Logistics box status intelligent inspection system

Abstract

The present invention relates to an intelligent inspection system for the state of logistics boxes, the system comprising: a box conveying mechanism, which conveys the express boxes that have just come off the production line of the express boxes to the top of the appearance inspection flat plate; a last-layer mapping mechanism, which uses a radial basis function neural network to intelligently analyze the real-time deformation level corresponding to the express boxes according to the various visualized contents of the express boxes in the multiple optimized images. The intelligent inspection system for the state of logistics boxes of the present invention is stable in operation and intelligent in design. Because a visual capture mechanism is introduced to perform a bird's-eye view action on the inspection scene where the express boxes that have just come off the production line are located, and multiple optimization mechanisms are introduced to successively perform artifact removal actions, image spatial domain enhancement actions, and recursive filtering actions on the acquired inspection scene images to obtain multiple optimized images, and obtain the various visualized contents of the express boxes in the multiple optimized images to perform intelligent analysis of the real-time deformation level corresponding to the express boxes, thereby preventing inferior express boxes from entering the market.

Description

Technical Field

[0001] The present invention relates to the field of express delivery management, and in particular to an intelligent inspection system for the status of a logistics box. Background Art

[0002] Logistics is the main link of express delivery management. Logistics refers to the use of modern information technology and equipment to achieve rationalized service models and advanced service processes. Logistics emerged with the emergence of commodity production and developed with the development of commodity production. The content of logistics management includes three aspects:

[0003] 1. Management of various elements of logistics activities, including transportation, storage and other links;

[0004] 2. Management of the elements of the logistics system, namely the management of the six elements of people, finance, materials, equipment, methods and information;

[0005] 3. The management of specific functions in logistics activities mainly includes the management of logistics planning, quality, technology, economy and other functions.

[0006] However, in express management based on express boxes, the express boxes are likely to be deformed due to transportation and production reasons. Such express boxes will easily cause damage to internal objects and excessively squeeze the internal space, bringing inconvenience to express management. Summary of the invention

[0007] In order to solve the technical problems in the related fields, the present invention provides an intelligent inspection system for the status of logistics boxes, which introduces a visual capture mechanism arranged just above the appearance inspection flat plate to perform a bird's-eye view of the inspection scene where the express box that has just come off the line is located, so as to obtain and output the corresponding inspection scene image, and introduces a multiple optimization mechanism to successively perform artifact removal actions, image spatial domain enhancement actions and recursive filtering actions on the received inspection scene images, so as to obtain and output corresponding multiple optimized images with better image quality, obtain the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, and at the same time obtain the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and The average curvature data of the edge curve of the reference top figure corresponding to the express box is obtained, and the arithmetic mean value of the multiple curvature values ​​remaining after removing the maximum and minimum values ​​of the respective curvature values ​​corresponding to the evenly spaced positions on the edge curve of the reference top figure corresponding to the express box is calculated to obtain the value. A radial basis function neural network is also used to intelligently analyze the real-time deformation level corresponding to the express box according to the respective curvature data corresponding to the respective pixel points of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box, thereby completing the intelligent judgment of the degree of shape deviation of the express box.

[0008] According to the present invention, a logistics box state intelligent inspection system is provided, the system comprising:

[0009] The box conveying mechanism is used to convey the express box just off the express box production line to the top of the appearance inspection flat plate, the box conveying mechanism comprises a transmission belt, a driving wheel, a follower wheel and a main controller, the main controller is connected to the driving wheel, the transmission belt wraps the driving wheel and the follower wheel on both sides of the transmission belt, and the express box production line conveys the express box just off the express box production line to the transmission starting end of the transmission belt;

[0010] A visual capture mechanism is arranged just above the appearance inspection flat plate and is used to perform a top-down shooting action on the inspection scene where the express box just off the production line is located, so as to obtain and output a corresponding inspection scene image;

[0011] A multiple optimization mechanism, which is disposed in a control box body directly below the appearance inspection plate and is connected to the visual capture mechanism, is used to successively perform an artifact removal action, an image spatial domain enhancement action, and a recursive filtering action on the received inspection scene image to obtain and output a corresponding multiple optimization image;

[0012] The first-layer mapping mechanism is arranged in the control box body directly below the shape inspection plate and connected to the multiple optimization mechanism, and is used to receive the multiple optimization images, obtain the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimization images, and simultaneously obtain the number of pixel rows and pixel columns occupied by the edge of the express box in the multiple optimization images;

[0013] The secondary mapping mechanism is used to obtain the average curvature data of the edge curve of the reference top figure corresponding to the express box, and calculate the value obtained by arithmetic average of the remaining multiple curvature values ​​after removing the maximum and minimum values ​​of the curvature values ​​corresponding to the evenly spaced positions on the edge curve of the reference top figure corresponding to the express box;

[0014] The last layer mapping mechanism is connected to the first layer mapping mechanism and the second layer mapping mechanism respectively, and is used to use a radial basis neural network to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box;

[0015] Among them, a radial basis function neural network is used to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the benchmark top figure corresponding to the express box. The method includes: the higher the real-time deformation level corresponding to the express box obtained by the intelligent analysis, the greater the shape deviation of the express box just off the production line compared with the benchmark top figure.

[0016] It can be seen that the present invention mainly has the following three significant technical effects:

[0017] Technical effect A: A visual capture mechanism disposed directly above the appearance inspection flat plate is introduced to perform a bird's-eye view of the inspection scene where the express box that has just come off the production line is located, so as to obtain and output a corresponding inspection scene image, and a multiple optimization mechanism is introduced to successively perform an artifact removal action, an image spatial domain enhancement action, and a recursive filtering action on the received inspection scene image, so as to obtain and output corresponding multiple optimized images with better image quality, thereby providing more reliable reference information for the subsequent judgment of the degree of deformation of the express box;

[0018] Technical effect B: obtaining curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, and obtaining the number of pixel rows and pixel columns occupied by the edge of the express box in the multiple optimized images, and obtaining the average curvature data of the edge curve of the reference top figure corresponding to the express box, and calculating the arithmetic average of the remaining multiple curvature values ​​after removing the maximum and minimum values ​​of the curvature values ​​corresponding to each evenly spaced position on the edge curve of the reference top figure corresponding to the express box;

[0019] Technical effect C: A radial basis neural network is used to intelligently analyze the real-time deformation level of the express box according to the curvature data corresponding to each pixel point of the edge of the express box in multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top graphic corresponding to the express box, thereby completing the intelligent judgment of the degree of shape deviation of the express box.

[0020] The intelligent inspection system for the state of logistics boxes of the present invention is stable in operation and intelligent in design. By introducing a visual capture mechanism to perform a bird's-eye view of the inspection scene where the express boxes that have just come off the line are located, a multiple optimization mechanism is introduced to successively perform artifact removal, image spatial domain enhancement, and recursive filtering on the acquired inspection scene images to obtain multiple optimized images, and obtain various visualized contents of the express boxes in the multiple optimized images to perform intelligent analysis of the real-time deformation level corresponding to the express boxes, thereby preventing inferior express boxes from entering the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0022] Figure 1 Schematic diagram of the internal structure of a logistics box status intelligent inspection system according to the first embodiment of the present invention.

[0023] Figure 2 Schematic diagram of the internal structure of a logistics box status intelligent inspection system according to the second embodiment of the present invention.

[0024] Figure 3 Schematic diagram of the internal structure of a logistics box status intelligent inspection system according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0025] The embodiment of the intelligent inspection system for the state of a logistics box of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1This is a schematic diagram of the internal structure of a logistics box state intelligent inspection system according to a first embodiment of the present invention, wherein the system comprises:

[0027] The box conveying mechanism is used to convey the express box just off the express box production line to the top of the appearance inspection flat plate, the box conveying mechanism comprises a transmission belt, a driving wheel, a follower wheel and a main controller, the main controller is connected to the driving wheel, the transmission belt wraps the driving wheel and the follower wheel on both sides of the transmission belt, and the express box production line conveys the express box just off the express box production line to the transmission starting end of the transmission belt;

[0028] For example, a box conveying mechanism is used to convey an express box that has just come off the express box production line to just above the appearance inspection flat plate, the box conveying mechanism includes a transmission belt, a driving wheel, a follower wheel and a main controller, the main controller is connected to the driving wheel, the transmission belt wraps the driving wheel and the follower wheel on both sides of the transmission belt, and the express box production line conveys the express box that has just come off the express box production line to the transmission starting end of the transmission belt, including: selecting an MCU control chip to implement the main controller for connecting to the driving wheel;

[0029] A visual capture mechanism is arranged just above the appearance inspection flat plate and is used to perform a top-down shooting action on the inspection scene where the express box just off the production line is located, so as to obtain and output a corresponding inspection scene image;

[0030] A multiple optimization mechanism, which is disposed in a control box body directly below the appearance inspection plate and is connected to the visual capture mechanism, is used to successively perform an artifact removal action, an image spatial domain enhancement action, and a recursive filtering action on the received inspection scene image to obtain and output a corresponding multiple optimization image;

[0031] The first-layer mapping mechanism is arranged in the control box body directly below the shape inspection plate and connected to the multiple optimization mechanism, and is used to receive the multiple optimization images, obtain the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimization images, and simultaneously obtain the number of pixel rows and pixel columns occupied by the edge of the express box in the multiple optimization images;

[0032] The secondary mapping mechanism is used to obtain the average curvature data of the edge curve of the reference top figure corresponding to the express box, and calculate the value obtained by arithmetic average of the remaining multiple curvature values ​​after removing the maximum and minimum values ​​of the curvature values ​​corresponding to the evenly spaced positions on the edge curve of the reference top figure corresponding to the express box;

[0033] The last layer mapping mechanism is connected to the first layer mapping mechanism and the second layer mapping mechanism respectively, and is used to use a radial basis neural network to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box;

[0034] Among them, the radial basis neural network is used to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box, including: the higher the real-time deformation level corresponding to the express box obtained by intelligent analysis, the greater the shape deviation of the express box just off the express box production line compared with the reference top figure;

[0035] Among them, the multiple optimization mechanism is arranged in the control box body directly below the appearance inspection plate and connected to the visual capture mechanism, and is used to successively perform artifact removal actions, image spatial domain enhancement actions and recursive filtering actions on the received inspection scene image to obtain and output the corresponding multiple optimization images, including: the multiple optimization mechanism has built-in artifact processing components, enhancement processing components and filtering processing components, which are used to respectively perform artifact removal actions, image spatial domain enhancement actions and recursive filtering actions on the received image data;

[0036] Among them, the radial basis neural network is used to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box, including: the radial basis neural network used is a radial basis neural network after executing a set number of learning times, and the value of the set number is proportional to the number of pixel points in the inspection scene image;

[0037] Among them, the radial basis neural network is used to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the benchmark top figure corresponding to the express box. It also includes: the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the benchmark top figure corresponding to the express box are all in the form of data representation after numerical normalization.

[0038] Figure 2 Schematic diagram of the internal structure of a logistics box status intelligent inspection system according to the second embodiment of the present invention.

[0039] Compared to Figure 1 The intelligent inspection system for logistics box status according to the second embodiment of the present invention may further include:

[0040] A parameter configuration device is connected to the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism respectively, and is used to provide the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism with the parameter configuration services they need respectively;

[0041] The parameter configuration device is respectively connected to the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism, and is used to provide the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism with the parameter configuration services they need, including: the parameter configuration device uses an IIC configuration bus to provide the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism with the parameter configuration services they need;

[0042] The parameter configuration device uses the IIC configuration bus to provide the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism with the required parameter configuration services respectively, including: the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism have different IIC configuration addresses respectively;

[0043] The multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism, and the last-layer mapping mechanism respectively have different IIC configuration addresses, including: the different IIC configuration addresses respectively have by the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism, and the last-layer mapping mechanism are binary numerical representation modes;

[0044] And wherein, the different IIC configuration addresses respectively possessed by the multiple optimization mechanisms, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism are in a binary numerical representation mode, including: the byte lengths of the different IIC configuration addresses respectively possessed by the multiple optimization mechanisms, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism are equal.

[0045] Figure 3 Schematic diagram of the internal structure of a logistics box status intelligent inspection system according to the third embodiment of the present invention.

[0046] Compared to Figure 1 The intelligent inspection system for logistics box status according to the third embodiment of the present invention may further include:

[0047] A positioning operation device is connected to the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism respectively, and is used to provide positioning services for the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism respectively;

[0048] Wherein, the positioning operation device is respectively connected with the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism, and is used to provide positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism respectively, including: the positioning operation device adopts the GPS positioning mechanism to provide GPS positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism respectively;

[0049] And wherein, the positioning operation device is respectively connected to the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism, and is used to provide positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism respectively, including: the positioning operation device adopts the Beidou positioning mechanism to provide Beidou positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism respectively.

[0050] In addition, in the logistics box status intelligent inspection system, a multiple optimization mechanism is arranged in the control box body directly below the appearance inspection plate and is connected to the visual capture mechanism, which is used to successively perform artifact removal actions, image spatial domain enhancement actions and recursive filtering actions on the received inspection scene images to obtain and output corresponding multiple optimized images. It also includes: an artifact processing component, an enhancement processing component and a filtering processing component are connected in sequence.

[0051] While the invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled 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 claims.

Claims

1. A logistics box status intelligent inspection system, characterized in that: The system comprises: The box conveying mechanism is used to convey the express box just off the express box production line to the top of the appearance inspection flat plate, the box conveying mechanism comprises a transmission belt, a driving wheel, a follower wheel and a main controller, the main controller is connected to the driving wheel, the transmission belt wraps the driving wheel and the follower wheel on both sides of the transmission belt, and the express box production line conveys the express box just off the express box production line to the transmission starting end of the transmission belt; A visual capture mechanism is arranged just above the appearance inspection flat plate and is used to perform a top-down shooting action on the inspection scene where the express box just off the production line is located, so as to obtain and output a corresponding inspection scene image; A multiple optimization mechanism, which is disposed in a control box body directly below the appearance inspection plate and is connected to the visual capture mechanism, is used to successively perform an artifact removal action, an image spatial domain enhancement action, and a recursive filtering action on the received inspection scene image to obtain and output a corresponding multiple optimization image; The first-layer mapping mechanism is arranged in the control box body directly below the shape inspection plate and connected to the multiple optimization mechanism, and is used to receive the multiple optimization images, obtain the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimization images, and simultaneously obtain the number of pixel rows and pixel columns occupied by the edge of the express box in the multiple optimization images; The secondary mapping mechanism is used to obtain the average curvature data of the edge curve of the reference top figure corresponding to the express box, and calculate the value obtained by arithmetic average of the remaining multiple curvature values ​​after removing the maximum and minimum values ​​of the curvature values ​​corresponding to the evenly spaced positions on the edge curve of the reference top figure corresponding to the express box; The last layer mapping mechanism is connected to the first layer mapping mechanism and the second layer mapping mechanism respectively, and is used to use a radial basis neural network to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box; Among them, the radial basis neural network is used to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box, including: the higher the real-time deformation level corresponding to the express box obtained by intelligent analysis, the greater the shape deviation of the express box just off the express box production line compared with the reference top figure; A multiple optimization mechanism, which is arranged in the control box body directly below the appearance inspection plate and connected to the visual capture mechanism, is used to successively perform artifact removal, image spatial domain enhancement and recursive filtering actions on the received inspection scene image to obtain and output the corresponding multiple optimization images, including: the multiple optimization mechanism has built-in artifact processing components, enhancement processing components and filtering processing components, which are used to respectively perform artifact removal, image spatial domain enhancement and recursive filtering actions on the received image data, and the artifact processing components, enhancement processing components and filtering processing components are sequentially connected; Among them, the radial basis neural network is used to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the reference top figure corresponding to the express box, including: the radial basis neural network used is a radial basis neural network after executing a set number of learning times, and the value of the set number is proportional to the number of pixel points in the inspection scene image; Among them, the radial basis neural network is used to intelligently analyze the real-time deformation level corresponding to the express box according to the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the benchmark top figure corresponding to the express box. It also includes: the curvature data corresponding to each pixel point of the edge of the express box in the multiple optimized images, the number of pixel rows and the number of pixel columns occupied by the edge of the express box in the multiple optimized images, and the average curvature data of the edge curve of the benchmark top figure corresponding to the express box are all in the form of data representation after numerical normalization.

2. The intelligent inspection system for logistics box status according to claim 1, characterized in that: The system further comprises: A parameter configuration device is connected to the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism respectively, and is used to provide the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism with the parameter configuration services they need respectively; Among them, the parameter configuration device is respectively connected to the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism, and is used to provide the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism with the parameter configuration services they need respectively, including: the parameter configuration device uses the IIC configuration bus to provide the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism with the parameter configuration services they need respectively.

3. The intelligent inspection system for logistics box status according to claim 2, characterized in that: The parameter configuration device adopts the IIC configuration bus to provide the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism with the parameter configuration services they need, including: the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism have different IIC configuration addresses respectively.

4. The intelligent inspection system for logistics box status according to claim 3 is characterized in that: The multiple optimization mechanisms, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism respectively have different IIC configuration addresses, including: the different IIC configuration addresses of the multiple optimization mechanisms, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism are in binary numerical representation mode.

5. The intelligent inspection system for logistics box status according to claim 4, characterized in that: The different IIC configuration addresses respectively possessed by the multiple optimization mechanisms, the first-layer mapping mechanisms, the sub-layer mapping mechanisms and the last-layer mapping mechanisms are in a binary numerical representation mode, including: the byte lengths of the different IIC configuration addresses respectively possessed by the multiple optimization mechanisms, the first-layer mapping mechanisms, the sub-layer mapping mechanisms and the last-layer mapping mechanisms are equal.

6. The intelligent inspection system for logistics box status according to claim 1, characterized in that: The system further comprises: The positioning operation device is respectively connected to the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism, and is used to provide positioning services for the multiple optimization mechanism, the first layer mapping mechanism, the second layer mapping mechanism and the last layer mapping mechanism respectively.

7. The intelligent inspection system for logistics box status according to claim 6, characterized in that: The positioning operation device is respectively connected to the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism, and is used to provide positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism respectively, including: the positioning operation device adopts the GPS positioning mechanism to provide GPS positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the second-layer mapping mechanism and the last-layer mapping mechanism respectively.

8. The intelligent inspection system for logistics box status according to claim 6, characterized in that: The positioning operation device is respectively connected to the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism, and is used to provide positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism respectively, including: the positioning operation device adopts the Beidou positioning mechanism to provide Beidou positioning services for the multiple optimization mechanism, the first-layer mapping mechanism, the sub-layer mapping mechanism and the last-layer mapping mechanism respectively.

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