Automatic distribution system for intelligent logistics

Abstract

The invention relates to an automatic distribution system for intelligent logistics, and the system comprises a data storage device which is disposed in a cellular storage cabinet, and the cellular storage cabinet comprises a plurality of storage units with different volumes; the state notification equipment is used for executing a notification of allowing to receive a field state corresponding to the object when the field analysis volume, obtained through intelligent analysis, of the current to-be-stored object is smaller than or equal to more than one storage volume in the multiple storage volumes; and taking the storage unit corresponding to the storage volume closest to the received on-site analysis volume value in the more than one storage volume as a reference storage unit to perform on-site broadcasting of the storage number of the reference storage unit. Through the method and the device, the 3D entity volume corresponding to the current to-be-stored object can be intelligently analyzed, whether the cellular storage cabinet is rejected or not is determined based on the 3D entity volume, and the most matched storage unit is determined when rejection is not executed, so that the object storage speed and efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of smart logistics, and more particularly to an automated distribution system for smart logistics. Background Technology

[0002] Logistics, originally meaning "physical distribution" or "goods delivery," is a part of supply chain activities. It involves the planning, implementation, and control of the efficient and low-cost flow and storage of goods, services, and related information from production to consumption to meet customer needs. Logistics comprises the transportation of goods, services, distribution, warehousing, packaging, handling and loading / unloading, distribution processing, and related logistics information. Specific aspects of logistics activities include: customer service, demand forecasting, order processing, distribution, inventory control, transportation, warehouse management, factory and warehouse layout and location, handling and loading / unloading, procurement, packaging, and information management.

[0003] In the existing technology, when storing the items to be stored in the honeycomb storage cabinet at the logistics receiving end, since the honeycomb storage cabinet has multiple storage units of different sizes, it is necessary to manually judge the size of the items to be stored and the maximum capacity size of the multiple storage units. Based on the results of these two judgments, the storage units are manually numbered and selected, which makes the storage process cumbersome and the storage time too long. Summary of the Invention

[0004] According to the present invention, an automated distribution system for smart logistics is provided, the system comprising:

[0005] A data storage device is installed inside a honeycomb storage cabinet, which includes multiple storage units of different volumes. The data storage device is used to store multiple copies of the storage volume corresponding to each of the multiple storage units.

[0006] A status notification device, connected to the data storage device, is used to execute a status notification for rejecting an object when the received on-site analysis volume is greater than each of the multiple storage volumes, and is also used to execute a status notification for allowing the acceptance of an object when the received on-site analysis volume is less than or equal to one or more of the multiple storage volumes, and to broadcast the storage unit corresponding to the storage volume among the multiple storage volumes that is closest to the received on-site analysis volume value as a reference storage unit.

[0007] A visualization camera is positioned directly above the object to be stored, used to perform camera processing on the scene where the object is located, in order to obtain and output the corresponding scene image;

[0008] The successive mapping mechanism, connected to the visualization camera, includes an artifact removal device, a data sharpening device, and a recursive filtering device. It is used to sequentially perform artifact removal processing, spatial domain differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image.

[0009] An information application device, connected to the successive mapping mechanism, includes a component recognition unit, a region processing unit, and a volume estimation unit. The region processing unit is connected to both the component recognition unit and the volume estimation unit. The information application device is used to take the R component values, G component values, and B component values ​​in the received successive mapping image as target pixels in the R component value interval, G component value interval, and B component value interval corresponding to the current object to be stored, respectively. It fits each target pixel in the image to be processed to obtain a target region. Based on the total number of pixels occupied by the target region, the overall depth value of the target region, and the position information corresponding to each target pixel, it uses a feedforward neural network value after multiple learning operations to parse the 3D entity volume corresponding to the current object to be stored.

[0010] The information application device is also connected to the status notification device and is used to send the 3D entity volume corresponding to the object to be stored as the on-site resolution volume to the status notification device.

[0011] This invention solves the technical problems in the prior art. Based on the total number of pixels occupied by the target area where the object to be stored is located in the successively mapped image, the overall depth value of the target area, and the positional information corresponding to each target pixel, a feedforward neural network value after multiple learning iterations is used to analyze the 3D entity volume corresponding to the object to be stored. This 3D entity volume is sent as the on-site analyzed volume to a status notification device. The status notification device is used to reject the object's status notification when the on-site analyzed volume is greater than each of the multiple storage volumes corresponding to the multiple storage units of the honeycomb storage cabinet; and to allow the object's status notification when the on-site analyzed volume is less than or equal to one or more of the multiple storage volumes. The storage unit corresponding to the storage volume closest to the on-site analyzed volume among the multiple storage volumes is used as a reference storage unit, and its storage number is broadcast on-site. This achieves intelligent management of the honeycomb storage cabinet, reducing a significant amount of manual operation. Attached Figure Description

[0012] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0013] Figure 1This is a schematic diagram of the internal structure of an automated distribution system for smart logistics according to a first embodiment of the present invention.

[0014] Figure 2 This is a schematic diagram of the internal structure of an automated distribution system for smart logistics according to a second embodiment of the present invention.

[0015] Figure 3 This is a schematic diagram of the internal structure of an automated distribution system for smart logistics according to a third embodiment of the present invention. Detailed Implementation

[0016] The embodiments of the automatic distribution system for smart logistics of the present invention will now be described in detail with reference to the accompanying drawings.

[0017] First Embodiment

[0018] Figure 1 The diagram illustrates the internal structure of an automated distribution system for smart logistics according to a first embodiment of the present invention. The system includes the following components:

[0019] A data storage device is installed inside a honeycomb storage cabinet, which includes multiple storage units of different volumes. The data storage device is used to store multiple copies of the storage volume corresponding to each of the multiple storage units.

[0020] For example, a data storage device is installed in a honeycomb storage cabinet, the honeycomb storage cabinet including multiple storage units of different volumes, the data storage device being used to store multiple storage volumes corresponding to the multiple storage units respectively includes: the data storage device can be implemented using FLASH flash memory or MMC storage chips, for storing multiple storage volumes corresponding to the multiple storage units respectively;

[0021] A status notification device, connected to the data storage device, is used to execute a status notification for rejecting an object when the received on-site analysis volume is greater than each of the multiple storage volumes, and is also used to execute a status notification for allowing the acceptance of an object when the received on-site analysis volume is less than or equal to one or more of the multiple storage volumes, and to broadcast the storage unit corresponding to the storage volume among the multiple storage volumes that is closest to the received on-site analysis volume value as a reference storage unit.

[0022] A visualization camera is positioned directly above the object to be stored, used to perform camera processing on the scene where the object is located, in order to obtain and output the corresponding scene image;

[0023] The successive mapping mechanism, connected to the visualization camera, includes an artifact removal device, a data sharpening device, and a recursive filtering device. It is used to sequentially perform artifact removal processing, spatial domain differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image.

[0024] An information application device, connected to the successive mapping mechanism, includes a component recognition unit, a region processing unit, and a volume estimation unit. The region processing unit is connected to both the component recognition unit and the volume estimation unit. The information application device is used to take the R component values, G component values, and B component values ​​in the received successive mapping image as target pixels in the R component value interval, G component value interval, and B component value interval corresponding to the current object to be stored, respectively. It fits each target pixel in the image to be processed to obtain a target region. Based on the total number of pixels occupied by the target region, the overall depth value of the target region, and the position information corresponding to each target pixel, it uses a feedforward neural network value after multiple learning operations to parse the 3D entity volume corresponding to the current object to be stored.

[0025] The information application device is also connected to the status notification device and is used to send the 3D entity volume corresponding to the object to be stored as the on-site resolution volume to the status notification device.

[0026] The successive mapping mechanism, connected to the visualization camera and including an artifact removal device, a data sharpening device, and a recursive filtering device, is used to sequentially perform artifact removal processing, spatial differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image. The artifact removal device is used to perform artifact removal processing on the received image signal.

[0027] Second Embodiment

[0028] Figure 2 The schematic diagram of the internal structure of an automated distribution system for smart logistics according to a second embodiment of the present invention includes the following components:

[0029] A data storage device is installed inside a honeycomb storage cabinet, which includes multiple storage units of different volumes. The data storage device is used to store multiple copies of the storage volume corresponding to each of the multiple storage units.

[0030] A status notification device, connected to the data storage device, is used to execute a status notification for rejecting an object when the received on-site analysis volume is greater than each of the multiple storage volumes, and is also used to execute a status notification for allowing the acceptance of an object when the received on-site analysis volume is less than or equal to one or more of the multiple storage volumes, and to broadcast the storage unit corresponding to the storage volume among the multiple storage volumes that is closest to the received on-site analysis volume value as a reference storage unit.

[0031] A visualization camera is positioned directly above the object to be stored, used to perform camera processing on the scene where the object is located, in order to obtain and output the corresponding scene image;

[0032] The successive mapping mechanism, connected to the visualization camera, includes an artifact removal device, a data sharpening device, and a recursive filtering device. It is used to sequentially perform artifact removal processing, spatial domain differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image.

[0033] An information application device, connected to the successive mapping mechanism, includes a component recognition unit, a region processing unit, and a volume estimation unit. The region processing unit is connected to both the component recognition unit and the volume estimation unit. The information application device is used to take the R component values, G component values, and B component values ​​in the received successive mapping image as target pixels in the R component value interval, G component value interval, and B component value interval corresponding to the current object to be stored, respectively. It fits each target pixel in the image to be processed to obtain a target region. Based on the total number of pixels occupied by the target region, the overall depth value of the target region, and the position information corresponding to each target pixel, it uses a feedforward neural network value after multiple learning operations to parse the 3D entity volume corresponding to the current object to be stored.

[0034] A power detection device is disposed near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, and is respectively connected to the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device;

[0035] The power detection device, located near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device and connected to each of them, includes: the power detection device being used to perform on-site measurement of the current power of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

[0036] Third Embodiment

[0037] Figure 3 The diagram illustrates the internal structure of an automated distribution system for smart logistics according to a third embodiment of the present invention, comprising the following components:

[0038] A data storage device is installed inside a honeycomb storage cabinet, which includes multiple storage units of different volumes. The data storage device is used to store multiple copies of the storage volume corresponding to each of the multiple storage units.

[0039] A status notification device, connected to the data storage device, is used to execute a status notification for rejecting an object when the received on-site analysis volume is greater than each of the multiple storage volumes, and is also used to execute a status notification for allowing the acceptance of an object when the received on-site analysis volume is less than or equal to one or more of the multiple storage volumes, and to broadcast the storage unit corresponding to the storage volume among the multiple storage volumes that is closest to the received on-site analysis volume value as a reference storage unit.

[0040] A visualization camera is positioned directly above the object to be stored, used to perform camera processing on the scene where the object is located, in order to obtain and output the corresponding scene image;

[0041] The successive mapping mechanism, connected to the visualization camera, includes an artifact removal device, a data sharpening device, and a recursive filtering device. It is used to sequentially perform artifact removal processing, spatial domain differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image.

[0042] An information application device, connected to the successive mapping mechanism, includes a component recognition unit, a region processing unit, and a volume estimation unit. The region processing unit is connected to both the component recognition unit and the volume estimation unit. The information application device is used to take the R component values, G component values, and B component values ​​in the received successive mapping image as target pixels in the R component value interval, G component value interval, and B component value interval corresponding to the current object to be stored, respectively. It fits each target pixel in the image to be processed to obtain a target region. Based on the total number of pixels occupied by the target region, the overall depth value of the target region, and the position information corresponding to each target pixel, it uses a feedforward neural network value after multiple learning operations to parse the 3D entity volume corresponding to the current object to be stored.

[0043] A heat detection device is disposed near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, and is respectively connected to the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device;

[0044] The heat detection device, located near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, and connected to each of these devices, includes: the heat detection device being used to perform on-site measurement of the current heat of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

[0045] Next, the specific structure of the automatic distribution system for smart logistics of the present invention will be further described.

[0046] In an automated distribution system for smart logistics according to any embodiment of the present invention:

[0047] An MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processed data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

[0048] In an automated distribution system for smart logistics according to any embodiment of the present invention:

[0049] The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively. This includes performing histogram equalization on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively.

[0050] In an automated distribution system for smart logistics according to any embodiment of the present invention:

[0051] The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively. This includes performing logarithmic image enhancement on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively.

[0052] In an automated distribution system for smart logistics according to any embodiment of the present invention:

[0053] The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively. This includes performing exponential image enhancement on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively.

[0054] And in an automated distribution system for smart logistics according to any embodiment of the present invention:

[0055] The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device. This includes performing high-contrast-preserving image enhancement processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

[0056] In addition, in the automatic distribution system for smart logistics, the successive mapping mechanism, connected to the visualization camera and including an artifact removal device, a data sharpening device, and a recursive filtering device, is used to sequentially perform artifact removal processing, spatial differential sharpening processing, and adaptive recursive filtering processing on the received on-site scene image to obtain and output the corresponding successive mapping image. The data sharpening device is used to perform spatial differential sharpening processing on the received image signal.

[0057] The successive mapping mechanism, connected to the visualization camera, includes an artifact removal device, a data sharpening device, and a recursive filtering device. It is used to sequentially perform artifact removal processing, spatial domain differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image. The recursive filtering device is used to perform adaptive recursive filtering processing on the received image signal.

[0058] This invention has at least the following three important technical features:

[0059] 1. An information application device with a customized structure, including a component recognition unit, a region processing unit, and a volume estimation unit, is used to complete the screening of basic data for intelligent analysis of the volume of the 3D entity corresponding to the object to be stored.

[0060] 2. Based on the total number of pixels occupied by the target region where the object to be stored is located in the successively mapped image, the overall depth value of the target region, and the position information corresponding to each target pixel, the 3D solid volume corresponding to the object to be stored is analyzed using the feedforward neural network value after multiple learnings.

[0061] 3. The 3D solid volume corresponding to the object to be stored is sent as the on-site resolution volume to the status notification device. The status notification device is used to execute an on-site status notification to reject the object when the on-site resolution volume is greater than each of the multiple storage volumes corresponding to the multiple storage units of the honeycomb storage cabinet. When the on-site resolution volume is less than or equal to one or more of the multiple storage volumes, it executes an on-site status notification to allow the object to be received. The storage unit corresponding to the storage volume that is closest to the on-site resolution volume value among the multiple storage volumes is used as the reference storage unit and the storage number of the reference storage unit is broadcast on-site. This realizes intelligent management of the honeycomb storage cabinet and reduces a lot of manual operation steps.

[0062] The automatic allocation system for smart logistics of the present invention addresses the technical problem that the storage of items in honeycomb storage cabinets relies too heavily on manual methods, resulting in storage speed and efficiency that cannot meet the current level of logistics management. It can intelligently analyze the 3D physical volume of the item to be stored, and determine whether the honeycomb storage cabinet should reject the item based on the 3D physical volume. If rejection is not performed, it determines the most suitable storage unit, thereby improving the speed and efficiency of item storage.

[0063] By examining the accompanying drawings and detailed description, other systems, methods, features, and advantages of the present invention will become apparent or will become apparent to those skilled in the art. All additional systems, methods, features, and advantages should be included within the foregoing description, fall within the scope of the invention, and are protected by the appended claims.

Claims

1. An automated distribution system for smart logistics, characterized in that, The system includes: A data storage device is installed inside a honeycomb storage cabinet, which includes multiple storage units of different volumes. The data storage device is used to store multiple copies of the storage volume corresponding to each of the multiple storage units. A status notification device, connected to the data storage device, is used to execute a status notification for rejecting an object when the received on-site analysis volume is greater than each of the multiple storage volumes, and is also used to execute a status notification for allowing the acceptance of an object when the received on-site analysis volume is less than or equal to one or more of the multiple storage volumes, and to broadcast the storage unit corresponding to the storage volume among the multiple storage volumes that is closest to the received on-site analysis volume value as a reference storage unit. A visualization camera is positioned directly above the object to be stored, used to perform camera processing on the scene where the object is located, in order to obtain and output the corresponding scene image; The successive mapping mechanism, connected to the visualization camera, includes an artifact removal device, a data sharpening device, and a recursive filtering device. It is used to sequentially perform artifact removal processing, spatial domain differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image. An information application device, connected to the successive mapping mechanism, includes a component recognition unit, a region processing unit, and a volume estimation unit. The region processing unit is connected to both the component recognition unit and the volume estimation unit. The information application device is used to take the R component values, G component values, and B component values ​​in the received successive mapping image as target pixels in the R component value interval, G component value interval, and B component value interval corresponding to the current object to be stored, respectively. It fits each target pixel in the image to be processed to obtain a target region. Based on the total number of pixels occupied by the target region, the overall depth value of the target region, and the position information corresponding to each target pixel, it uses a feedforward neural network value after multiple learning operations to parse the 3D entity volume corresponding to the current object to be stored. The information application device is also connected to the status notification device and is used to send the 3D entity volume corresponding to the object to be stored as the on-site resolution volume to the status notification device.

2. The automatic distribution system for smart logistics as described in claim 1, characterized in that: The successive mapping mechanism, connected to the visualization camera and including an artifact removal device, a data sharpening device, and a recursive filtering device, is used to sequentially perform artifact removal processing, spatial domain differential sharpening processing, and adaptive recursive filtering processing on the received scene image to obtain and output the corresponding successive mapping image. The artifact removal device is used to perform artifact removal processing on the received image signal.

3. The automatic distribution system for smart logistics as described in claim 2, characterized in that, The system also includes: A power detection device is disposed near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, and is respectively connected to the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device; The power detection device, located near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device and connected to each of them, includes: the power detection device being used to perform on-site measurement of the current power of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

4. The automatic distribution system for smart logistics as described in claim 2, characterized in that, The system also includes: A heat detection device is disposed near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, and is respectively connected to the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device; The heat detection device, located near the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, and connected to each of these devices, includes: the heat detection device being used to perform on-site measurement of the current heat of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

5. The automated distribution system for smart logistics as described in any one of claims 2-4, characterized in that: An MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processed data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

6. The automatic distribution system for smart logistics as described in claim 5, characterized in that: The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively. This includes performing histogram equalization on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively.

7. The automatic distribution system for smart logistics as described in claim 5, characterized in that: The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively. This includes performing logarithmic image enhancement on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively.

8. The automatic distribution system for smart logistics as described in claim 5, characterized in that: The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively. This includes performing exponential image enhancement on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device, respectively.

9. The automatic distribution system for smart logistics as described in claim 5, characterized in that: The MSP430 microcontroller is used to perform image data processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device. This includes performing high-contrast-preserving image enhancement processing on the output data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device to obtain the corresponding output processing data of the information application device, the artifact removal device, the data sharpening device, and the recursive filtering device.

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