Robot distribution cabin identification method, computer equipment and storage medium

Abstract

The invention discloses a robot distribution cabin recognition method, computer equipment and a storage medium, and relates to the field of robotics.The robot distribution cabin recognition method comprises the steps that in response to opening of a cabin door, an image collection device is used for obtaining an in-cabin image of a robot distribution cabin; identifying the in-cabin image to obtain the object category and the object number of the target object; judging whether the target article is out of stock based on the article category and the article quantity; and when the target article is out of stock, in response to closing of the cabin door, reporting identification data, the identification data including the article category and the article quantity. In this way, the identification efficiency of the robot distribution cabin can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of robots, and particularly to a method for identifying a robot delivery compartment, a computer device, and a storage medium. Background Art

[0002] Based on the requirements of energy conservation and emission reduction, hotels are prohibited from providing disposable items such as toothbrushes, toothpaste, soaps, shampoos, body washes, etc. in rooms. Hotels need to respond to this requirement, which has generated more work for delivering guest supplies.

[0003] With the rapid development of robot technology, robot delivery services have gradually become popular. And hotel storage robots are automated devices used for hotel storage management, mainly for item storage, handling, and retrieval. It can improve efficiency, reduce costs, and reduce human errors.

[0004] To ensure the safety and integrity of the delivered items, it is necessary to monitor the internal state of the robot delivery compartment in real time. Traditional methods mostly use sensors for monitoring, but there are problems such as limited monitoring information and inability to intuitively reflect the actual situation inside the compartment. Summary of the Invention

[0005] The main technical problem to be solved by the present invention is to provide a method for identifying a robot delivery compartment, a computer device, and a storage medium, which can improve the efficiency of identifying the robot delivery compartment.

[0006] To solve the above technical problem, a technical solution adopted by the present invention is: to provide a method for identifying a robot delivery compartment, the method for identifying a robot delivery compartment includes: in response to the opening of the compartment door, using an image acquisition device to obtain an internal image of the robot delivery compartment; identifying the internal image to obtain the item category of the target item and the quantity of the target item; judging whether the target item is out of stock based on the item category and the quantity; when the target item is out of stock, in response to the closing of the compartment door, reporting the identification data, and the identification data includes the item category and the quantity.

[0007] In a possible implementation, identifying the internal image to obtain the item category of the target item and the quantity of the target item includes: identifying the item compartments in the internal image; when the item compartments exist, identifying the item category of the target item and the quantity of the target item in the item compartments; when the item compartments do not exist, in response to the closing of the compartment door, reporting the identification data, and the identification data includes the status of the item compartments.

[0008] In a possible implementation, when the item compartment exists, identifying the item category of the target item and the quantity of the target item in the item compartment includes: performing target recognition on the target item in the item compartment to obtain the item category of the target item; identifying the capacity ratio of the target object in the item compartment; and obtaining the quantity of the target item based on the item category and the capacity ratio.

[0009] In a possible implementation, obtaining the quantity of the target item based on the item category and the capacity ratio includes: obtaining the maximum capacity of the target item in the item compartment based on the item category; and determining the quantity of the target item based on the maximum capacity and the capacity ratio.

[0010] In a possible implementation, determining whether the target item is out of stock based on the item category and the quantity of the item includes: obtaining the stock threshold of the target item based on the item category; and determining that the target item is out of stock when the quantity of the item is less than the stock threshold.

[0011] In a possible implementation, the method for identifying the robot delivery compartment further includes: obtaining a preset time interval; and in response to reaching the preset time interval, using an image acquisition device to obtain an image inside the robot delivery compartment.

[0012] In a possible implementation, in response to the hatch door being closed, reporting the recognition data includes: modifying the data format of the recognition data to the json format and uploading it to the terminal application layer.

[0013] In a possible implementation, the method for identifying the robot delivery compartment further includes: if it is impossible to obtain an image inside the robot delivery compartment using the image acquisition device in response to the hatch door being opened or in response to reaching the preset time interval; reporting the abnormal state data of the image acquisition device.

[0014] To solve the above technical problems, another technical solution adopted by the present invention is: providing a robot delivery compartment identification device, which includes a processor for executing to implement the above-mentioned robot delivery compartment identification method.

[0015] To solve the above technical problems, another technical solution adopted by the present invention is: providing a computer-readable storage medium for storing instructions / program data, and the instructions / program data can be executed to implement the above-mentioned robot delivery compartment identification method.

[0016] The beneficial effects of the present invention are: different from the prior art, the present invention obtains the in-cabin inventory data through visual recognition, sets the order-taking status of the robot through application logic, solves the manpower problem in the delivery of common guest supplies in hotels, and saves the time of hotel service staff and improves work efficiency through centralized replenishment and automatic robot delivery. Brief Description of the Drawings

[0017] Figure 1 is a schematic flowchart of a method for identifying a robot delivery compartment in an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of another method for identifying a robot delivery compartment in an embodiment of the present application;

[0019] Figure 3 is a schematic diagram of a robot delivery compartment in an embodiment of the present application;

[0020] Figure 4 is a schematic diagram of an in - cabin image of a robot delivery compartment in an embodiment of the present application;

[0021] Figure 5 is a schematic flowchart of yet another method for identifying a robot delivery compartment in an embodiment of the present application;

[0022] Figure 6 is a schematic structural diagram of a device for identifying a robot delivery compartment in an embodiment of the present application;

[0023] Figure 7 is a schematic structural diagram of a computer - readable storage medium in an embodiment of the present application. Detailed Embodiment

[0024] To make the objectives, technical solutions and effects of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples.

[0025] The present application provides a method for identifying a robot delivery compartment. Through visual recognition, in - cabin inventory data is obtained, and through application logic, the order - receiving status of the robot is set, solving the manpower problem in the delivery of common guest supplies in hotels. Through centralized replenishment and automatic robot delivery, the time of hotel service staff is saved and the human efficiency is improved.

[0026] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for identifying a robot delivery compartment in an embodiment of the present application. It should be noted that if there are substantially the same results, this embodiment is not limited to the Figure 1 shown process sequence. As Figure 1 shown, this embodiment includes:

[0027] Step S110: In response to the opening of the cabin door, use an image acquisition device to obtain an in - cabin image of the robot delivery compartment.

[0028] Among them, the image acquisition device is arranged on the top or side wall inside the robot delivery compartment and can cover the entire in - cabin area.

[0029] In one implementation, the image acquisition device is a cloud eye camera.

[0030] Specifically, when the door of the robot delivery compartment is opened, an image acquisition instruction is triggered. According to the image acquisition instruction, the image acquisition device arranged in the robot delivery compartment is controlled to perform image acquisition on the interior of the compartment to obtain an interior image of the compartment.

[0031] In another implementation, a fill light is also arranged in the robot delivery compartment. The fill light is used to illuminate the acquisition position of the image acquisition device.

[0032] Specifically, when the door of the robot delivery compartment is opened, an illumination instruction and an image acquisition instruction are triggered. According to the illumination instruction, the fill light arranged in the robot delivery compartment is controlled to perform fill light. According to the image acquisition instruction, the image acquisition device arranged in the robot delivery compartment is controlled to perform image acquisition on the interior of the compartment to obtain an interior image of the compartment.

[0033] Step S120: Identify the interior image to obtain the item category of the target item and the quantity of the target item.

[0034] The target items include common customer-needed items such as toothbrushes, toothpaste, soap, shampoo, body wash, etc.

[0035] Specifically, perform category identification of the target items on the interior image and count the quantity of items corresponding to each identified category.

[0036] In one implementation, multiple item compartments are placed in the delivery compartment of the robot, and different categories of target items are placed in each item compartment. Perform item compartment detection on the interior image and further count the quantity of each category of item.

[0037] Exemplarily, five item compartments are placed in the delivery compartment of the robot, corresponding to the toothbrush, toothpaste, soap, shampoo, and body wash compartments respectively. First, identify the item category in the compartment. For example, if the item identified in the compartment is a toothbrush, further identify the quantity of the toothbrush as 7.

[0038] In another implementation, directly perform item identification on the interior image, identify the item category of each item, and then classify and count the item categories to obtain the quantity of items corresponding to each category.

[0039] Step S130: Determine whether the target item is out of stock based on the item category and the quantity of the item.

[0040] Generally, each item category has a quantity requirement. Compare the quantity requirement corresponding to each item category with the quantity of the item. When the quantity of the item is less than the quantity requirement, it is determined that the target item is out of stock.

[0041] Exemplarily, the required quantity of toothbrushes is 7, and the recognized quantity of toothbrushes is 4. Since the quantity of the item is greater than the required quantity, it is determined that there is a shortage of the target item.

[0042] Step S140: When there is a shortage of the target item, in response to the closing of the hatch, report the recognition data.

[0043] Among them, the recognition data includes the item category and the quantity of the item.

[0044] Specifically, save each item category and the corresponding quantity of the item respectively, and detect the change in the quantity of the item category in real time. When the hatch is closed, report the item category and the quantity of the item with a shortage that are recognized, and do not report the item category and the quantity of the item for other items.

[0045] This application obtains the in-cabin inventory data through visual recognition, sets the order-taking status of the robot through application logic, solves the manpower problem in the distribution of commonly used guest supplies in hotels, and saves the time of hotel service staff and improves work efficiency through centralized replenishment and automatic robot distribution.

[0046] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of another method for identifying a robot delivery cabin in an embodiment of this application. It should be noted that if there are substantially the same results, this embodiment is not limited to Figure 2 the shown process sequence. As Figure 2 shown, this embodiment includes:

[0047] Step S210: In response to the opening of the hatch, use an image acquisition device to obtain an in-cabin image of the robot delivery cabin.

[0048] In one implementation, when the hatch of the robot delivery cabin is opened, trigger a lighting instruction and an image acquisition instruction. According to the lighting instruction, control the fill light arranged in the robot delivery cabin to illuminate the items in the cabin, and according to the image acquisition instruction, control the cloud eye camera arranged in the robot delivery cabin to perform image acquisition on the cabin to obtain an in-cabin image.

[0049] Further, if an in-cabin image of the robot delivery cabin cannot be obtained using the image acquisition device in response to the opening of the hatch, report the abnormal state data of the image acquisition device.

[0050] In another implementation, an in-cabin image is acquired once within a set time interval.

[0051] Specifically, obtain a preset time interval. In response to reaching the preset time interval, trigger an illumination instruction and an image acquisition instruction. According to the illumination instruction, control the fill light disposed in the robot delivery compartment to illuminate the items in the compartment, and according to the image acquisition instruction, control the cloud eye camera disposed in the robot delivery compartment to perform image acquisition on the compartment to obtain an in-compartment image.

[0052] Furthermore, if an in-compartment image of the robot delivery compartment cannot be obtained by using the image acquisition device in response to reaching the preset time interval, report the abnormal state data of the image acquisition device.

[0053] Exemplarily, the preset time interval is 15 minutes.

[0054] Specifically, please refer to Figure 3 and Figure 4 , Figure 3 is a schematic diagram of the robot delivery compartment in an embodiment of the present application, Figure 4 is a schematic diagram of the in-compartment image of the robot delivery compartment in an embodiment of the present application.

[0055] Step S220: Identify the item category of the target item and the quantity of the target item from the in-compartment image.

[0056] Specifically, step S220 includes:

[0057] Step S221: Identify the item compartments in the in-compartment image.

[0058] A plurality of item compartments are placed in the delivery compartment of the robot, and different categories of target items are placed in each item compartment.

[0059] In one implementation, directly identify the number of compartments present in the in-compartment image and the position of each compartment.

[0060] In another implementation, each item compartment is provided with a compartment mark, and the compartment mark is used to indicate the category of the item stored in the compartment. Identify the number of compartments present in the compartment image, the position of each compartment, and the compartment category.

[0061] Step S222: When the item compartment exists, identify the item category of the target item in the item compartment and the quantity of the target item.

[0062] A plurality of item compartments are placed in the delivery compartment of the robot, and different categories of target items are placed in each item compartment. Perform item compartment detection on the in-compartment image and further count the quantity of each category of item.

[0063] In one implementation, target recognition is performed on the target item in the item compartment to obtain the item category of the target item, the capacity ratio of the target object in the item compartment is identified, and the number of target items is obtained based on the item category and the capacity ratio.

[0064] In another implementation, target recognition is performed on the target item in the item compartment to obtain the item category of the target item, and the maximum capacity of the target item in the item compartment is obtained based on the item category; the number of target items is determined based on the maximum capacity and the capacity ratio.

[0065] In another implementation, the category of the item compartment is obtained, the item category of the target item is determined, the capacity ratio of the target object in the item compartment is identified, and the number of target items is obtained based on the item category and the capacity ratio.

[0066] In another implementation, the category of the item compartment is obtained, the item category of the target item is determined, and the maximum capacity of the target item in the item compartment is obtained based on the item category; the number of target items is determined based on the maximum capacity and the capacity ratio.

[0067] Step S223: When the item compartment does not exist, in response to the closing of the hatch, the recognition data is reported.

[0068] Among them, the recognition data includes the status of the item compartment.

[0069] When it is recognized that the item compartment corresponding to the corresponding item category does not exist, it means that there is a shortage of goods in the compartment. When the hatch is closed, the data of the compartment category and the compartment status where the shortage is recognized is reported, and the data of the compartment category and the compartment status of other compartments is not reported.

[0070] In one implementation, the data format of the obtained status data of the item compartment is modified to the json format and uploaded to the terminal application layer.

[0071] Step S230: Determine whether the target item is out of stock based on the item category and the number of items.

[0072] Specifically, step S230 includes:

[0073] Step S231: Obtain the stock threshold of the target item based on the item category.

[0074] Step S232: When the number of items is less than the stock threshold, it is determined that the target item is out of stock.

[0075] Step S240: When the target item is out of stock, in response to the closing of the hatch, the recognition data is reported.

[0076] Among them, the recognition data includes the item category and the number of items.

[0077] Specifically, each item category and the corresponding item quantity are saved separately, and the change in the quantity of the item category is detected in real time. When the hatch is closed, the item categories and item quantities with out-of-stock situations identified are reported, while the item categories and item quantities of other items are not reported.

[0078] In one implementation, the data formats of the obtained item category data and item quantity data are modified to the json format and uploaded to the terminal application layer.

[0079] In this implementation, grids made of cardboard are placed inside the cabin, and customer-needed items are placed in the grids. Through the overhead camera, the proportion of items in each grid is observed to calculate the item inventory. To save device power and computing power, the vision algorithm is only activated for visual recognition in two cases. When the hatch is open, visual recognition is enabled. When the hatch is closed, the visual results are reported, which can solve the recognition of replenishment and item retrieval. Every preset time interval, the cabin lights and camera are activated for detection once, which can solve the problem of inaccurate recognition caused by human interference. After recognition, the data is reported in the json format and uploaded to the application layer of the software. After application logic processing, it is determined whether the current robot can automatically undertake the customer-needed delivery task and whether replenishment is required.

[0080] In a specific implementation, please refer to Figure 5 , Figure 5 is a schematic flowchart of another method for identifying a robot delivery cabin in the embodiments of the present application. It should be noted that if there are substantially the same results, this embodiment is not limited to Figure 5 the flowchart sequence shown. As Figure 5 shown, this implementation includes:

[0081] When the robot delivery cabin hatch is opened in response, the cloud eye is turned on. When the robot delivery cabin hatch is closed in response, the cloud eye is turned off, and the first judgment process is performed.

[0082] The first judgment process determines whether cloud eye data, that is, the image data inside the delivery cabin, is received. When the cloud eye data is not received, it indicates that the cloud eye is offline, and the cloud eye offline information is reported, and the second judgment process is performed. When the cloud eye data is received, the third judgment process is performed.

[0083] The second judgment process identifies whether the current cabin grid is a customer-needed cabin (application). When it is identified that the current cabin grid is not a customer-needed cabin (application), the process ends. When it is identified that the current cabin grid is a customer-needed cabin (application), the customer-needed cabin is closed, and the cloud eye data is obtained again, and the fourth judgment process is performed.

[0084] The fourth judgment process determines whether cloud eye data is received. When cloud eye data is received, the fifth judgment process is performed. When cloud eye data is not received, the sixth judgment process is performed.

[0085] The sixth judgment process determines whether the current device needs to be periodically detected. If the current device needs to be periodically detected, the periodic detection mechanism is enabled to detect the cloud eye device, and the fourth judgment process continues. If the current device does not need to be periodically detected, the process ends.

[0086] The third judgment process determines whether the cloud eye recognizes the customer demand box. When the customer demand box is not recognized, the seventh judgment process is performed; when the customer demand box is recognized, the fifth judgment process is performed.

[0087] The fifth judgment process determines whether the current compartment is the customer demand compartment (application). When it is recognized that the current compartment is not the customer demand compartment (application), the customer demand compartment is opened, and a message is reported that the customer demand compartment has been opened, and the seventh judgment process is performed. When it is recognized that the current compartment is the customer demand compartment (application), the eighth judgment process is performed.

[0088] The eighth judgment process determines whether the cloud eye recognizes that the inventory is 0. When it is recognized that the inventory is 0, the in-position status of the customer demand compartment remains unchanged, and a shortage is reported, and the process ends; when it is recognized that the inventory is not 0, the process ends.

[0089] The seventh judgment process determines whether the cloud eye recognizes that there is an item. When it is recognized that there is an item, the ninth judgment process is performed; when it is not recognized that there is an item, the tenth judgment process is performed.

[0090] The ninth judgment process determines whether the current compartment is the customer demand compartment (application). When it is recognized that the current compartment is not the customer demand compartment (application), no processing is required, and it is still considered not in position, and the process ends. When it is recognized that the current compartment is the customer demand compartment (application), no processing is required, and it is still considered not in position, and the process ends.

[0091] The tenth judgment process determines whether the current compartment is the customer demand compartment (application). When it is recognized that the current compartment is not the customer demand compartment (application), no processing is required, and it is still considered not in position, and the process ends. When it is recognized that the current compartment is the customer demand compartment (application), the customer demand compartment is closed, and a message is reported that the customer demand compartment has been removed, and the process ends.

[0092] In this embodiment, a robot delivery compartment recognition device is also provided. This device is used to implement the above-mentioned embodiment and the preferred implementation manner, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0093] Please refer to Figure 6 , Figure 6It is a schematic structural diagram of a robot delivery cabin recognition device in an embodiment of the present application. In this embodiment, the robot delivery cabin recognition device includes a collection module 601, an identification module 602, a judgment module 603, and a reporting module 604.

[0094] Among them, the collection module 601 is used to obtain the in-cabin image of the robot delivery cabin by using an image acquisition device in response to the opening of the cabin door.

[0095] The identification module 602 is used to identify the in-cabin image to obtain the item category and item quantity of the target item.

[0096] The judgment module 603 is used to judge whether the target item is out of stock based on the item category and item quantity.

[0097] The reporting module 604 is used to report the identification data in response to the closing of the cabin door when the target item is out of stock. The identification data includes the item category and item quantity. This robot delivery cabin recognition device can obtain the in-cabin inventory data through visual recognition, set the order-taking status of the robot through application logic, solve the manpower problem in the delivery of common guest supplies in hotels, save the time of hotel service staff, and improve work efficiency through centralized replenishment and automatic robot delivery.

[0098] The embodiment of the present invention also provides a computer device having the above-mentioned Figure 6 robot delivery cabin recognition device shown.

[0099] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 7 , the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common main board or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 7 One processor 10 is taken as an example in

[0100] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0101] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0102] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0103] The memory 20 can include a volatile memory, for example, a random access memory; the memory can also include a non-volatile memory, for example, a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0104] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 can be connected through a bus or other means. Figure 5 Taking connection through a bus as an example.

[0105] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (for example, an LED), and a tactile feedback device (for example, a vibration motor), etc. The above display device includes but is not limited to a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some alternative embodiments, the display device can be a touch screen.

[0106] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0107] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the methods described herein can be stored as such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0108] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms.

[0109] In addition, in each embodiment of the present application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0110] The above is only the implementation manner of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A robot delivery compartment identification method, characterized in that: The method comprises: In response to the hatch door opening, using an image acquisition device to acquire an image inside the robot delivery cabin; Recognize the image in the cabin to obtain the category of the target object and the quantity of the target object; Determining whether the target item is out of stock based on the item category and the item quantity; When the target item is out of stock, in response to the hatch being closed, identification data is reported, where the identification data includes the item category and the item quantity.

2. The robot delivery compartment identification method according to claim 1, characterized in that: The step of identifying the image in the cabin and obtaining the category of the target object and the quantity of the target object includes: Identifying an item compartment in the cabin image; When the item compartment exists, identifying the item category and the item quantity of the target item in the item compartment; When the item compartment does not exist, in response to the door closing, identification data is reported, the identification data including the status of the item compartment.

3. The robot delivery compartment identification method according to claim 2, characterized in that: When the item compartment exists, identifying the item category and the item quantity of the target item in the item compartment includes: Performing target identification on the target item in the item compartment to obtain the item category of the target item; Identify the volume ratio of the target object in the item compartment; The quantity of the target item is obtained based on the item category and the capacity ratio.

4. The robot delivery compartment identification method according to claim 3, characterized in that: The acquiring the quantity of the target item based on the item category and the capacity proportion includes: Obtaining a maximum capacity of the target item in the item compartment based on the item category; The number of items of the target item is determined based on the maximum capacity and the capacity ratio.

5. The robot delivery compartment identification method according to claim 1, characterized in that: The determining whether the target item is out of stock based on the item category and the item quantity includes: Acquire a stock threshold of the target item based on the item category; When the item quantity is less than the inventory threshold, it is determined that the target item is out of stock.

6. The robot delivery compartment identification method according to claim 1, characterized in that: The method further comprises: Get the preset time interval; In response to reaching the preset time interval, the image acquisition device is used to acquire the interior image of the robot delivery cabin.

7. The robot delivery compartment identification method according to claim 1 or 2, characterized in that: In response to the door closing, reporting the identification data includes: The data format of the identification data is modified to json format and uploaded to the terminal application layer.

8. The robot delivery compartment identification method according to claim 6, characterized in that: The method further comprises: If, in response to the hatch opening or in response to reaching the preset time interval, the image acquisition device cannot be used to acquire the image inside the robot delivery cabin; The abnormal status data of the image acquisition device is reported.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the robot delivery compartment identification method according to any one of claims 1 to 8 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the robot delivery compartment identification method described in any one of claims 1 to 8.

Images