KR20170101555A - Goods loading system using 3d camera and bigdata system, and thereof method - Google Patents

Goods loading system using 3d camera and bigdata system, and thereof method Download PDF

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KR20170101555A
KR20170101555A KR1020160024236A KR20160024236A KR20170101555A KR 20170101555 A KR20170101555 A KR 20170101555A KR 1020160024236 A KR1020160024236 A KR 1020160024236A KR 20160024236 A KR20160024236 A KR 20160024236A KR 20170101555 A KR20170101555 A KR 20170101555A
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loading
cargo
image
information
space
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KR101815583B1 (en
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신동렬
채윤주
얼 김
김다연
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성균관대학교산학협력단
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    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K19/00—Record carriers for use with machines and with at least a part designed to carry digital markings
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    • G06K19/06018—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking one-dimensional coding
    • G06K19/06028—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking one-dimensional coding using bar codes
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    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
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Abstract

The present invention relates to a logistics loading system using a 3D camera and a big data platform, and a loading assisting method using the same. More particularly, the present invention relates to a loading space image in a container, A 3D camera unit; The information on the remaining freight space of the freight cargoes and information on the freight cargo in the loading space of the container from the loading space image and the cargo cargo image acquired by the 3D camera unit are generated to optimally load the cargo in the container loading space A central processing system for generating the loading scheduling information for performing the scheduling; And creating a virtual loading space corresponding to the loading space and a virtual loading object corresponding to the loading freight corresponding to the remaining space information and the transportation freight contents information and storing the virtual loading information in the virtual loading space according to the loading scheduling information, And an augmented reality image generation unit for generating an augmented reality load image by combining a virtual load simulation image for placing the object with the load space image and displaying the generated augmented reality load image on a display device.
The present invention provides a logistics loading assisting system that can efficiently and quickly perform an optimal loading even if an inexperienced beginner is not experienced by creating a loading scheduling method that is efficient in the case of a racking and informing the operator visually and intuitively, ≪ / RTI >

Description

TECHNICAL FIELD [0001] The present invention relates to a logistics load assisting system using a 3D camera and a big data platform,

The present invention relates to a logistics assisting system and a loading assisting method thereof, and more particularly, to a logistics assisting system and a loading assisting method thereof that omits a conventional bar code scanning process by a worker in sorting and loading processes and is capable of quickly and efficiently loading transportation freight A 3D camera that provides loading scheduling information, and a logistics support system using a big data platform.

As Internet commerce is rapidly becoming common, the volume of freight using courier services is rapidly increasing. Unlike the industrial logistics distribution system, the courier service deals with unstructured items, so the logistics cost and delivery time may vary depending on how quickly, efficiently, and efficiently each of these sizes is carried.

Conventionally, logistics loading systems adopted by courier companies are fundamentally conveying belts of cargo boxes to the cargo container entrance basically, and depend on the personal ability of skilled workers to build up cargo boxes with experience and sense. Therefore, there is a problem that the amount of the logistics is fluctuating according to the skill level of the worker, making it difficult for the work to be standardized and inefficiency.

In addition, there has been a problem that the operator has to manually scan barcodes of all cargoes received from the arterial car in the cargo handling dock of the logistics hub terminal. Second, in loading the luggage, conventionally, There is a problem that it is laborious, costly, and time-consuming due to the inconvenience of being loaded on a heavy truck.

Thirdly, when the car is moving, the size, shape and weight of the box are different, and cargo continues to come out. Therefore, the load of the novice and the expert is also different. Therefore, according to the experience and skill of the individual worker, And fourth, the courier company is responsible for damages when the courier goods are damaged or damaged. Most of the courier goods damage occurs during the up and down process. Therefore, the operator should check the handling mark and handle it carefully There is a problem that it is difficult to confirm the mark of the handling in the situation where the goods are continuously coming in. Therefore, there is a need for a method of preventing the damage caused by the damage of the goods and the economic loss thereof.

Korean Patent Registration No. 10-1493745 (Registration date: February 10, 2015) U.S. Patent Publication No. US2015 / 0199644 A1 (published on July 16, 2015)

The 3D camera and the log data loading assisting system using the big data platform according to the present invention and the loading assisting method thereof have the following problems.

First, the present invention intends to provide a logistics loading assisting system and a loading assisting method which can quickly and efficiently perform a logistics process by skipping a bar code scanning process manually performed at a logistics hub terminal of a courier.

Second, the present invention creates an efficient loading scheduling method through a central processing system when a large-sized truck is to be sent to a sales office and informs the operator visually and intuitively that even an inexperienced operator can perform optimum loading quickly and efficiently And to provide a method of assisting the loading of the logistics.

Third, the present invention is to provide a logistics assisting system and a loading assisting method that can automatically recognize a transportation cargo requiring handling attention in advance to reduce the economic loss due to the damage caused in the loading and transportation process.

The present invention has been made in view of the above problems, and it is an object of the present invention to provide an apparatus and method for controlling the same.

A first aspect of the present invention to solve the above-described problems is to provide a logistics loading assisting system using a camera and a big data platform, wherein the loading space image in the container and the transportation cargoes that are classified after getting off and transported through the conveyor belt are photographed A 3D camera unit for acquiring a transportation cargo image; The information on the remaining freight space of the freight cargoes and information on the freight cargo in the loading space of the container from the loading space image and the cargo cargo image acquired by the 3D camera unit are generated to optimally load the cargo in the container loading space A central processing system for generating the loading scheduling information for performing the scheduling; And creating a virtual loading space corresponding to the loading space and a virtual loading object corresponding to the loading freight corresponding to the remaining space information and the transportation freight contents information and storing the virtual loading information in the virtual loading space according to the loading scheduling information, And an augmented reality image generation unit for generating an augmented reality load image by combining a virtual load simulation image for placing the object with the load space image and displaying the generated augmented reality load image on a display device.

Here, the 3D camera unit may include: a 3D first camera that acquires a loading space inside the container at a container entrance and a loading space image of the loaded objects; And a 3D second camera for capturing a transported cargo image by photographing the transported cargo transported through the conveyor belt, wherein the central processing system comprises: It is preferable that size information of the freight cargo and contents information of the freight cargo are generated from the cargo cargo image acquired by the 3D second camera, in addition to the state where the cargo cargo is already loaded in the loading space and the remaining space information.

In addition, it is preferable that the cargo information includes at least any one of size, weight, and degree of damage of the cargo, and it is preferable that the cargo information includes at least one of three-dimensional It is preferable to calculate and obtain the three-dimensional size of the transportation cargo through the coordinates, and to recognize the barcode and character displayed on the transportation cargo through each RGB color value to acquire the content information.

Preferably, the central processing system is a big data system that calculates an input scheduling information by calculating an image input from a 3D camera unit as a parallel distributed system, and the 3D camera unit includes a 2D camera; And an optical sensor or an ultrasonic sensor capable of measuring a distance at a specific point of the object.

The second feature of the present invention is a logistics loading assisting system using a 3D camera and a big data platform, wherein the loading space image in the container and the transportation cargo transported through the conveyor belt after being taken out from the container are taken, A 3D camera unit; The loading space information and the loading cargo information of the cargo cargoes in the loading space of the container from the loading space image and the cargo cargo image acquired by the 3D camera unit and for optimally loading the cargo in the container loading space A central processing system for generating load scheduling information; And a loading freight sorting device for sorting the freight cargo according to the loading order of the loading scheduling information of the central processing system; And creating a virtual loading space corresponding to the loading space and a virtual loading object corresponding to the loading freight corresponding to the remaining space information and the transportation freight contents information and storing the virtual loading information in the virtual loading space according to the loading scheduling information, And an augmented reality image generation unit for generating an augmented reality load image by combining a virtual load simulation image for placing the object with the load space image and displaying the generated augmented reality load image on a display device.

Here, the 3D camera unit may include: a 3D first camera that acquires a loading space inside the container at a container entrance and a loading space image of the loaded objects; And a 3D second camera for capturing the transported cargo images by photographing the transported cargoes that are separated and sorted and transported through the conveyor belt.

In addition, the central processing system may further include a step of generating, from the loading space image acquired from the 3D first camera, the state where the loaded cargo is already loaded in the loading space and the residual space information, It is preferable to generate the size information of the transportation freight and the contents information of the transportation freight and the transportation freight information preferably includes at least any one of the size, .

In addition, the central processing system calculates and obtains the three-dimensional size of the transportation cargo from the transportation cargo image through the image processing on the three-dimensional coordinates of each pixel, It is preferable that the central processing system is a big data system that calculates the load scheduling information by calculating an image input from the 3D camera unit as a parallel distributed system.

The third feature of the present invention resides in a method of assisting the loading of a logistics using a 3D camera and a big data platform, comprising the steps of: (a) loading a space image inside a container in a 3D camera unit, Capturing and acquiring images; (b) generating, from the loading space image and the transportation cargo image acquired by the central processing unit, information on the remaining space of the transportation cargoes and information on the transportation cargo in the loading space of the container, Generating loading scheduling information for optimally loading the transportation cargo; And (c) the augmented reality image generation unit generates a virtual loading space corresponding to the loading space and a virtual loading object corresponding to the transportation cargo corresponding to the remaining space information and the transportation cargo contents information, And combining the virtual load simulation image for arranging the virtual cargo object in the virtual load space with the load space image to generate an augmented reality load image and displaying the generated augmented reality load image on a display device.

It is preferable that the method further comprises classifying the transportation cargo according to the loading order of the loading scheduling information received by the central processing system by the loading transportation cargo classifying apparatus, and in the step (b) The loading scheduling information may be configured to set weights according to size information, weight information, and handling information of the transportation freight so that the loading order and the position .

The 3D camera and the log data storage assisting system using the big data platform according to the present invention and the loading assisting method thereof have the following effects.

First, the present invention provides a logistics loading assisting system and a loading assisting method which can automate the bar code scanning process manually performed by using a 3D camera and a big data platform.

Second, the present invention relates to a method and apparatus for recognizing and acquiring external information and contents information of a freight cargo using a 3D camera and a big data platform, and realizing the loading scheduling information System and its loading aid method.

Third, the present invention provides visual and intuitive display of a virtual augmented reality load image in accordance with optimal load scheduling information, thereby providing a load assistant method for a logistics load assisting system that can perform a loading operation quickly and efficiently even for a beginner to provide.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.

1 is a schematic diagram of a logistics loading assisting system using a 3D camera and a big data platform according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating a flow of a logistics loading assisting method using a 3D camera and a big data platform according to another embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Wherever possible, the same or similar parts are denoted using the same reference numerals in the drawings.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms as used herein include plural forms as long as the phrases do not expressly express the opposite meaning thereto.

Means that a particular feature, region, integer, step, operation, element and / or component is specified and that other specific features, regions, integers, steps, operations, elements, components, and / It does not exclude the existence or addition of a group.

All terms including technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Predefined terms are further interpreted as having a meaning consistent with the relevant technical literature and the present disclosure, and are not to be construed as ideal or very formal meanings unless defined otherwise.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.

1 is a schematic diagram of a logistics loading assisting system using a 3D camera and a big data platform according to an embodiment of the present invention. As shown in FIG. 1, the loading assistance system according to the embodiment of the present invention includes a 3D camera unit for capturing a loading space image within a container and a transportation cargo image taken after getting off the cargo and being transported through a conveyor belt, (100); From the loading space image and the transportation cargo image acquired from the 3D camera unit 100, information on the remaining space of the transportation cargoes and information on the transportation cargo in the loading space of the container, A central processing system (200) for generating load scheduling information for optimal loading; And generating a virtual load space and a virtual load object corresponding to the remaining space information and the transport freight contents information, and generating a virtual load object in the virtual load space according to the load scheduling information, And an augmented reality image generation unit (300) for generating an augmented reality loaded image by combining a virtual placement simulation image for placing the object with the load space image and displaying the generated augmented reality loaded image on a display device.

As described above, according to the present invention, by using the central processing system 200 including the 3D camera and the big data system, conventionally, the transportation cargo information is acquired and stored in the logistics hub terminal of the delivery company manually by bar code scanning The present invention provides a logistics support system that can quickly and accurately automate the scheduling planning process.

In addition, the present invention provides a fast and efficient loading method to a worker by scheduling a loading method in a central processing system (200) when a large truck is loaded for freight transportation such as courier service, and it is visually and intuitively Thereby providing a logistics loading assisting system that can easily perform optimum loading even for an inexperienced operator who has no experience.

In addition, the present invention can provide a safe and economical logistics service by reducing the economic loss due to the damage of the goods by recognizing the contents of the goods or the freight to be handled with care by using the image processing technique in the information acquired from the 3D camera .

Here, the 3D camera unit 100 is a device that generates 3D coordinates through a plurality of cameras and accordingly acquires three-dimensional coordinate data of the object or a distance between the 2D camera and the object at each point Which is capable of acquiring three-dimensional coordinate data by combining an optical sensor or an ultrasonic sensor. Of course, it is also possible to acquire three-dimensional coordinate data through an image.

The 3D camera unit 100 includes a 3D first camera for acquiring a loading space inside the container and a loading space image obtained by photographing the load cargoes, And a 3D second camera for acquiring the transportation cargo image.

In other words, since the first 3D camera can accurately load three-dimensional information such as the size and volume of the loading space to be loaded, it is possible to load the transportation cargo to be loaded optimally so that various sizes, The loading space inside the container is photographed in real time, and the loading space image is acquired and transmitted to the central processing system 200.

The 3D second camera is a device for capturing the information of the cargo and the contents of the cargo when it is transported to the conveyor belt as a place for cargo sorting after each cargo leaves the truck after being taken out from the truck. As described above, the 3D second camera can measure the three-dimensional size and size of the outer shape of the transportation cargo by acquiring the three-dimensional coordinate data. The 3D second camera can perform the image processing from the image of the barcode, Contents information can be obtained.

That is, since the barcode attached to the delivery vehicle can be photographed and the barcode can be recognized from the photographed image information, it is possible to omit the process in which the operator has previously scanned all the barcodes directly with the barcode scanner. And the size of the delivery box can be calculated more accurately by the three-dimensional coordinates of x, y and z.

In addition, since the 3D second camera has x, y, z values and RGB color values for each pixel of the acquired image, the central processing system 200 receives the transported cargo image, It recognizes characters or texts, recognizes the items to be cautious when handling courier services such as handling care,? Fragile ?, etc., or other handling caution marks, so that it is possible to classify the items that are at risk of breakage. Such smart identification and classification data of the freight cargo can be determined in which of the upper and lower positions when the container freight is loaded, thereby obtaining loading scheduling information for stable and efficient loading.

The central processing system 200 generates the remaining space information and the transportation cargo information that can be loaded from the above-described loading space image and the transportation cargo image received from the 3D camera unit 100, and loads it for optimal loading in the container loading remaining space A computing device that generates scheduling information, or a big data system.

Cargo Handling at the Hub Terminal The courier cargo or freight cargoes departing from the Zhang dock are classified by region through the existing automatic cargo classifier. As described above, the transportation cargoes are photographed from the 3D camera unit 100, and the remaining space information and the transportation cargo information are inputted into the central processing system 200. In the central processing system 200, the three-dimensional outer shape information such as the size of the box and the contents information of the transportation freight are calculated using the image received from the 3D camera, and the three-dimensional loading position and order And generates scheduling information for scheduling.

The 3D image of the courier box or the transportation cargo obtained from the 3D camera unit 100 stores X, Y, and Z values, which are three-dimensional spatial values, and RGB values of the surface color in each pixel, The size is usually around 10MB. When a photographic image comes in from the camera in real time, the central processing system 200 calculates the three-dimensional size of the article from the input image, and classifies the handled article using barcode scanning and text recognition using the RGB value information of the surface .

In addition, the central processing system 200 adopts a big data analysis system or method in that a 3D image is to be processed for a relatively short period of time, since the data size of the remaining remaining space information and the cargo information is large, . When a large number of input images are input to the central processing system 200, the big data system of the central processing system 200 performs a parallel processing preprocessing process at a plurality of job nodes (computers) at the same time, Is scanned, the handling items are sorted, and the size of the article is calculated.

After the size of the article is calculated, the central processing system 200 calculates the load scheduling information in the optimum loading position and order in which the greatest amount of cargo can be loaded on the large truck in consideration of the previous article load information, To the real image generating unit 300 or the simulator.

Here, the big data system is formed as a group of Hadoop clusters capable of distributing and parallelizing a large amount of data as a computing device or a group thereof capable of network communication. That is, it is composed of a plurality of NoSQL databases and a cluster of Hadoop format composed of a master node and a plurality of slave nodes. The application of the central processing system 200 according to the embodiment of the present invention as a big data system is based on the fact that the search function of large capacity data can be enhanced by loading an Elasticsearch search engine, Or computation.

The augmented reality image generation unit 300 generates a virtual loading space corresponding to the loading space and a virtual loading object corresponding to the transportation cargo corresponding to the remaining space information and the transportation cargo contents information, And a virtual load simulation image in which a virtual load object is placed in the virtual load space according to the load scheduling information received in the load accumulation unit 200, and displays the augmented reality load image on a display device.

That is, according to the loading scheduling information generated in the central processing system 200, the position and order in which the courier goods or the transportation freight are loaded on the container of the heavy truck are visualized through the display device as the augmented reality. This allows the operator to perform the loading operation without worrying about how to load the goods.

As another embodiment of the present invention, in the embodiment illustrated in FIG. 1, a loading auxiliary system further comprising a loading carrying cargo sorting apparatus for sorting cargo in accordance with the loading order of the loading scheduling information of the central processing system 200 For example. In such a system, the load carrying cargo sorting apparatus receives the load scheduling information generated by the central processing system 200 and classifies the load cargo according to size, weight, and contents according to load scheduling immediately before the container to be loaded, It becomes possible to provide a load assisting system which can load a cargo more easily by a worker and shorten a loading time (not shown)

FIG. 2 is a flowchart illustrating a flow of a logistics loading assisting method using a 3D camera and a big data platform according to another embodiment of the present invention. As shown in FIG. 2, the logistics loading assisting method using the D camera and the big data platform according to the embodiment of the present invention uses the logistics loading assisting system of FIG. 1, wherein (a) A step S100 of capturing and capturing a transported cargo image that is transported through a conveyor belt after being separated from the loaded space image in the inside; (b) The central processing system 200 generates the loadable remaining space information of the transportation cargoes and the information of the transportation cargo in the loading space of the container from the loading space image and the transportation cargo image acquired from the 3D camera unit 100 (S210), generating loading scheduling information for optimally loading the transportation cargo in the container loading space (S250); And (c) the augmented reality image generation unit 300 generates a virtual loading space corresponding to the loading space and a virtual loading object corresponding to the transportation cargo corresponding to the remaining space information and the transportation cargo contents information (S310 ) Combining the virtual load simulation image in which the virtual load object is arranged in the virtual load space according to the load scheduling information with the load space image to generate an augmented reality load image and displaying it on the display device (S350) .

Hereinafter, the process of the logistics loading assisting method according to the embodiment of the present invention will be described in detail.

When the transportation freight comes in, it obtains the courier information such as the dispatch site, and accordingly, it is classified according to the region to be transported by the cargo classifier and moved through the conveyor belt. In step (a), 3D image information is acquired through the 3D camera through the transportation cargo in this process (S100). Here, the 3D camera includes a 2D camera and a distance measurement sensor (optical sensor, ultrasonic sensor, Devices that can be acquired are also possible.

In addition, the remaining remaining space image of the container to be loaded is acquired using the 3D camera, and transmitted to the central processing system 200 together with the transportation cargo image. Therefore, it is preferable that the 3D camera unit 100 is provided with a communication device connected to the central processing system 200 by wire or wireless, and capable of transmitting and receiving in real time.

In step (b), the central processing system 200 generates the remaining remaining space information from the load space image in real time, and obtains the information such as the size through the three-dimensional coordinates and distance of the cargo And recognizes the text or barcode displayed on the transportation cargo through the RGB color information of each pixel to generate the transportation cargo content information (S210). Herein, the loading natural space information is information of the transportation cargo already loaded in the inner space of the container Refers to the remaining space that can be loaded, minus the space corresponding to size and volume.

Then, the central processing system 200 performs an optimal matching of the above-described remaining space information and the cargo information using the Big Data Platform, calculates optimal loading scheduling information including position and order information for loading (S250)

In addition, the information collected from the 3D camera unit 100 is stored using a distributed storage system of a big data platform installed in the central processing system, and subjected to image preprocessing using a distributed real time processing system Is determined through an image preprocessing process, and image refinement is performed.) A 3D image of the transportation cargo is generated.

In step (c), the augmented reality image generation unit 300 generates a virtual loading space corresponding to the loading remaining space information and the carrying cargo contents information generated in the central processing system 200, A virtual load simulation image in which a virtual load object is placed in the virtual load space according to the load scheduling information generated in the central processing system 200 is combined with the load space image to be augmented After the real-load image is generated, it is displayed through the display device (S350)

That is, the augmented reality image generation unit 300 schedules an optimized loading method using the information collected in the central processing system 200 and the image information that has undergone the preprocessing process, and displays the simulated virtual image as an augmented reality loaded image .

Through this, the operator can easily view the image and quickly load the appropriate transportation cargo into the loading space of the container in an appropriate order. That is, the worker can be loaded on the hub loading and moving means of the destination area to be transmitted in the optimal order and method through the augmented reality screen.

Then, the baggage or transported cargo is moved by the loaded moving means and transmitted to the hub central processing system 200 of the destination area to which the information about the loaded cargo loaded through the load moving means in the central processing system is to be sent , The central processing system 200 of the corresponding area repeats the steps (a) to (c) together with the received information to proceed with the up-and-down process.

After the shipment is delivered to the final destination or the logistics recipient, the shipment that has not been shipped to the recipient is sent back to the distribution center and the above steps (a) and (c) are repeated. It is preferable that the central processing system 200 gives priority to the transportation freight that has not been delivered to the loaded transportation freight to generate the loading scheduling information. In other words, in the case of a freight that has not been received by the courier service, it is possible to schedule the priority of the next freight to be increased, thereby improving the efficiency of the delivery and shortening the delivery time.

In step (b), the central processing system 200 may update the generation of the loading scheduling information in real time or periodically. If loading freight information is changed due to loading of cargo freight and other freight information is changed according to other variables in other unpredictable conditions, loading scheduling information by matching the actual loading space with the freight cargo It can be different.

Preferably, the loading scheduling information is weighted according to the size information, the weight information, and the handling information of the transportation freight so that the loading order and the position are determined. In order to calculate the scheduling information for loading the transportation cargo in the optimal remaining order space and the remaining remaining space acquired by the central processing system 200, the handling information corresponding to the size information, the weight information, This is important.

For example, heavy cargo is loaded at the bottom, and fragile and fragile cargo is placed at the top, and bulk or large cargo is placed at the bottom, which is the most stable loading.

That is, the central processing system 200 generates and acquires contents information on the volume, weight, and damage of the transportation cargo based on the recognized barcode and the image processing result. Then, the central processing system 200 schedules the heavy and bulky items to be loaded first, giving them the greatest weight, and if the bulk or weight is small, the central processing system 200 schedules them to be loaded first Scheduling information having stability and optimum efficiency can be generated.

In the same category, weight and weight values are assigned to each element in order to determine the priority value. In order to increase the accuracy, the volume reference and the weight reference can be further subdivided so that the optimum load scheduling information . Table 1 is an example of a determination criterion of the loading method indicating the loading position and order of the loading scheduling information generated in the central processing system 200.

Whether or not to be damaged volume weight process ○ Reference size
More than Reference value
More than - Highest priority among current scheduling
- 0.7 * volume + 0.3 * in the same category. Larger weight value means higher priority ○ Reference size
More than Reference value
Below - Priority +1
- 0.7 * volume + 0.3 * in the same category. Larger weight value means higher priority ○ Reference size
Below Reference value
More than - Priority +2
- 0.7 * volume + 0.3 * in the same category. Larger weight value means higher priority ○ Reference size
Below Reference value
Below - Assign the lowest priority to be loaded last. × Reference size
More than Reference value
More than - Priority 1
- 0.7 * volume + 0.3 * in the same category. Larger weight value means higher priority × Reference size
More than Reference value
Below - Priority 2
- 0.7 * volume + 0.3 * in the same category. Larger weight value means higher priority × Reference size
Below Reference value
More than - Priority 3
- 0.7 * volume + 0.3 * in the same category. Larger weight value means higher priority × Reference size
Below Reference value
Below - Priority 4
- 0.7 * volume + 0.3 * in the same category. Larger weight value means higher priority

The embodiments and the accompanying drawings described in the present specification are merely illustrative of some of the technical ideas included in the present invention. Accordingly, the embodiments disclosed herein are for the purpose of describing rather than limiting the technical spirit of the present invention, and it is apparent that the scope of the technical idea of the present invention is not limited by these embodiments. It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

100: 3D camera unit 200: Central processing system
300: augmented reality image generation unit

Claims (17)

A 3D camera unit for capturing a loading space image inside the container and a transportation cargo image by capturing transportation cargoes classified after getting off and transported through a conveyor belt;
The information on the remaining freight space of the freight cargoes and information on the freight cargo in the loading space of the container from the loading space image and the cargo cargo image acquired by the 3D camera unit are generated to optimally load the cargo in the container loading space A central processing system for generating the loading scheduling information for performing the scheduling; And
A virtual load object corresponding to the remaining space information and the transported cargo contents information is created so as to correspond to the loaded space and the virtual cargo object corresponding to the transported cargo, And an augmented reality image generation unit for generating an augmented reality loaded image by combining the loaded augmented reality image with the loaded space image and displaying the generated augmented reality loaded image on a display device. The method according to claim 1,
The 3D camera unit includes:
A first 3D camera for acquiring a loading space inside the container at the entrance of the container and a loading space image of the load carries; And
And a second 3D camera for capturing the transported cargo images by taking images of the transported cargoes that are taken out and sorted and transported through the conveyor belt, and a 3D camera and a big data platform. The method of claim 2,
The central processing system,
From the loading space image acquired from the first 3D camera, generates a state in which the loaded cargo is already loaded in the loading space and remaining space information,
Dimensional image data and the contents information of the transportation freight from the transportation cargo image acquired from the 3D second camera. The method according to claim 1,
The cargo information,
And a handling information indicating the size, weight of the transportation cargo, and degree of possibility of damage when moving, and a 3D camera and a log data loading assistance system using the big data platform. The method according to claim 1,
The central processing system,
From the transported cargo image through image processing,
Dimensional size of the transportation cargo is calculated and obtained through the three-dimensional coordinates of each pixel,
And the contents information is obtained by recognizing the barcode and characters displayed on the transportation cargo through each RGB color value. The method according to claim 1,
The central processing system,
A 3D camera and a big data platform, wherein the 3D camera is a big data system that calculates an image input from a 3D camera unit as a parallel distributed system and calculates load scheduling information. The method according to claim 1,
The 3D camera unit,
2D camera; And
And a light sensor or an ultrasonic sensor capable of measuring a distance from a specific point of the object. A 3D camera unit for capturing a loading space image inside the container and a transportation cargo image by capturing transportation cargoes classified after getting off and transported through a conveyor belt;
The loading space information and the loading cargo information of the cargo cargoes in the loading space of the container from the loading space image and the cargo cargo image acquired by the 3D camera unit and for optimally loading the cargo in the container loading space A central processing system for generating load scheduling information;
And a loading freight sorting device for sorting the freight cargo according to the loading order of the loading scheduling information of the central processing system; And
A virtual load object corresponding to the remaining space information and the transported cargo contents information is created so as to correspond to the loaded space and the virtual cargo object corresponding to the transported cargo, And an augmented reality image generating unit for generating an augmented reality loaded image by combining the virtual augmented simulation image with the load space image and displaying the generated augmented reality loaded image on a display device. . The method of claim 8,
The 3D camera unit includes:
A first 3D camera for acquiring a loading space inside the container at the entrance of the container and a loading space image of the load carries; And
And a second 3D camera for capturing the transported cargo images by taking images of the transported cargoes that are taken out and sorted and transported through the conveyor belt, and a 3D camera and a big data platform. The method of claim 9,
The central processing system,
From the loading space image acquired from the first 3D camera, generates a state in which the loaded cargo is already loaded in the loading space and remaining space information,
Dimensional image data and the contents information of the transportation freight from the transportation cargo image acquired from the 3D second camera. The method of claim 8,
The cargo information,
And a handling information indicating the size, weight of the transportation cargo, and degree of possibility of damage when moving, and a 3D camera and a log data loading assistance system using the big data platform. The method of claim 8,
The central processing system,
From the transported cargo image through image processing,
Dimensional size of the transportation cargo is calculated and obtained through the three-dimensional coordinates of each pixel,
And the contents information is obtained by recognizing the barcode and characters displayed on the transportation cargo through each RGB color value. The method of claim 8,
The central processing system,
A 3D camera and a big data platform using a 3D data platform, wherein the 3D image data is a big data system that calculates an image input from a 3D camera unit as a parallel distributed system and calculates load scheduling information. (a) capturing and acquiring a loading space image within a container in a 3D camera unit and a transportation cargo image that is classified and then transported through a conveyor belt;
(b) generating, from the loading space image and the transportation cargo image acquired by the central processing unit, information on the remaining space of the transportation cargoes and information on the transportation cargo in the loading space of the container, Generating loading scheduling information for optimally loading the transportation cargo; And
(c) the augmented reality image generation unit generates a virtual loading space corresponding to the loading space and a virtual loading object corresponding to the transportation loading, corresponding to the remaining space information and the transportation cargo contents information, Combining the virtual load simulation image in which the virtual load object is placed in the virtual load space with the load space image to generate an augmented reality load image and displaying it on a display device. A method of assisting logistics loading. 15. The method of claim 14,
Further comprising the step of classifying the transportation cargo according to a loading order of the loading scheduling information received by the central processing system by the loading transportation cargo classifying device. 15. The method of claim 14,
Further comprising the step of updating the generation of the loading scheduling information in real time or periodically in the step (b). 15. The method of claim 14,
In the step (b)
Wherein the loading scheduling information is weighted according to size information, weight information, and handling information of the transportation cargo, and the loading order and the position are determined, and the 3D camera and the big data platform are used.
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