result, where the second verification result is used to characterize that the preset position of the goods is the same as the target position.

[0070] Optionally, when the update system determines that the i-th verification result is the first verification result, the update system sets the indicator light of the AR device to yellow; when the update system determines that the i-th verification result is the second verification result, the update system sets the indicator light of the AR device to green.

[0071] In an optional embodiment, after determining L verification results based on the first identifier and L second identifiers, the update system first determines the marking color of each of the L goods based on the L verification results, wherein the marking color corresponding to the first verification result is different from the marking color corresponding to the second verification result. Then, the update system draws the second picture on a blank canvas to obtain a first canvas. Then, the update system draws a marking box for each goods in the first canvas according to the marking color of the goods to obtain a target canvas. Finally, the update system transmits the target canvas to a visualization terminal of a preset device.

[0072] Optionally, after obtaining L verification results, the update system sets a labeling box of different colors for each of the goods in the second picture according to different verification results, and transmits the labeled target canvas to the AR device for display, thereby achieving the purpose of intuitively visualizing the L verification results.

[0073] In an optional embodiment, after updating the location of the goods corresponding to the first verification result based on the first verification result among the L verification results, the update system can also update the first verification result among the L verification results based on the updated location of the goods, and then the update system stores the updated L verification results in a preset database.

[0074] Optionally, a fourth preset table is also stored in the preset database, wherein the fourth preset table is used to store status information after the cargo location is confirmed, and the status information at least includes a primary key (ID), a unique code of the location-cargo correspondence, a batch, and a confirmation result (i.e., a verification result). Similarly, the update system can also add other management fields to the fourth preset table based on actual application scenarios.

[0075] Optionally, the first preset table, the second preset table, the third preset table and the fourth preset table are all provided with corresponding add, delete, modify, check, import and export interfaces, and the above interfaces can be provided to the front-end page corresponding to the preset database for use, so as to manage the data in the preset table in a timely manner.

[0076] Optionally, when the indicator light of the AR device is yellow, the storage location of the i-th cargo is abnormal and the current storage location of the i-th cargo needs to be adjusted. After updating the locations of all abnormally stored cargo at the target location, the update system saves the updated L verification results to a preset database through voice commands, and publishes the updated L verification results to MQ (Message Queue), thereby achieving the purpose of sending the updated verification results to users who subscribe to MQ in a timely manner.

[0077] In an optional embodiment, Figure 2 is a flowchart of another optional method for updating the cargo location according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0078] Step S201: voice-control the AR device to detect the location and identify the location information (ie, the first identifier).

[0079] Step S202: voice-control the AR device to perform batch detection on the goods in the target location to obtain goods information (ie, the second identifier).

[0080] Step S203, verifying the correlation between the cargo information and the location information to obtain a verification result.

[0081] Step S204: persistently store the verification result.

[0082] In an optional embodiment, Figure 3 is a workflow diagram of an optional cargo location update system according to an embodiment of the present application, such as Figure 3 As shown, the update system includes at least an AR client, a server API and a model reasoning API, wherein the AR client is the client of the AR device, the server API is at least used for persistent storage of the verification results, and the model reasoning API is the backend API of the preset model.

[0083] First, the user turns on the AR device through voice control, and controls the AR device to scan the target location through voice control. After that, the location picture (i.e., the first picture) scanned by the AR device is submitted to the preset model through the model reasoning API. Then, the preset model obtains the location identification information (i.e., the first identification) based on the location picture recognition, and verifies the location identification information according to the first preset table in the preset database, and returns the verification result corresponding to the location identification information to the model reasoning API.

[0084] Then, the verification result corresponding to the location identification information is returned to the AR client through the model inference API. When the verification result corresponding to the location identification information represents an identification of a location picture in the first preset table, the user controls the AR device through voice to batch scan multiple goods in the target location, and controls the AR device to submit the scanned pictures containing multiple goods information to the preset model.

[0085] Afterwards, the preset model recognizes the picture with multiple goods information and obtains the identification information of the multiple goods. Then, the identification information of the multiple goods is returned to the AR client. The AR client submits the identification information of the multiple goods and locations to the server API, and then verifies the correspondence between the multiple goods identification information and the location identification information by calling the server API, thereby obtaining the assembly verification result (i.e., L verification results).

[0086] Next, the assembly verification results are returned to the AR client for display. The user can adjust the positions of all goods with assembly abnormalities according to the assembly verification results displayed in the AR client and update the assembly verification results. After that, the user uses voice control to persistently store the updated assembly verification results and publish the updated assembly verification results to MQ.

[0087] Optionally, the AR client includes some UI interfaces, which can be implemented using the UX components in the MRTK toolkit. The UI interface can be used to display the status of the assembly confirmation process and remind the user to adjust the position of the goods with abnormal assembly. In addition, the application can also implement the voice recognition function of the AR client through the "Voice Command" attribute in the preset component, thereby supporting the user to interact with the UI interface of the AR client through voice. For example, the AR device collects the user's voice commands and matches them with the "Voice Command" attributes of each interactive component of the UI interface, and executes the corresponding operation of the interactive component after the match is successful.

[0088] Optionally, the present application can also design relevant voice recognition instructions according to functional requirements, wherein the voice recognition instructions include starting assembly confirmation, location scanning, cargo scanning, submitting verification results, and completing assembly confirmation. Then, the keyword recognizer is used to bind the keywords and related operation methods in a dictionary manner, and the binding results are registered in the keyword recognizer. When the AR device recognizes the voice keyword, it can trigger the execution of the corresponding method, thereby achieving the purpose of voice control.

[0089] For example, the development of AR client functions is mainly based on Unity, and its development language is C# (a programming language). When the AR client receives the "position scan" command, the AR device begins to periodically capture video key frames of the camera video stream. The time interval for capturing key frames can be set to 0.3s. After that, the acquired key frame images are used as input images of the preset model (corresponding to the first image and the second image in this application) for recognition. After the preset model recognition is completed, the recognition result is returned, where the recognition result includes the first identifier and the second identifier. Then, the AR client will submit the recognized first identifier and the second identifier to the back-end API interface for verification, thereby obtaining the verification results corresponding to the first identifier and the second identifier.

[0090] Optionally, after the AR client receives the verification results corresponding to the first identifier and the second identifier, the goods in the key frame image are marked with annotation boxes of different colors according to the verification results through the OpenCVSharp (a cross-platform computer vision library) tool, and the marked key frame image is pushed to the visualization screen of the AR device for display, thereby prompting the user to promptly deal with the goods with abnormal storage locations, thereby ensuring the correctness of the assembly results of multiple goods. After the processing of the goods with abnormal storage locations is completed, the user uses voice commands to persist the goods assembly confirmation results (i.e., the updated L verification results) to the preset database, and publishes the changes to MQ at the same time, so as to facilitate subsequent processing by the subscription service.

[0091] Optionally, the location identification information (first identification), the cargo identification information (second identification) and the L verification results are not only stored in the cache of the AR device, but are also persistently saved as file information in the Resources folder, thereby preventing data loss caused by failure of the AR device.

[0092] Optionally, the present application combines target detection technology, AR technology and Unity engine technology to implement a cargo location update system.

[0093] Optionally, the technical solution of the present application has the following advantages:

[0094] (1) Improved user experience in the assembly confirmation process: The existing assembly confirmation process mainly uses assembly confirmation sheets or handheld terminals for confirmation, which requires a lot of manual proofreading and manual operation. This application requires almost no manual proofreading and manual operation, and the entire process is completed through AR devices and voice control, thereby improving the user experience.

[0095] (2) Improving the convenience of system operation: Full voice command control is one of the advantages of this application. Through voice control, there is no need for manual operation of the terminal device, thereby reducing the difficulty of operating the device.

[0096] (3) Improve the efficiency of assembly confirmation work: This system can perform batch assembly confirmation on multiple goods, instead of manually checking them one by one, thereby reducing time costs and improving the efficiency of assembly confirmation work.

[0097] (4) Improve the accuracy of assembly confirmation work: This system uses AR equipment to automatically identify location information and cargo information throughout the process, without the need for manual proofreading, thereby improving the accuracy of assembly confirmation work.

[0098] From the above content, it can be seen that the present application collects the location identification (i.e., the first identification) of multiple storage goods and the identification (i.e., the second identification) of each stored goods through an AR device (i.e., the preset device), without the need for manual observation, thereby improving the efficiency of subsequent detection of the storage location of the goods. Afterwards, the present application determines whether the storage location of each goods meets expectations through the matching relationship between the location identification and the identification of each goods that need to be confirmed (i.e., L verification results). Then, the present application readjusts the storage location of the goods whose verification result is the first verification result (i.e., the goods whose storage location does not meet expectations), thereby omitting the step of screening the goods whose storage location does not meet expectations through manual observation, thereby improving the efficiency of subsequent adjustment of the storage location of the goods.

[0099] It can be seen that the present application achieves the technical effect of improving the efficiency of detecting and adjusting the position of goods by determining the matching relationship between the location identification collected by the AR device and the identification of each goods, thereby solving the technical problem of low efficiency in detecting and adjusting the position of goods manually in the prior art.

[0100] According to another aspect of the embodiment of the present application, a device for updating the location of goods is also provided. Figure 4 is a schematic diagram of an optional device for updating the location of goods according to an embodiment of the present application, such as Figure 4 As shown, the device for updating the cargo position includes: a collection unit 401 , a first determination unit 402 , and a first update unit 403 .

[0101] Optionally, the collection unit is used to control the preset device to collect a first identifier and L second identifiers, wherein the preset device is an augmented reality device, the first identifier is an identifier of a target location, the target location is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a goods in the target location; the first determination unit is used to determine L verification results based on the first identifier and the L second identifiers, wherein the L verification results correspond to the L goods one-to-one, and the L verification results are comparison results between the preset locations and the target locations of the L goods; the first updating unit is used to update the location of the goods corresponding to the first verification result based on the first verification result of the L verification results, wherein the first verification result is used to characterize that the preset location of the goods is different from the target location.

[0102] In an optional embodiment, the collection unit includes: a first collection subunit, an input subunit and a picture cutting subunit.

[0103] Optionally, a first acquisition subunit is used to control a preset device to acquire a first picture through voice commands, wherein the first picture is a picture including a target location; an input subunit is used to input the first picture into a preset model to obtain first information, wherein the first information is position information of a first nameplate in the first picture, the first nameplate is used to record a first identification, and the preset model is a neural network model obtained by training N training pictures, N is a positive integer, and each training picture includes at least one cargo; a cutting subunit is used to cut the first picture based on the first information to obtain a first identification corresponding to the first nameplate.

[0104] In an optional embodiment, the collection unit further includes: a first verification subunit, a first generation subunit, a second collection subunit and a first determination subunit.

[0105] Optionally, a first verification subunit is used to verify the first identification according to a first preset table after obtaining the first identification, wherein the first preset table includes at least identifications of locations where goods are allowed to be stored; a first generation subunit is used to generate a first warning message when the first identification does not exist in the first preset table, and transmit the first warning data to a visualization terminal of a preset device, wherein the first warning message is used to prompt a user to re-collect the first identification; a second collection subunit is used to control the preset device to collect a second picture through voice commands when the first identification exists in the first preset table, wherein the second picture is a picture including L goods; a first determination subunit is used to determine L second identifications based on the second picture.

[0106] In an optional embodiment, the first determination unit includes: a second generation subunit and a second verification subunit.

[0107] Optionally, a second generating subunit is used to generate L request data based on a first identifier and L second identifiers, wherein each of the L request data includes at least a first identifier and a second identifier; a second verifying subunit is used to verify the L request data based on a second preset table to obtain L verification results, wherein the second preset table includes at least a correspondence between an identifier of a location where goods are allowed to be stored and an identifier of goods allowed to be stored at the location.

[0108] In an optional embodiment, the first determining unit further includes: a scanning subunit, a second determining subunit and a third determining subunit.

[0109] Optionally, a scanning subunit is used to scan the second preset table row by row to obtain M row data, where M is a positive integer; a second determination subunit is used to determine that the i-th verification result is the first verification result when each row data in the M row data does not simultaneously include the first identifier and the second identifier corresponding to the i-th request data, where i is a positive integer less than or equal to L; a third determination subunit is used to determine that the i-th verification result is the second verification result when any one row data in the M row data simultaneously includes the first identifier and the second identifier corresponding to the i-th request data, where the second verification result is used to characterize that the preset position of the goods is the same as the target position.

[0110] In an optional embodiment, the device for updating the cargo location further includes: a second determining unit, a first drawing unit, a second drawing unit, and a first transmission unit.

[0111] Optionally, a second determination unit is used to determine the marking color of each of the L goods based on the L verification results, wherein the marking color corresponding to the first verification result is different from the marking color corresponding to the second verification result; a first drawing unit is used to draw the second picture onto a blank canvas to obtain a first canvas; a second drawing unit is used to draw a marking box for each of the goods in the first canvas based on the marking color of the goods to obtain a target canvas; and a first transmission unit is used to transmit the target canvas to a visualization terminal of a preset device.

[0112] In an optional embodiment, the device for updating the location of goods further includes: a second updating unit and a storage unit.

[0113] Optionally, the second updating unit is used to update the first verification result among the L verification results according to the updated location of the goods; and the storage unit is used to store the updated L verification results in a preset database.

[0114] From the above content, it can be seen that the present application collects the location identification (i.e., the first identification) of multiple storage goods and the identification (i.e., the second identification) of each stored goods through an AR device (i.e., the preset device), without the need for manual observation, thereby improving the efficiency of subsequent detection of the storage location of the goods. Afterwards, the present application determines whether the storage location of each goods meets expectations through the matching relationship between the location identification and the identification of each goods that need to be confirmed (i.e., L verification results). Then, the present application readjusts the storage location of the goods whose verification result is the first verification result (i.e., the goods whose storage location does not meet expectations), thereby omitting the step of screening the goods whose storage location does not meet expectations through manual observation, thereby improving the efficiency of subsequent adjustment of the storage location of the goods.

[0115] It can be seen that the present application achieves the technical effect of improving the efficiency of detecting and adjusting the position of goods by determining the matching relationship between the location identification collected by the AR device and the identification of each goods, thereby solving the technical problem of low efficiency in detecting and adjusting the position of goods manually in the prior art.

[0116] According to another aspect of an embodiment of the present application, a computer program product is further provided, the computer program product comprising a stored computer program, wherein when the computer program is running, the computer program product is controlled to execute any one of the above methods for updating the location of the cargo.

[0117] According to another aspect of an embodiment of the present application, there is also provided an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any of the above-mentioned methods for updating the cargo location by executing the executable instructions.

[0118] Optionally, Figure 5 is a schematic diagram of an optional electronic device according to an embodiment of the present application, such as Figure 5 As shown, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, any one of the above-mentioned methods for updating the location of the cargo is implemented.

[0119] Optionally, when the processor executes the program, it can implement the following steps: control the preset device to collect a first identifier and L second identifiers, wherein the preset device is an augmented reality device, the first identifier is an identifier of a target position, the target position is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a good in the target position; determine L verification results based on the first identifier and the L second identifiers, wherein the L verification results correspond one-to-one to the L goods, and the L verification results are comparison results between the preset positions and the target positions of the L goods; update the position of the goods corresponding to the first verification result based on the first verification result among the L verification results, wherein the first verification result is used to characterize that the preset position of the goods is different from the target position.

[0120] Optionally, the processor can implement the following steps when executing the program: control a preset device to capture a first picture through voice commands, wherein the first picture is a picture including a target location; input the first picture into a preset model to obtain first information, wherein the first information is position information of a first nameplate in the first picture, the first nameplate is used to record a first identification, and the preset model is a neural network model obtained by training N training pictures, N is a positive integer, and each training picture includes at least one cargo; cut the first picture based on the first information to obtain a first identification corresponding to the first nameplate.

[0121] Optionally, the processor can implement the following steps when executing the program: after obtaining the first identifier, verify the first identifier according to a first preset table, wherein the first preset table at least includes identifiers of locations where goods are allowed to be stored; if the first identifier does not exist in the first preset table, generate a first warning message, and transmit the first warning data to a visualization terminal of a preset device, wherein the first warning message is used to prompt the user to re-collect the first identifier; if the first identifier exists in the first preset table, control the preset device to collect a second picture through voice commands, wherein the second picture is a picture including L goods; determine L second identifiers based on the second picture.

[0122] Optionally, when the processor executes the program, it can implement the following steps: generate L request data based on the first identifier and L second identifiers, wherein each of the L request data includes at least the first identifier and one second identifier; verify the L request data according to the second preset table to obtain L verification results, wherein the second preset table includes at least the correspondence between the identifier of the location where goods are allowed to be stored and the identifier of the goods allowed to be stored at the location.

[0123] Optionally, when the processor executes the program, it can implement the following steps: scan the second preset table row by row to obtain M row data, where M is a positive integer; when each row data in the M row data does not simultaneously include the first identifier and the second identifier corresponding to the i-th request data, determine the i-th verification result as the first verification result, where i is a positive integer less than or equal to L; when any one row data in the M row data simultaneously includes the first identifier and the second identifier corresponding to the i-th request data, determine the i-th verification result as the second verification result, where the second verification result is used to characterize that the preset position of the goods is the same as the target position.

[0124] Optionally, the processor can implement the following steps when executing the program: determine the marking color of each of the L goods based on the L verification results, wherein the marking color corresponding to the first verification result is different from the marking color corresponding to the second verification result; draw the second picture on a blank canvas to obtain a first canvas; draw a marking box for each good in the first canvas based on the marking color of the good to obtain a target canvas; and transmit the target canvas to a visualization terminal of a preset device.

[0125] Optionally, when the processor executes the program, the following steps can be implemented: updating a first verification result among the L verification results according to the updated location of the goods; and storing the updated L verification results in a preset database.

[0126] From the above content, it can be seen that the present application collects the location identification (i.e., the first identification) of multiple storage goods and the identification (i.e., the second identification) of each stored goods through an AR device (i.e., the preset device), without the need for manual observation, thereby improving the efficiency of subsequent detection of the storage location of the goods. Afterwards, the present application determines whether the storage location of each goods meets expectations through the matching relationship between the location identification and the identification of each goods that need to be confirmed (i.e., L verification results). Then, the present application readjusts the storage location of the goods whose verification result is the first verification result (i.e., the goods whose storage location does not meet expectations), thereby omitting the step of screening the goods whose storage location does not meet expectations through manual observation, thereby improving the efficiency of subsequent adjustment of the storage location of the goods.

[0127] It can be seen that the present application achieves the technical effect of improving the efficiency of detecting and adjusting the position of goods by determining the matching relationship between the location identification collected by the AR device and the identification of each goods, thereby solving the technical problem of low efficiency in detecting and adjusting the position of goods manually in the prior art.

[0128] The above-mentioned embodiments or examples disclosed in the present application are not exhaustive, but are only illustrative of some embodiments or examples, and are not intended to be specific limitations on the scope of protection disclosed in the present application. In the absence of contradiction, each step in a certain embodiment or example in the present application can be implemented as an independent example, and the steps can be combined arbitrarily. For example, the scheme after removing some steps in a certain embodiment or example can also be implemented as an independent example, and the order of the steps in a certain embodiment or example can be arbitrarily exchanged. In addition, the optional methods or optional examples in a certain embodiment or example can be combined arbitrarily; in addition, the various embodiments or examples can be combined arbitrarily, for example, some or all steps of different embodiments or examples can be combined arbitrarily, and a certain embodiment or example can be combined arbitrarily with the optional methods or optional examples of other embodiments or examples.

[0129] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0133] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0134] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0135] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0136] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0137] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for updating the location of goods, characterized in that: include: Control a preset device to collect a first identifier and L second identifiers, wherein the preset device is an augmented reality device, the first identifier is an identifier of a target location, the target location is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a goods in the target location; Determine L verification results according to the first identifier and the L second identifiers, wherein the L verification results correspond to the L goods one by one, and the L verification results are comparison results between the preset positions of the L goods and the target positions; The position of the goods corresponding to the first verification result is updated according to a first verification result among the L verification results, wherein the first verification result is used to indicate that the preset position of the goods is different from the target position.

2. The method for updating the cargo location according to claim 1, characterized in that: Controlling a preset device to collect a first identifier and L second identifiers includes: Controlling the preset device to capture a first picture through a voice command, wherein the first picture is a picture including the target location; Input the first picture into a preset model to obtain first information, wherein the first information is position information of a first nameplate in the first picture, the first nameplate is used to record the first identification, and the preset model is a neural network model obtained by training N training pictures, N is a positive integer, and each training picture includes at least one commodity; The first picture is cut based on the first information to obtain a first identifier corresponding to the first nameplate.

3. The method for updating the cargo location according to claim 1, characterized in that: Controlling a preset device to collect a first identifier and L second identifiers includes: After obtaining the first identification, verifying the first identification according to a first preset table, wherein the first preset table at least includes identifications of locations where goods are allowed to be stored; If the first identifier does not exist in the first preset table, generate first warning information, and transmit the first warning data to the visualization terminal of the preset device, wherein the first warning information is used to prompt the user to re-collect the first identifier; When the first identifier exists in the first preset table, controlling the preset device to capture a second picture through a voice command, wherein the second picture is a picture including the L goods; The L second identifiers are determined according to the second image.

4. The method for updating the cargo location according to claim 1, characterized in that: Determining L verification results according to the first identifier and the L second identifiers includes: Generate L request data according to the first identifier and the L second identifiers, wherein each request data in the L request data includes at least the first identifier and one second identifier; The L request data are verified according to a second preset table to obtain the L verification results, wherein the second preset table at least includes a correspondence between an identifier of a location where goods are allowed to be stored and an identifier of goods allowed to be stored at the location.

5. The method for updating the cargo location according to claim 4, characterized in that: In the process of verifying the L request data according to the second preset table to obtain the L verification results, the method for updating the cargo location further includes: Scan the second preset table row by row to obtain M row data, where M is a positive integer; In a case where each row of the M rows of data does not simultaneously include the first identifier and the second identifier corresponding to the i-th request data, determining the i-th verification result as the first verification result, where i is a positive integer less than or equal to L; When any one of the M rows of data includes both the first identifier and the second identifier corresponding to the i-th request data, the i-th verification result is determined to be the second verification result, wherein the second verification result is used to characterize that the preset position of the goods is the same as the target position.

6. The method for updating the cargo location according to claim 1, characterized in that: After determining L verification results according to the first identifier and the L second identifiers, the method for updating the cargo location further includes: Determining a marking color for each of the L goods according to the L verification results, wherein the marking color corresponding to the first verification result is different from the marking color corresponding to the second verification result; Draw the second picture onto the blank canvas to obtain the first canvas; Draw a labeling frame of each commodity in the first canvas according to the labeling color of each commodity to obtain a target canvas; The target canvas is transmitted to a visualization terminal of the preset device.

7. The method for updating the cargo location according to claim 1, characterized in that: After updating the location of the goods corresponding to the first verification result among the L verification results according to the first verification result, the method for updating the location of the goods further includes: Updating a first verification result among the L verification results according to the updated location of the goods; The updated L verification results are stored in a preset database.

8. A device for updating the location of goods, characterized in that: include: a collection unit, configured to control a preset device to collect a first identifier and L second identifiers, wherein the preset device is an augmented reality device, the first identifier is an identifier of a target location, the target location is used to store L goods, L is a positive integer, and each of the L second identifiers is an identifier of a good in the target location; a first determining unit, configured to determine L verification results according to the first identifier and the L second identifiers, wherein the L verification results correspond to the L goods one by one, and the L verification results are comparison results between the preset positions of the L goods and the target positions; The first updating unit is used to update the position of the goods corresponding to the first verification result according to the first verification result among the L verification results, wherein the first verification result is used to indicate that the preset position of the goods is different from the target position.

9. A computer program product, characterized in that The computer program product comprises a computer program, wherein when the computer program is run, the computer program product is controlled to execute the method for updating the cargo position according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store 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 updating the cargo location as described in any one of claims 1 to 7.

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