A silo warehouse position automatic calibration method and system, electronic equipment and storage medium

Abstract

The application provides a kind of silo position automatic calibration method, system, electronic equipment and storage medium, method includes: according to each camera monitor screen selection and the corresponding camera of to-be-calibrated warehouse position, obtains the information of stacking state according to the spatial scanning data of warehouse;According to the information of selected camera, construct three-dimensional rendering camera object, render the whole scene of three-dimensional rendering camera object into texture;Texture is matched with the information of stacking state, obtains the spatial coordinate graph containing each point information;According to the image data of selected camera, identify all stacking range, find the contour pixel point corresponding to each stack in the pixel point of stacking range;According to the contour pixel point corresponding to each stack, sample the spatial coordinate graph, obtain the three-dimensional space range of each stack, project the three-dimensional space range as two-dimensional warehouse position range.The silo calibration operation of the application is simple, high degree of automation and small error.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehouse management, and more specifically, to a method, system, electronic device, and storage medium for automatic location calibration of material storage areas. Background Technology

[0002] With the development of smart parks and intelligent warehouses, the demand for automation within these spaces is increasing, including in areas such as cargo transportation, material storage, and merchandise monitoring. This requires automated system scheduling, allocation of storage locations, monitoring of compliant material storage, and prevention of material loss. To better manage materials and meet daily production needs, materials need to be stored in designated areas, known as "storage locations." These locations are labeled with material information and used by automated management systems to allocate storage space rationally, facilitating material retrieval in daily production and enabling compliance monitoring and loss investigation. However, this approach suffers from issues such as heavy measurement workload and data errors.

[0003] Because automated management systems primarily rely on image recognition or object scanning technology to assess space usage, determining space occupancy based on object information before proceeding with allocation, scheduling, and other logical calculations, the spatial coordinate system and the warehouse location measurement may not be on the same system. This can lead to errors after data entry. Furthermore, changes in production plans can necessitate adjustments to warehouse locations, requiring re-measurement and data entry. Existing manual measurement and data entry methods are labor-intensive and prone to significant data errors. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing an automatic calibration method, system, electronic device, and storage medium for warehouse locations, which is simple to operate, highly automated, and has low error.

[0005] According to a first aspect of the present invention, an automatic location calibration method for a material warehouse is provided, comprising:

[0006] Select the camera corresponding to the storage location to be calibrated based on the monitoring screen of each camera, and obtain the stacking status information based on the spatial scanning data of the warehouse.

[0007] A 3D rendering camera object is constructed based on the information of the selected camera, and the entire scene of the 3D rendering camera object is rendered into the texture; the texture is matched with the material stacking state information to obtain a spatial coordinate map containing information of each point.

[0008] The entire material pile area is identified based on the image data from the selected camera. The pixels within the material pile area are clustered to find the contour pixels corresponding to each material pile. The spatial coordinate map is sampled based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile. The three-dimensional spatial range is then projected into a two-dimensional storage location range.

[0009] Based on the above technical solution, the present invention can also be improved as follows.

[0010] Optionally, the step of selecting the camera corresponding to the location to be calibrated based on the monitoring images of each camera includes:

[0011] Input the information of each surveillance camera in the warehouse, display the monitoring images of each camera, and select the camera with a clear view and direction that includes multiple material piles.

[0012] Optionally, the camera information includes at least the camera's position, orientation, video output width and height, and field of view.

[0013] Optionally, the stockpile status information is point cloud data or triangular mesh data.

[0014] Optionally, the step of identifying the entire material pile area based on image data from the selected camera, and clustering the pixels within the material pile area to obtain the contour pixels corresponding to each material pile, includes:

[0015] Acquire image data output from the selected camera, perform color value filtering on the image data to filter out pixels with color values ​​that do not meet the requirements, and obtain the entire material stacking range with clear boundaries;

[0016] Similarity point clustering is performed on the contour pixels of the entire material pile range, and contour pixels within the same pixel threshold range are regarded as contour pixels of the same material pile.

[0017] Optionally, the step of sampling the spatial coordinate map based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile, and projecting the three-dimensional spatial range into a two-dimensional storage location range, includes:

[0018] Multiple contour pixels of the same material pile are mapped to the spatial coordinate map for sampling to obtain the three-dimensional coordinates of multiple contour pixels of the same material pile. The three-dimensional coordinates are merged to obtain a spatial bounding box, which is the three-dimensional spatial range of each material pile.

[0019] The spatial bounding box is projected onto a two-dimensional plane to obtain the range value of the storage location. A two-dimensional rectangular representation is constructed based on the range value of the storage location until the two-dimensional rectangles corresponding to all material piles are obtained.

[0020] Optional, also includes:

[0021] The obtained two-dimensional storage location range is manually corrected.

[0022] According to a second aspect of the present invention, an automatic warehouse location calibration system is provided, comprising:

[0023] The acquisition module is used to acquire information from each camera and the monitoring images from each camera, select the camera corresponding to the storage location to be calibrated based on the monitoring images from each camera, and also to acquire the spatial scanning data of the warehouse and obtain the stacking status information based on the spatial scanning data of the warehouse.

[0024] The spatial construction module is used to construct a 3D rendering camera object based on the information of the selected camera, render the entire scene of the 3D rendering camera object into a texture, and match the texture with the material stacking state information to obtain a spatial coordinate map containing information of each point.

[0025] The identification module is used to identify the entire material pile range based on the image data of the selected camera, and to cluster the pixels within the material pile range to find the contour pixels corresponding to each material pile; it is also used to sample the spatial coordinate map based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile, and to project the three-dimensional spatial range into a two-dimensional storage location range.

[0026] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is configured to implement a method for automatically calibrating warehouse locations when executing a computer management program stored in the memory.

[0027] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer management class program stored thereon, which, when executed by a processor, implements the steps of an automatic warehouse location calibration method.

[0028] This invention provides a method, system, electronic device, and storage medium for automatic warehouse location calibration. It eliminates the need for manual measurement of warehouse locations, automatically determining the location range, size, and quantity based on warehouse material stacking and monitoring data. This avoids multiple measurement and data entry operations due to changes in production requirements, improving calibration efficiency and reducing labor intensity. The calculation of the warehouse location range is based on spatial scanning data, resulting in minimal numerical deviation and high accuracy. Attached Figure Description

[0029] Figure 1 A flowchart of an automatic warehouse location calibration method provided by the present invention;

[0030] Figure 2 This is a schematic diagram illustrating the generation of a spatial coordinate map according to the present invention.

[0031] Figure 3This is a schematic diagram of the color value filtering method of the present invention;

[0032] Figure 4 This is a schematic diagram illustrating the manual correction of the storage location range according to the present invention;

[0033] Figure 5 A structural block diagram of an automatic warehouse location calibration system provided by the present invention;

[0034] Figure 6 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0035] Figure 7 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0036] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0037] Figure 1 A flowchart of an automatic warehouse location calibration method provided by the present invention is shown below. Figure 1 As shown, the method includes:

[0038] 101. Select the camera corresponding to the location to be calibrated based on the monitoring screen of each camera, and obtain the stacking status information based on the spatial scanning data of the warehouse;

[0039] 102. For example Figure 2 As shown, a 3D rendering camera object is constructed based on the information of the selected camera, and the entire scene of the 3D rendering camera object is rendered into a texture; the texture is matched with the material stacking state information to obtain a spatial coordinate map containing information of each point;

[0040] 103. Identify the entire material pile range based on the image data of the selected camera, cluster the pixels within the material pile range to find the contour pixels corresponding to each material pile, sample the spatial coordinate map based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile, and project the three-dimensional spatial range into a two-dimensional storage location range.

[0041] Understandably, given the deficiencies in the background technology, this embodiment of the invention proposes an automatic warehouse location calibration method. Several surveillance cameras are installed in the warehouse; these cameras are existing equipment within the warehouse. Each camera collects image information within its monitored area, achieving full video surveillance coverage of all material storage areas in the warehouse. The method in this embodiment utilizes the surveillance images output by these cameras, combined with the warehouse's spatial scanning data, to identify and determine each storage location, ultimately calibrating it. When material changes cause changes in the storage status of each location, the location needs to be recalibrated. This can be achieved by recalculating based on the new surveillance images output by the initially selected cameras and the new spatial scanning data of the warehouse, thus identifying the new storage location range.

[0042] This invention provides an automatic warehouse location calibration method that eliminates the need for manual measurement. Instead, it automatically determines the location range, size, and quantity based on warehouse material stacking and monitoring data. This avoids multiple measurement and data entry operations due to changes in production requirements, improving calibration efficiency and reducing labor intensity. The calculation of the location range is based on warehouse spatial scanning data, resulting in minimal numerical deviation and high accuracy.

[0043] In one possible embodiment, selecting the camera corresponding to the location to be calibrated based on the monitoring images of each camera includes:

[0044] Input the information of each monitoring camera in the warehouse, and the system will display the monitoring screen of each camera. Select the camera with a clear view and direction that contains multiple material piles corresponding to the warehouse locations to be calibrated.

[0045] It is understood that this embodiment demonstrates the steps for selecting the camera corresponding to the location to be calibrated. This step can be entered once during the initial calibration, and subsequent calibrations can directly use the previously entered information or adjust some parameters of each camera as needed.

[0046] In one possible embodiment, the camera information includes at least the camera's position, orientation, width and height of the video output, and field of view (i.e., fov angle).

[0047] Understandably, the camera information mentioned above allows for the quick selection of a suitable camera and facilitates the construction of a 3D rendering camera object during subsequent calculations, enabling the rendering of the entire scene of the 3D rendering camera object onto the texture.

[0048] In one possible embodiment, the stockpile status information is point cloud data or triangular mesh data.

[0049] It is understandable that the stacking status information is obtained through spatial scanning data of the warehouse. The spatial scanning data can come from model data scanning or geographic information data collection, and it contains relatively accurate spatial coordinate information.

[0050] In one possible embodiment, such as Figure 3 As shown, the step of identifying the entire material pile area based on image data from the selected camera, and clustering the pixels within the material pile area to obtain the contour pixels corresponding to each material pile, includes:

[0051] Acquire image data output from the selected camera, perform color value filtering on the image data to filter out pixels with color values ​​that do not meet the requirements, and obtain the entire material stacking range with clear boundaries;

[0052] Similarity point clustering is performed on the contour pixels of the entire material pile range, and contour pixels within the same pixel threshold range are regarded as contour pixels of the same material pile.

[0053] Understandably, by filtering the color values ​​of the image data output by the camera, the area range of each material pile in the monitored screen can be identified. Specifically, for the value of each pixel in the RGB three channels of the image data, a set value is subtracted from each, and then the square is calculated. This makes the original pixel value's channel value larger, thus making the boundaries of the values ​​of each color channel clearer. For example... Figure 3 As shown, irrelevant pixels, such as pedestrians, other equipment, and buildings, are filtered out to facilitate the identification of the material stack outline, resulting in an image that includes the entire material stack area with clear boundaries.

[0054] Since this embodiment obtains the stacking range of all material piles, it is necessary to distinguish each material pile in order to calibrate each storage location. Therefore, similarity point clustering is performed on the contour pixels of the entire stacking range, and contour pixels within the same pixel threshold range are regarded as contour pixels of the same material pile. Specifically, clustering is performed on the image obtained in the previous step that includes the entire stacking range with clear boundaries. Points with multiple similar range pixel values ​​are considered to be the same part within a certain range (i.e., the preset same pixel threshold range), and are thus regarded as the same material pile. Through the above steps, the stacking range of each material pile is distinguished.

[0055] In one possible embodiment, sampling the spatial coordinate map based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile, and projecting the three-dimensional spatial range into a two-dimensional storage location range, includes:

[0056] Multiple contour pixels of the same material pile are mapped to the spatial coordinate map for sampling to obtain the three-dimensional coordinates of multiple contour pixels of the same material pile. The three-dimensional coordinates are merged to obtain a spatial bounding box, which is the three-dimensional spatial range of each material pile.

[0057] The spatial bounding box is projected onto a two-dimensional plane to obtain the range value of the storage location. A two-dimensional rectangular representation is constructed based on the range value of the storage location until the two-dimensional rectangles corresponding to all material piles are obtained.

[0058] Understandably, after obtaining the outline pixels of each material pile in the image data, it is necessary to map each material pile area to a spatial coordinate system to manage the storage location of each pile. Mapping the outline pixels of each pile to a spatial coordinate graph yields the 3D coordinate values ​​of all outline pixels. Connecting and merging all outline pixels of the same pile provides the 3D spatial range corresponding to each pile. To facilitate a more intuitive display of the storage location of each pile, the 3D spatial range corresponding to each pile needs to be projected into a 2D graphic representing each storage location, such as a rectangle, to enable management of each storage location.

[0059] In one possible embodiment, the method further includes manually correcting the obtained two-dimensional storage location range.

[0060] It is understood that the two-dimensional rectangle representing the storage location range obtained through the methods of the preceding embodiments is based on the current material pile monitoring image and the material stacking status obtained through spatial scanning, and is related to the current material stacking quantity. However, due to other reasons, such as production planning requirements, it is necessary to adjust the range or location of certain storage locations, such as increasing the reserved space of certain storage locations. In such cases, the two-dimensional graphic representation of the corresponding storage location needs to be manually adjusted and corrected. Figure 4 The image shown is a schematic diagram of the manually adjusted operation page. After scaling, moving, and other processing, the final image displaying each storage location is obtained.

[0061] Figure 5 A structural diagram of an automatic warehouse location calibration system provided in an embodiment of the present invention is shown below. Figure 5 As shown, an automatic warehouse location calibration system includes an acquisition module, a space construction module, and an identification module, wherein:

[0062] The acquisition module is used to acquire information from each camera and the monitoring images from each camera, select the camera corresponding to the storage location to be calibrated based on the monitoring images from each camera, and also to acquire the spatial scanning data of the warehouse and obtain the stacking status information based on the spatial scanning data of the warehouse.

[0063] The spatial construction module is used to construct a 3D rendering camera object based on the information of the selected camera, render the entire scene of the 3D rendering camera object into a texture, and match the texture with the material stacking state information to obtain a spatial coordinate map containing information of each point.

[0064] The identification module is used to identify the entire material pile range based on the image data of the selected camera, and to cluster the pixels within the material pile range to find the contour pixels corresponding to each material pile; it is also used to sample the spatial coordinate map based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile, and to project the three-dimensional spatial range into a two-dimensional storage location range.

[0065] This embodiment may also include a correction module for manually correcting the obtained two-dimensional storage location range.

[0066] It is understood that the automatic warehouse location calibration system provided by the present invention corresponds to the automatic warehouse location calibration method provided in the foregoing embodiments. The relevant technical features of the automatic warehouse location calibration system can be referred to the relevant technical features of the automatic warehouse location calibration method, and will not be repeated here.

[0067] Please see Figure 6 , Figure 6 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 6 As shown, this embodiment of the invention provides an electronic device 600, including a memory 610, a processor 620, and a computer program 611 stored in the memory 610 and executable on the processor 620. When the processor 620 executes the computer program 611, it performs the following steps:

[0068] Select the camera corresponding to the storage location to be calibrated based on the monitoring screen of each camera, and obtain the stacking status information based on the spatial scanning data of the warehouse.

[0069] A 3D rendering camera object is constructed based on the information of the selected camera, and the entire scene of the 3D rendering camera object is rendered into the texture; the texture is matched with the material stacking state information to obtain a spatial coordinate map containing information of each point.

[0070] The entire material pile area is identified based on the image data from the selected camera. The pixels within the material pile area are clustered to find the contour pixels corresponding to each material pile. The spatial coordinate map is sampled based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile. The three-dimensional spatial range is then projected into a two-dimensional storage location range.

[0071] Please see Figure 7 , Figure 7 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by the present invention. (See diagram below.) Figure 7As shown, this embodiment provides a computer-readable storage medium 700, on which a computer program 711 is stored. When the computer program 711 is executed by a processor, it performs the following steps:

[0072] Select the camera corresponding to the storage location to be calibrated based on the monitoring screen of each camera, and obtain the stacking status information based on the spatial scanning data of the warehouse.

[0073] A 3D rendering camera object is constructed based on the information of the selected camera, and the entire scene of the 3D rendering camera object is rendered into the texture; the texture is matched with the material stacking state information to obtain a spatial coordinate map containing information of each point.

[0074] The entire material pile area is identified based on the image data from the selected camera. The pixels within the material pile area are clustered to find the contour pixels corresponding to each material pile. The spatial coordinate map is sampled based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile. The three-dimensional spatial range is then projected into a two-dimensional storage location range.

[0075] This invention provides an automatic warehouse location calibration method, system, and storage medium. Monitoring cameras installed within the warehouse capture images of various material pile areas. After processing the images, the outline pixels of the entire pile area are obtained. These outline pixels are mapped to a spatial coordinate graph to obtain the three-dimensional spatial range of each pile. Further projection yields the two-dimensional warehouse location range, thus achieving automatic warehouse location calibration. When material changes necessitate remarking of warehouse locations, updating the camera monitoring images and spatial scan data allows for recalculation and new location calibration. Manual adjustment and correction of calibrated locations are also possible. This invention eliminates the need for manual measurement of warehouse locations. Instead, it automatically determines the location range, size, and quantity based on warehouse stacking conditions and monitoring images, avoiding multiple measurement and data entry tasks due to production requirement changes. This improves warehouse location calibration efficiency and reduces labor intensity. The calculation of the warehouse location range is based on spatial scan data, resulting in minimal numerical deviation and high accuracy.

[0076] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0082] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for automatically calibrating warehouse locations, characterized in that, include: Select the camera corresponding to the storage location to be calibrated based on the monitoring screen of each camera, and obtain the stacking status information based on the spatial scanning data of the warehouse. A 3D rendering camera object is constructed based on the information of the selected camera, and the entire scene of the 3D rendering camera object is rendered into the texture; the texture is matched with the material stacking state information to obtain a spatial coordinate map containing information of each point. Based on image data from the selected camera, the entire material pile area is identified. Pixels within the material pile area are clustered to obtain the contour pixels corresponding to each material pile. The spatial coordinate map is sampled based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile. This three-dimensional spatial range is then projected into a two-dimensional storage location range, including: Multiple contour pixels of the same material pile are mapped to the spatial coordinate map for sampling to obtain the three-dimensional coordinates of multiple contour pixels of the same material pile. The three-dimensional coordinates are merged to obtain a spatial bounding box, which is the three-dimensional spatial range of each material pile. The spatial bounding box is projected onto a two-dimensional plane to obtain the range value of the storage location. A two-dimensional rectangular representation is constructed based on the range value of the storage location until the two-dimensional rectangles corresponding to all material piles are obtained.

2. The automatic location calibration method for a material warehouse according to claim 1, characterized in that, The step of selecting the camera corresponding to the location to be calibrated based on the monitoring images of each camera includes: Input the information of each surveillance camera in the warehouse, display the monitoring images of each camera, and select the camera with a clear view and direction that includes multiple material piles.

3. The automatic location calibration method for a material warehouse according to claim 1, characterized in that, The camera information includes at least the camera's position, orientation, video output width and height, and field of view.

4. The automatic location calibration method for a material warehouse according to claim 1, characterized in that, The stockpile status information is point cloud data or triangular mesh data.

5. The automatic location calibration method for a material warehouse according to claim 1, characterized in that, The step of identifying the entire material pile area based on image data from the selected camera, and clustering the pixels within the material pile area to obtain the contour pixels corresponding to each material pile, includes: Acquire image data output from the selected camera, perform color value filtering on the image data to filter out pixels with color values ​​that do not meet the requirements, and obtain the entire material stacking range with clear boundaries; Similarity point clustering is performed on the contour pixels of the entire material pile range, and contour pixels within the same pixel threshold range are regarded as contour pixels of the same material pile.

6. The automatic location calibration method for a material warehouse according to claim 1, characterized in that, Also includes: The obtained two-dimensional storage location range is manually corrected.

7. An automatic warehouse location calibration system, characterized in that, include: The acquisition module is used to acquire information from each camera and the monitoring images from each camera, select the camera corresponding to the storage location to be calibrated based on the monitoring images from each camera, and also to acquire the spatial scanning data of the warehouse and obtain the stacking status information based on the spatial scanning data of the warehouse. The spatial construction module is used to construct a 3D rendering camera object based on the information of the selected camera, render the entire scene of the 3D rendering camera object into a texture, and match the texture with the material stacking state information to obtain a spatial coordinate map containing information of each point. The identification module is used to identify the entire material pile area based on image data from a selected camera, cluster the pixels within the material pile area to obtain the contour pixels corresponding to each material pile, and sample the spatial coordinate map based on the contour pixels corresponding to each material pile to obtain the three-dimensional spatial range of each material pile, and project the three-dimensional spatial range into a two-dimensional storage location range, including: Multiple contour pixels of the same material pile are mapped to the spatial coordinate map for sampling to obtain the three-dimensional coordinates of multiple contour pixels of the same material pile. The three-dimensional coordinates are merged to obtain a spatial bounding box, which is the three-dimensional spatial range of each material pile. The spatial bounding box is projected onto a two-dimensional plane to obtain the range value of the storage location. A two-dimensional rectangular representation is constructed based on the range value of the storage location until the two-dimensional rectangles corresponding to all material piles are obtained.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the processor is used to execute computer management programs stored in the memory to implement the steps of the automatic warehouse location calibration method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer management program, which, when executed by a processor, implements the steps of an automatic warehouse location calibration method as described in any one of claims 1-6.

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