Logistics package management method and system, readable storage medium and computer

Abstract

The invention provides a logistics package management method and system, a readable storage medium and a computer. The method comprises the following steps: acquiring image data in a target detection area in a to-be-sorted area in real time to obtain a first image data set; performing image preprocessing on the first image data set, and performing contour extraction based on an image preprocessing result to obtain a second image data set; performing matching degree calculation on the image pixel region of each image in the second image data set by using a preset template database, and determining a standard template image corresponding to each image in the second image data set according to the calculated matching degree; performing positioning calculation on each image in the second image data set according to the standard template image to obtain pose information of each image, and performing sorting management on the logistics packages in the to-be-sorted area by utilizing the pose information. According to the invention, the image data after local segmentation is rapidly and accurately positioned through the standard template image, and the identification and detection accuracy and the positioning precision of logistics express are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a logistics package management method, system, readable storage medium and computer. Background Art

[0002] With the rapid development of science and technology and the improvement of people's living standards, the amount of logistics is gradually increasing. New management methods represented by multi-dimensional, all-round real-time monitoring have gradually penetrated into every level of logistics management, and have had a profound impact on the management model of logistics.

[0003] In the current logistics and transportation process, deep learning visual inspection algorithms are used to perform overall image analysis on images captured by the camera, and logistics packages are effectively sorted based on the image analysis results. However, traditional deep learning visual inspection algorithms are suitable for objects with weak textures and have good detection effects, but have poor detection effects on objects with complex textures and symmetry, and have problems such as slow detection and recognition speed and low positioning accuracy. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a logistics package management method, system, readable storage medium and computer to at least solve the deficiencies in the above-mentioned technology.

[0005] The present invention provides a logistics package management method, comprising: Constructing a target detection area in the area to be sorted, and collecting image data in the target detection area in real time to obtain a first image data set; Performing image preprocessing on the first image data set, and performing contour extraction based on the image preprocessing result to obtain a corresponding second image data set; Extracting an image pixel region of each image in the second image data set, and using a preset template database to calculate a matching degree for the image pixel region of each image in the second image data set, and determining a standard template image corresponding to each image in the second image data set according to the calculated matching degree; Positioning calculation is performed on each image in the second image data set according to the standard template image to obtain position information of each image in the second image data set, and the position information is used to perform sorting management on the logistics parcels in the sorting area.

[0006] Furthermore, the step of constructing a target detection area in the area to be sorted includes: Scanning the area to be sorted to obtain two-dimensional image data of the area to be sorted; The two-dimensional image data is subjected to bilateral filtering, and the obtained filtered image is subjected to background separation to construct a corresponding target detection area.

[0007] Furthermore, the step of performing image preprocessing on the first image data set and performing contour extraction based on the image preprocessing result to obtain the corresponding second image data set includes: Performing Gaussian filtering on all image data in the first image data set to obtain a preliminary image data set; Extracting pixel points of each image data in the preliminary image data set, and replacing the current pixel value of the image data according to the median value between two adjacent pixel points in the same image data, so as to obtain a preprocessed image data set; An edge detection algorithm is used to extract contours from the preprocessed image data set to obtain the second image data set.

[0008] Furthermore, the step of calculating the matching degree of the image pixel area of ​​each image in the second image data set by using a preset template database and determining the standard template image corresponding to each image in the second image data set according to the calculated matching degree includes: translating all template images in the template database in the image pixel area of ​​each image in the second image data set in a sliding window manner to obtain a coverage area of ​​each image in the second image data set covered by each template image; Calculating the matching degree between each of the template images and each image in the second image data set according to the coverage area; The standard template image corresponding to each image in the second image data set is determined according to the matching degree.

[0009] Furthermore, the calculation formula for the matching degree between each of the template images and each image in the second image data set is: ; In the formula, Indicates the first Template images In the second image dataset images Image pixel area The matching degree at Represents the image in the second image dataset Template Image the coverage area covered; , Represents the template image The number of rows and columns of pixels; Represents a template image All pixels of , Indicated in Image pixel area image and template image The average grayscale.

[0010] The present invention also provides a logistics package management system, comprising: A data acquisition module, used for constructing a target detection area in the area to be sorted, and collecting image data in the target detection area in real time to obtain a first image data set; An image preprocessing module, used to perform image preprocessing on the first image data set, and perform contour extraction based on the image preprocessing result to obtain a corresponding second image data set; a matching degree calculation module, used to extract the image pixel area of ​​each image in the second image data set, and use a preset template database to perform matching degree calculation on the image pixel area of ​​each image in the second image data set, and determine the standard template image corresponding to each image in the second image data set according to the calculated matching degree; The sorting management module is used to perform positioning calculation on each image in the second image data set according to the standard template image to obtain the posture information of each image in the second image data set, and use the posture information to sort the logistics packages in the sorting area.

[0011] Furthermore, the data acquisition module includes: A data scanning unit, used for performing data scanning on the area to be sorted to obtain two-dimensional image data of the area to be sorted; The background separation unit is used to perform bilateral filtering on the two-dimensional image data and perform background separation on the obtained filtered image to construct a corresponding target detection area.

[0012] Furthermore, the image preprocessing module includes: A Gaussian filtering unit, configured to perform Gaussian filtering on all image data in the first image data set to obtain a preliminary image data set; An image preprocessing unit, used for extracting pixel points of each image data in the preliminary image data set, and replacing the current pixel value of the image data according to the median value between two adjacent pixel points in the same image data, so as to obtain a preprocessed image data set; The contour extraction unit is used to extract the contour of the preprocessed image data set by using an edge detection algorithm to obtain the second image data set.

[0013] Furthermore, the matching degree calculation module includes: an area calculation unit, configured to translate all template images in the template database in the image pixel area of ​​each image in the second image data set in a sliding window manner, so as to obtain a coverage area of ​​each image in the second image data set covered by each template image; a matching degree calculation unit, configured to calculate a matching degree between each of the template images and each image in the second image data set according to the coverage area; A template determination unit is used to determine a standard template image corresponding to each image in the second image data set according to the matching degree.

[0014] The present invention also provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned logistics package management method is implemented.

[0015] The present invention also proposes a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned logistics package management method when executing the computer program.

[0016] The logistics package management method, system, readable storage medium and computer of the present invention pre-process the image data set of the sorting area to perform overall image analysis on the image data to identify the contour data of the image data and obtain the locally segmented image data; extract the image pixel area of ​​each image in the second image data set, calculate the matching degree thereof using the template database, segment each image in the second image data set using the obtained standard template image to process the individual package information, thereby quickly and accurately positioning the locally segmented image data, and realizing the sorting management of the logistics packages in the sorting area according to the precise positioning, thereby improving the accuracy of identification and detection of logistics express and the accuracy of positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a logistics package management method in a first embodiment of the present invention; Figure 2 for Figure 1 Detailed flow chart of step S101; Figure 3 for Figure 1 Detailed flow chart of step S102; Figure 4 for Figure 1 Detailed flow chart of step S103; Figure 5 is a structural block diagram of a logistics package management system in a second embodiment of the present invention; Figure 6 FIG. 4 is a structural block diagram of a computer in a third embodiment of the present invention.

[0018] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] Embodiment 1 See also Figure 1 , which shows a logistics package management method in a first embodiment of the present invention, and the method specifically includes steps S101 to S105: S101, constructing a target detection area in the area to be sorted, and collecting image data in the target detection area in real time to obtain a first image data set; For further information, see Figure 2 , the step S101 specifically includes steps S1011~S1012: S1011, performing data scanning on the area to be sorted to obtain two-dimensional image data of the area to be sorted; S1012, performing bilateral filtering on the two-dimensional image data, and performing background separation on the obtained filtered image to construct a corresponding target detection area.

[0022] In a specific implementation, the area to be sorted is scanned by an image acquisition device, wherein the image acquisition device includes but is not limited to a 3D camera. In some optional embodiments, the image acquisition device may also be other hardware devices with an image acquisition function. The two-dimensional image data of the area to be sorted obtained by the data scanning includes color images of multiple logistics packages and their corresponding depth images. The two-dimensional image data is subjected to bilateral filtering, and the filtered image after the bilateral filtering is subjected to background separation, so that the image of the non-logistics package area in the two-dimensional image data is stripped, and the target area obtained after the background separation is framed to construct a corresponding target detection area.

[0023] Furthermore, the above-mentioned image acquisition device is used to acquire image data within the target detection area in real time, that is, to acquire images of logistics packages within the target detection area in real time, and all images are integrated at intervals of the time when a single logistics package passes through the target detection area to obtain a first image data set.

[0024] S102, performing image preprocessing on the first image data set, and performing contour extraction based on the image preprocessing result to obtain a corresponding second image data set; For further information, see Figure 3 , the step S102 specifically includes steps S1021 to S1023: S1021, performing Gaussian filtering on all image data in the first image data set to obtain a preliminary image data set; S1022, extracting pixel points of each image data in the preliminary image data set, and replacing the current pixel value of the image data according to the median value between two adjacent pixel points in the same image data, so as to obtain a preprocessed image data set; S1023: Use an edge detection algorithm to perform contour extraction on the preprocessed image data set to obtain the second image data set.

[0025] In a specific implementation, a GaussianBlur function is used to perform a Gaussian filtering operation on each image in the first image data set to eliminate Gaussian noise in the image to obtain a preliminary image data set; Further, extract the pixel points of each image data in the above preliminary image data set, and use the median between adjacent pixels in the same image data to replace the corresponding current pixel value, so as to retain the edge details of the image to obtain the preprocessed image data set; Specifically, the above-mentioned preprocessed image data set is subjected to an image thresholding operation to obtain binary image data, the binary image data is denoised, and the edge information corresponding to the image is detected using an edge detection algorithm, and then the findContours() function is used to perform contour extraction to identify the contour edges in the image data to obtain a second image data set.

[0026] S103, extracting an image pixel region of each image in the second image data set, and using a preset template database to calculate a matching degree for the image pixel region of each image in the second image data set, and determining a standard template image corresponding to each image in the second image data set according to the calculated matching degree; For further information, see Figure 4 , the step S103 specifically includes steps S1031 to S1033: S1031, translating all template images in the template database in the image pixel area of ​​each image in the second image data set in a sliding window manner, so as to obtain a coverage area of ​​each image in the second image data set covered by each template image; S1032, calculating the matching degree between each of the template images and each image in the second image data set according to the coverage area; S1033: Determine a standard template image corresponding to each image in the second image data set according to the matching degree.

[0027] In the specific implementation, all template images in the preset template database are defined as ,in, represents the number of template images, ; Define the second image dataset as ,in, represents the number of images in the second image dataset, ; Specifically, the image pixel area of ​​each image in the second image data set is extracted, and all template images in the template database are translated in the image pixel area of ​​each image in the second image data set in a sliding window manner, so as to obtain the coverage area of ​​each image in the second image data set covered by each template image, and the matching degree between each template image and each image in the second image data set is calculated according to the coverage area: ; In the formula, Indicates the first Template images In the second image dataset images Image pixel area The matching degree at Represents the image in the second image dataset Template Image the coverage area covered; , Represents the template image The number of rows and columns of pixels; Represents a template image All pixels of , Indicated in Image pixel area image and template image The average grayscale.

[0028] Further, the template image with the greatest matching degree among the images in the second image data set is used as its corresponding standard template image. Template images In the second image dataset images The matching degree is the largest, then Template images Marked as the second image dataset images The standard template image.

[0029] S104: performing positioning calculation on each image in the second image data set according to the standard template image to obtain position information of each image in the second image data set, and performing sorting management on the logistics parcels in the to-be-sorted area using the position information.

[0030] In the specific implementation, the standard template image and the image in the corresponding second image data set are downsampled, the standard template image is rotated with the central pixel coordinates of the standard template image as the rotation center, the standard template image and the image in the corresponding second image data set at all angles are traversed, the matchTemplate() function is called in sequence, the best matching score and the corresponding angle are recorded, and a rough match is performed; Furthermore, the angle obtained by the rough matching is set as the initial value, and a smaller loop step is used to obtain a fine matching of the optimal matching angle, and finally the posture information of each image in the second image data set is obtained, wherein the posture information includes precise positioning information and rotation angle.

[0031] The obtained position information is converted into coordinate information of the sorting robot, and the coordinate information is used to control the sorting robot to sort and manage the logistics packages in the sorting area.

[0032] In summary, the logistics package management method in the above embodiment of the present invention pre-processes the image data set of the sorting area to perform overall image analysis on the image data to identify the contour data of the image data and obtain the locally segmented image data; extracts the image pixel area of ​​each image in the second image data set, calculates the matching degree thereof using the template database, and segments each image in the second image data set using the obtained standard template image to process the separate package information, thereby quickly and accurately positioning the locally segmented image data, and realizing the sorting management of the logistics packages in the sorting area according to the precise positioning, thereby improving the accuracy of recognition and detection of logistics express and the accuracy of positioning.

[0033] Embodiment 2 Another aspect of the present invention is to provide a logistics package management system, please refer to Figure 5 , which shows a logistics package management system in a second embodiment of the present invention, the system includes: The data acquisition module 11 is used to construct a target detection area in the area to be sorted, and to collect image data in the target detection area in real time to obtain a first image data set; Furthermore, the data acquisition module 11 includes: A data scanning unit, used for performing data scanning on the area to be sorted to obtain two-dimensional image data of the area to be sorted; The background separation unit is used to perform bilateral filtering on the two-dimensional image data and perform background separation on the obtained filtered image to construct a corresponding target detection area.

[0034] An image preprocessing module 12, configured to perform image preprocessing on the first image data set, and perform contour extraction based on the image preprocessing result to obtain a corresponding second image data set; Furthermore, the image preprocessing module 12 includes: A Gaussian filtering unit, configured to perform Gaussian filtering on all image data in the first image data set to obtain a preliminary image data set; An image preprocessing unit, used for extracting pixel points of each image data in the preliminary image data set, and replacing the current pixel value of the image data according to the median value between two adjacent pixel points in the same image data, so as to obtain a preprocessed image data set; The contour extraction unit is used to extract the contour of the preprocessed image data set by using an edge detection algorithm to obtain the second image data set.

[0035] a matching degree calculation module 13, configured to extract an image pixel region of each image in the second image data set, and perform matching degree calculation on the image pixel region of each image in the second image data set using a preset template database, and determine a standard template image corresponding to each image in the second image data set according to the calculated matching degree; Furthermore, the matching degree calculation module 13 includes: an area calculation unit, configured to translate all template images in the template database in the image pixel area of ​​each image in the second image data set in a sliding window manner, so as to obtain a coverage area of ​​each image in the second image data set covered by each template image; a matching degree calculation unit, configured to calculate a matching degree between each of the template images and each image in the second image data set according to the coverage area; A template determination unit is used to determine a standard template image corresponding to each image in the second image data set according to the matching degree.

[0036] The sorting management module 14 is used to perform positioning calculation on each image in the second image data set according to the standard template image to obtain the posture information of each image in the second image data set, and use the posture information to sort the logistics packages in the sorting area.

[0037] The functions or operation steps implemented when the above modules and units are executed are generally the same as those in the above method embodiments, and will not be repeated here.

[0038] The implementation principle and technical effects of the logistics parcel management system provided in the embodiment of the present invention are the same as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0039] Embodiment 3 The present invention also provides a computer, see Figure 6 , shown is a computer in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned logistics package management method is implemented.

[0040] The memory 10 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer, such as a hard disk of the computer. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Further, the memory 10 may also include both an internal storage unit of the computer and an external storage device. The memory 10 may be used not only to store application software and various types of data installed in the computer, but also to temporarily store data that has been output or is to be output.

[0041] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0042] It should be pointed out that Figure 6 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0043] The embodiment of the present invention further provides a readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the logistics package management method as described above is implemented.

[0044] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0045] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0046] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0047] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A logistics package management method, characterized in that: include: Constructing a target detection area in the area to be sorted, and collecting image data in the target detection area in real time to obtain a first image data set; Performing image preprocessing on the first image data set, and performing contour extraction based on the image preprocessing result to obtain a corresponding second image data set; Extracting an image pixel region of each image in the second image data set, and using a preset template database to calculate a matching degree for the image pixel region of each image in the second image data set, and determining a standard template image corresponding to each image in the second image data set according to the calculated matching degree; Positioning calculation is performed on each image in the second image data set according to the standard template image to obtain position information of each image in the second image data set, and the position information is used to perform sorting management on the logistics parcels in the sorting area.

2. The logistics package management method according to claim 1, characterized in that: The steps of constructing the target detection area in the area to be sorted include: Scanning the area to be sorted to obtain two-dimensional image data of the area to be sorted; The two-dimensional image data is subjected to bilateral filtering, and the obtained filtered image is subjected to background separation to construct a corresponding target detection area.

3. The logistics package management method according to claim 1, characterized in that: The steps of performing image preprocessing on the first image data set and performing contour extraction based on the image preprocessing result to obtain the corresponding second image data set include: Performing Gaussian filtering on all image data in the first image data set to obtain a preliminary image data set; Extracting pixel points of each image data in the preliminary image data set, and replacing the current pixel value of the image data according to the median value between two adjacent pixel points in the same image data, so as to obtain a preprocessed image data set; An edge detection algorithm is used to extract contours from the preprocessed image data set to obtain the second image data set.

4. The logistics package management method according to claim 1, characterized in that: The steps of calculating the matching degree of the image pixel area of ​​each image in the second image data set by using a preset template database and determining the standard template image corresponding to each image in the second image data set according to the calculated matching degree include: translating all template images in the template database in the image pixel area of ​​each image in the second image data set in a sliding window manner to obtain a coverage area of ​​each image in the second image data set covered by each template image; Calculating the matching degree between each of the template images and each image in the second image data set according to the coverage area; The standard template image corresponding to each image in the second image data set is determined according to the matching degree.

5. The logistics package management method according to claim 4, characterized in that: The calculation formula for the matching degree between each template image and each image in the second image data set is: ； In the formula, Indicates the first Template images In the second image dataset images Image pixel area The matching degree at Represents the image in the second image dataset Template Image the coverage area covered; , Represents the template image The number of rows and columns of pixels; Represents a template image All pixels of , Indicated in Image pixel area image and template image The average grayscale.

6. A logistics package management system, characterized in that: include: A data acquisition module, used for constructing a target detection area in the area to be sorted, and collecting image data in the target detection area in real time to obtain a first image data set; An image preprocessing module, used to perform image preprocessing on the first image data set, and perform contour extraction based on the image preprocessing result to obtain a corresponding second image data set; a matching degree calculation module, used to extract the image pixel area of ​​each image in the second image data set, and use a preset template database to perform matching degree calculation on the image pixel area of ​​each image in the second image data set, and determine the standard template image corresponding to each image in the second image data set according to the calculated matching degree; The sorting management module is used to perform positioning calculation on each image in the second image data set according to the standard template image to obtain the posture information of each image in the second image data set, and use the posture information to sort the logistics packages in the sorting area.

7. The logistics package management system according to claim 6, characterized in that: The data acquisition module comprises: A data scanning unit, used for performing data scanning on the area to be sorted to obtain two-dimensional image data of the area to be sorted; The background separation unit is used to perform bilateral filtering on the two-dimensional image data and perform background separation on the obtained filtered image to construct a corresponding target detection area.

8. The logistics package management system according to claim 6, characterized in that: The image preprocessing module comprises: A Gaussian filtering unit, configured to perform Gaussian filtering on all image data in the first image data set to obtain a preliminary image data set; An image preprocessing unit, used for extracting pixel points of each image data in the preliminary image data set, and replacing the current pixel value of the image data according to the median value between two adjacent pixel points in the same image data, so as to obtain a preprocessed image data set; The contour extraction unit is used to perform contour extraction on the preprocessed image data set using an edge detection algorithm to obtain the second image data set.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the logistics package management method as described in any one of claims 1 to 5 is implemented.

10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the logistics package management method as described in any one of claims 1 to 5 is implemented.

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