CN112529498B - Warehouse logistics management method and system - Google Patents
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Abstract
The invention discloses a warehouse logistics management method and system, wherein the management method comprises the steps of firstly, designing a route of an unmanned aerial vehicle according to a warehouse range and aerial photography parameters; 2. the unmanned aerial vehicle acquires aerial images according to the route; 3. image preprocessing is carried out on the acquired image; 4. generating a three-dimensional model according to the preprocessed image; 5. generating DEM data and DOM data according to the three-dimensional model; 6. forming a live three-dimensional map of the warehouse area through data fusion of the three-dimensional model, the DEM data and the DOM data; 7. and visually managing the warehouse logistics through the live-action three-dimensional map. The method has the advantages of simple steps, reasonable design and convenient realization, can be effectively applied to warehouse logistics management, combines a management system, realizes visual management and dynamic management, improves management efficiency, has obvious effect and is convenient to popularize.
Description
Technical Field
The invention belongs to the technical field of warehouse logistics, and particularly relates to a warehouse logistics management method and system.
Background
The basic work of logistics management is to collect and sort a large amount of logistics information, and at present, the collection of a large amount of storage data and logistics data basically depends on people, so that the workload is huge and the efficiency is low. For warehouse management, the first thing to solve is to track and manage and locate the incoming goods. In the prior art, a part of warehouse logistics uses a short-distance wireless transmission technology, such as an RFID technology, to monitor the entry and exit of goods, a label is attached to a batch of goods, main information of the batch of goods should be stored in the label, and when the batch of goods enters the warehouse, a reader-writer arranged at the position of entering the warehouse reads the goods information in the label, so that the fact that the batch of goods enters the warehouse is clarified. When the goods are transported out, the reader-writer at the delivery position senses the labels of the goods and reads the goods information in the labels, and then the background management system can know that the goods are delivered out. However, the management method can only know the flow direction of the goods, and the specific position of each batch of goods cannot be known after the goods enter the warehouse. Meanwhile, the problems of complicated goods, how to accurately and rapidly find target goods, measure and calculate the stock of various goods and the like all bring trouble to the management of the goods.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a warehouse logistics management method aiming at the defects in the prior art, which has the advantages of simple steps, reasonable design, convenient realization, effective application in warehouse logistics management, visual management and dynamic management, improvement of management efficiency, remarkable effect and convenient popularization.
In order to solve the technical problems, the invention adopts the following technical scheme: a warehouse logistics management method comprises the following steps:
firstly, designing a route of the unmanned aerial vehicle according to a storage range and aerial photography parameters;
step two, the unmanned aerial vehicle acquires aerial images according to the route;
step three, performing image preprocessing on the acquired image;
step four, generating a three-dimensional model according to the preprocessed image;
step five, generating DEM data and DOM data according to the three-dimensional model;
step six, forming a live-action three-dimensional map of the storage area through data fusion of the three-dimensional model, the DEM data and the DOM data;
and seventhly, visually managing the warehouse logistics through the live-action three-dimensional map.
In the above method for warehouse logistics management, in the first step, the warehouse range includes a whole three-dimensional geographic space for storing and flowing goods, and the aerial photography parameters include ground image resolution, image overlapping degree, altitude maintenance, photo inclination angle and photo rotation angle.
In the warehouse logistics management method, in the second step, the unmanned aerial vehicle performs aerial image acquisition according to the air route and adopts aerial triangulation, wherein the aerial triangulation comprises encryption point selection, encryption point observation, calculation and encryption process control.
The storage logistics management method comprises the steps that the encryption points observe and calculate the limit difference comprising the relative orientation limit difference and the model connection difference, and the relative orientation limit difference comprises the standard point upper-lower parallax and the check point upper-lower parallaxThe up-and-down parallax of the standard point is smaller than 0.003mm, and the up-and-down parallax of the check point is smaller than 0.007mm; the limited differences of the model connection differences comprise poor plane position and poor elevation, and the poor plane position delta S=0.5F sm -3d, said elevation being poorWherein DeltaS is poor in plane position, deltaH is poor in elevation, F sm Taking aerial photography proportion scale denominator, d is a photo base line, f l Is the focal length of the aerial camera.
In the above storage logistics management method, the image preprocessing of the collected image in the third step includes image enhancement of the low-illumination image and image stitching of the enhanced image, and the specific process of image enhancement includes:
step 3011, estimating an illumination component of the low-level image by adopting Gaussian weighted bilateral filtering;
step 3012, performing adaptive nonlinear stretching on the saturation component of the low-level image in the HSV color space to obtain an enhanced saturation component;
step 3013, performing single-scale Retinex algorithm enhancement on the brightness component of the low-level image in the HSV color space to obtain a reflection component;
step 3014, taking an index of the reflection component to obtain an enhanced brightness component;
step 3015, merging the hue, the enhanced saturation component and the enhanced brightness component, and converting the HSV color space into an RGB space;
and 3016, performing contrast correction processing by adopting a global self-adaptive logarithmic enhancement algorithm to obtain a final enhanced image.
The specific process of image stitching comprises the following steps:
step 3021, processing the enhanced image by adopting an image matching algorithm;
step 3022, extracting homonymous feature points contained in overlapping areas in adjacent images in the same aerial photographing band in the sequence images and homonymous feature points contained in overlapping areas in adjacent images in the adjacent aerial photographing band;
3023, performing Euclidean reconstruction on the obtained homonymous feature points and performing triangulation processing in a three-dimensional space;
and 3024, generating point cloud data of the warehouse goods through the sequence images of the warehouse goods.
In the above storage logistics management method, the specific process of generating the three-dimensional model according to the preprocessed image in the fourth step includes:
step 401, point cloud computing and registration
Solving transformation parameters among the frames of images, registering the images, taking a public part of a warehouse scene as a reference, superposing and matching multiple frames of images acquired at different times, angles and illumination into a unified coordinate system, calculating corresponding translation vectors and rotation matrixes, and eliminating redundant information;
step 402, data fusion
Dividing the point cloud space into a plurality of tiny cubes through a volume grid;
step 403, surface generation
And generating a three-dimensional model surface by using the real storage scene picture acquired by the inclined image as a three-dimensional model map and texture mapping.
In the above method for warehouse logistics management, the specific process of generating DEM data according to the three-dimensional model in the fifth step includes: and extracting characteristic points, lines and surface network interpolation according to the three-dimensional point cloud to generate the DEM.
In the above storage logistics management method, the specific process of generating DOM data according to the three-dimensional model in step five includes:
step 501, creating a test area file by introducing an empty three-encryption result, and recovering a model;
step 502, defining an operation area of a single model, generating a epipolar line image, and matching the epipolar line image to form a matching point and an equal parallax curve;
step 503, checking a matching result;
step 504, correcting and resampling the images by adopting a differential correction method according to the inside and outside azimuth elements of the images and the resolution of the images to generate a single model DOM;
step 505, hue and color adjustment;
and 506, setting a mosaic range according to the profile coordinates, designating a storage path, executing an image mosaic command and splicing the whole DOM.
The invention also discloses a warehouse logistics management system, which comprises:
the unmanned aerial vehicle carrying the camera is used for aerial photography and acquiring an acquired image;
the storage is used for storing a computer program, and the computer program is used for realizing a warehouse logistics management method;
a processor for executing the computer program in the memory;
and the display is used for displaying the real-scene three-dimensional map of the storage area obtained by the processor.
Compared with the prior art, the invention has the following advantages:
1. the method has simple steps, reasonable design and convenient realization.
2. According to the intelligent management system, the unmanned aerial vehicle is adopted to carry out intelligent management on the warehouse logistics, so that the working intensity of management staff is reduced, and the management efficiency is improved.
3. According to the invention, through unmanned aerial vehicle aerial photography and image preprocessing, and data fusion of the three-dimensional model, the DEM data and the DOM data, a live-action three-dimensional map of the storage area is formed, visual management of storage logistics is realized, target goods can be found accurately and rapidly, and stock measurement can be performed on various goods.
4. According to the image enhancement method in the image preprocessing, the irradiation component is estimated by adopting bilateral filtering, so that the halation phenomenon generated by the traditional algorithm is effectively avoided, and the edge blurring is prevented; according to the problem of insufficient image contrast improvement, the characteristics of a logarithmic function are utilized, a global self-adaptive logarithmic enhancement algorithm is adopted for correction, the high-illumination image is improved without being influenced, the estimation error of illumination intensity is effectively reduced, the generation of halation is avoided, the contrast is improved to play a role in obviously enhancing the dark area of the low-illumination image, and a good foundation is laid for subsequent image analysis.
5. The invention can be effectively applied to warehouse logistics management, realizes visual management and dynamic management, improves management efficiency, has obvious effect and is convenient to popularize.
In conclusion, the method has the advantages of simple steps, reasonable design and convenient realization, can be effectively applied to warehouse logistics management, combines a management system, realizes visual management and dynamic management, improves management efficiency, has obvious effect and is convenient to popularize.
Drawings
FIG. 1 is a flow chart of a warehouse logistics management method of the present invention;
FIG. 2 is a flow chart of a method of image enhancement according to the present invention;
FIG. 3 is a flow chart of a method of image stitching of the present invention;
FIG. 4 is a flow chart of a method of generating a three-dimensional model in accordance with the present invention;
fig. 5 is a flow chart of a method of generating DEM data in accordance with the invention.
Detailed Description
As shown in fig. 1, the warehouse logistics management method of the present invention comprises the following steps:
firstly, designing a route of the unmanned aerial vehicle according to a storage range and aerial photography parameters;
step two, the unmanned aerial vehicle acquires aerial images according to the route;
step three, performing image preprocessing on the acquired image;
step four, generating a three-dimensional model according to the preprocessed image;
step five, generating DEM data and DOM data according to the three-dimensional model;
step six, forming a live-action three-dimensional map of the storage area through data fusion of the three-dimensional model, the DEM data and the DOM data;
and seventhly, visually managing the warehouse logistics through the live-action three-dimensional map.
In this embodiment, in the first step, the storage range includes the whole three-dimensional geographic space where the goods are stored and moved, and the aerial photography parameters include ground image resolution, image overlapping degree, altitude maintenance, photo inclination angle and photo rotation angle.
In specific implementation, the resolution of the ground image is not lower than 0.08 m, the average course overlapping degree of course overlapping degree is 70%, the average side overlapping degree is 55%, the altitude difference of adjacent pictures on the same course is not greater than 5 m, the difference between the maximum altitude and the minimum altitude is not greater than 10 m, the difference between the actual altitude and the design altitude is not greater than 15 m, the picture inclination angle is not greater than 8 degrees, the number of pictures exceeding 5 degrees is not greater than 10% of the total number, the picture rotation angle is not greater than 10 degrees, and the number of pictures reaching or approaching the maximum rotation deflection angle limit difference on one course is not greater than 20 degrees on the premise that the picture course and the side overlapping degree meet the standard requirement; the number of shots at which the maximum rotation angle occurs in one shot is not more than 10% of the total number of shots in the shot.
In this embodiment, in the second step, the unmanned aerial vehicle performs aerial image acquisition according to the route, and aerial triangulation is used, where the aerial triangulation includes encryption point selection, encryption point observation and calculation, and encryption process control.
In the specific implementation, photo data are adopted as original data of image control encryption, relative orientation is carried out piece by piece, and then absolute orientation is carried out according to an area network, and then integral adjustment of a beam method area network is carried out, so that an encryption point result is obtained, the connection point of encryption is selected to be nearby six standard points of 1, 3, 5, 2, 4 and 6, wherein the 1 point and the 2 point are selected within a range of 1cm from an image main point, the 3 point, 4 point, 5 point and 6 point are consistent with a mapping orientation point, and the distance from the 3 point, 4 point, 5 point and 6 point to an azimuth line is larger than 4cm.
In this embodiment, the encryption point observation and calculation includes a relative directional limit difference and a model connection difference, where the relative directional limit difference includes a standard point up-down parallax and a check point up-down parallax, the standard point up-down parallax is less than 0.003mm, and the check point up-down parallax is less than 0.007mm; the limited differences of the model connection differences comprise poor plane position and poor elevation, and the poor plane position delta S=0.5F sm -3d, said elevation being poorWherein DeltaS is poor in plane position, deltaH is poor in elevation, F sm Taking aerial photography proportion scale denominator, d is a photo base line, f l Is the focal length of the aerial camera.
In this embodiment, the image preprocessing of the acquired image in the third step includes image enhancement of the low-illumination image and image stitching of the enhanced image, as shown in fig. 2, where the specific process of image enhancement includes:
step 3011, estimating an illumination component of the low-level image by adopting Gaussian weighted bilateral filtering;
in the implementation, in a flat area of an image, the pixel value change of the image is small, the space weight plays a main role and is equivalent to Gaussian blur, in an edge area of the image, the pixel value change range is large, and the similar weight is successively large, so that the information of the edge is maintained. Therefore, bilateral filtering can adaptively estimate the place with large difference of the edges of the low-illumination images, and can effectively avoid the halation phenomenon while maintaining the edge information of the images.
Step 3012, performing adaptive nonlinear stretching on the saturation component of the low-level image in the HSV color space to obtain an enhanced saturation component;
in particular, in order to make the image color full, the saturation needs to be stretched, the color image has R, G, B three color channels, when the Retinex enhancement is performed on the color image, the three channels need to be enhanced respectively, but three components in the RGB color space have strong correlation, the enhancement of the three components respectively damages the correlation between the three components to cause the color distortion of the image, and the hue, the saturation and the brightness components can be separated in the HSV color space, so that the brightness and the saturation of the image are improved, and meanwhile, the original color structure of the image can be well reserved.
Step 3013, performing single-scale Retinex algorithm enhancement on the brightness component of the low-level image in the HSV color space to obtain a reflection component;
step 3014, taking an index of the reflection component to obtain an enhanced brightness component;
step 3015, merging the hue, the enhanced saturation component and the enhanced brightness component, and converting the HSV color space into an RGB space;
and 3016, performing contrast correction processing by adopting a global self-adaptive logarithmic enhancement algorithm to obtain a final enhanced image.
In particular, after Retinex enhancement is performed on a low-luminance image based on bilateral filtering, the contrast enhancement degree of different images is different, so that the enhanced image needs to be corrected. The global self-adaptive logarithmic enhancement algorithm utilizes logarithmic characteristics and logarithmic mapping relations, logarithmic average brightness is introduced, when the dynamic range of a scene changes, the average brightness value is always smaller than or equal to the maximum brightness value no matter whether the image is too dark or too strong, the display brightness value can be mapped to 0-1, smooth increment of other display brightness values is ensured, distortion phenomenon of the image can not occur, the logarithmic characteristics enhancement is obviously effected on a low-illumination image, and the phenomenon of over-enhancement can also be prevented from influencing a high-illumination image by utilizing the logarithmic mapping relations.
Under night or backlight conditions, the images shot by the unmanned aerial vehicle have the problems of poor brightness, poor contrast and the like, and the subsequent various image processing is affected to a certain extent, so that the image contrast is improved and dark areas are restrained through image enhancement.
In this embodiment, as shown in fig. 3, the specific process of image stitching includes:
step 3021, processing the enhanced image by adopting an image matching algorithm;
step 3022, extracting homonymous feature points contained in overlapping areas in adjacent images in the same aerial photographing band in the sequence images and homonymous feature points contained in overlapping areas in adjacent images in the adjacent aerial photographing band;
3023, performing Euclidean reconstruction on the obtained homonymous feature points and performing triangulation processing in a three-dimensional space;
and 3024, generating point cloud data of the warehouse goods through the sequence images of the warehouse goods.
In this embodiment, as shown in fig. 4, the specific process of generating the three-dimensional model according to the preprocessed image in the fourth step includes:
step 401, point cloud computing and registration
Solving transformation parameters among the frames of images, registering the images, taking a public part of a warehouse scene as a reference, superposing and matching multiple frames of images acquired at different times, angles and illumination into a unified coordinate system, calculating corresponding translation vectors and rotation matrixes, and eliminating redundant information;
step 402, data fusion
Dividing the point cloud space into a plurality of tiny cubes through a volume grid;
in the implementation, the depth information after registration is still point cloud data scattered and disordered in space, and only partial information of scenes can be displayed, so that fusion processing is required to be carried out on the point cloud data to obtain a finer reconstruction model.
Step 403, surface generation
And generating a three-dimensional model surface by using the real storage scene picture acquired by the inclined image as a three-dimensional model map and texture mapping.
In this embodiment, the specific process of generating DEM data according to the three-dimensional model in the fifth step includes: and extracting characteristic points, lines and surface network interpolation according to the three-dimensional point cloud to generate the DEM.
In the specific implementation, the DEM grid points are mapped to a three-dimensional model, inspection and observation are carried out, and each DEM grid point is guaranteed to be close to the ground; when the elevation error of the DEM grid point exceeds the limit and needs to be edited, a problematic contour line and Cheng Zhuji points should be searched for repair, or feature points and feature lines are added, and the grid interpolation DEM is reconstructed.
In this embodiment, as shown in fig. 5, the specific process of generating DOM data according to the three-dimensional model in the fifth step includes:
step 501, creating a test area file by introducing an empty three-encryption result, and recovering a model;
step 502, defining an operation area of a single model, generating a epipolar line image, and matching the epipolar line image to form a matching point and an equal parallax curve;
step 503, checking a matching result;
step 504, correcting and resampling the images by adopting a differential correction method according to the inside and outside azimuth elements of the images and the resolution of the images to generate a single model DOM;
step 505, hue and color adjustment;
and 506, setting a mosaic range according to the profile coordinates, designating a storage path, executing an image mosaic command and splicing the whole DOM.
When the method is implemented, after the image embedding is completed, the generated DOM is required to be checked, and the repair is required to be given to the part where the image blurring and the image omission occur in the butt joint edge area, so that the distortion of the image generated by inconsistent projection directions of high-rise goods is prevented.
The warehouse logistics management system of the present invention comprises:
the unmanned aerial vehicle carrying the camera is used for aerial photography and acquiring an acquired image;
the storage is used for storing a computer program, and the computer program is used for realizing a warehouse logistics management method;
a processor for executing the computer program in the memory;
and the display is used for displaying the real-scene three-dimensional map of the storage area obtained by the processor.
In the implementation process, the memory, the processor and the display can be integrated in various mobile terminals, so that the mobile terminal is convenient to carry and easy to manage.
According to the invention, the visual management of the warehouse logistics is realized through the real-time three-dimensional map of the unmanned aerial vehicle mapping warehouse area, the change of goods in the warehouse area is updated in real time, and the specific position of each batch of goods is updated in real time after the goods enter the warehouse area, so that the target goods can be accurately and rapidly found, meanwhile, the stock measurement and calculation can be carried out on various goods, and the intelligent management of the warehouse logistics is improved.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and any simple modification, variation and equivalent structural changes made to the above embodiment according to the technical substance of the present invention still fall within the scope of the technical solution of the present invention.
Claims (6)
1. The warehouse logistics management method is characterized by comprising the following steps of:
firstly, designing a route of the unmanned aerial vehicle according to a storage range and aerial photography parameters;
step two, the unmanned aerial vehicle acquires aerial images according to the route;
step three, performing image preprocessing on the acquired image;
the image preprocessing of the acquired image comprises image enhancement of a low-illumination image and image stitching of the enhanced image, and the specific process of image enhancement comprises the following steps:
step 3011, estimating an illumination component of the low-level image by adopting Gaussian weighted bilateral filtering;
step 3012, performing adaptive nonlinear stretching on the saturation component of the low-level image in the HSV color space to obtain an enhanced saturation component;
step 3013, performing single-scale Retinex algorithm enhancement on the brightness component of the low-level image in the HSV color space to obtain a reflection component;
step 3014, taking an index of the reflection component to obtain an enhanced brightness component;
step 3015, merging the hue, the enhanced saturation component and the enhanced brightness component, and converting the HSV color space into an RGB space;
step 3016, performing contrast correction processing by adopting a global self-adaptive logarithmic enhancement algorithm to obtain a final enhanced image;
the specific process of the image stitching comprises the following steps:
step 3021, processing the enhanced image by adopting an image matching algorithm;
step 3022, extracting homonymous feature points contained in overlapping areas in adjacent images in the same aerial photographing band in the sequence images and homonymous feature points contained in overlapping areas in adjacent images in the adjacent aerial photographing band;
3023, performing Euclidean reconstruction on the obtained homonymous feature points and performing triangulation processing in a three-dimensional space;
step 3024, generating point cloud data of the warehouse goods through the sequence images of the warehouse goods;
step four, generating a three-dimensional model according to the preprocessed image;
the specific process comprises the following steps:
step 401, point cloud computing and registration
Solving transformation parameters among the frames of images, registering the images, taking a public part of a warehouse scene as a reference, superposing and matching multiple frames of images acquired at different times, angles and illumination into a unified coordinate system, calculating corresponding translation vectors and rotation matrixes, and eliminating redundant information;
step 402, data fusion
Dividing the point cloud space into a plurality of tiny cubes through a volume grid;
step 403, surface generation
The real storage scene picture acquired by the inclined image is used as a three-dimensional model map, and a three-dimensional model surface is generated through texture mapping;
step five, generating DEM data and DOM data according to the three-dimensional model;
the specific process for generating DOM data according to the three-dimensional model comprises the following steps:
step 501, creating a test area file by introducing an empty three-encryption result, and recovering a model;
step 502, defining an operation area of a single model, generating a epipolar line image, and matching the epipolar line image to form a matching point and an equal parallax curve;
step 503, checking a matching result;
step 504, correcting and resampling the images by adopting a differential correction method according to the inside and outside azimuth elements of the images and the resolution of the images to generate a single model DOM;
step 505, hue and color adjustment;
step 506, setting a mosaic range according to the graph profile coordinates, designating a storage path, executing an image mosaic command, and splicing a whole DOM;
step 507, after the image embedding is completed, checking the generated DOM, and repairing the part where the image blurring and the image omission occur in the abutting edge area;
step six, forming a live-action three-dimensional map of the storage area through data fusion of the three-dimensional model, the DEM data and the DOM data;
and seventhly, visually managing the warehouse logistics through the live-action three-dimensional map.
2. A warehouse logistics management method as claimed in claim 1, wherein in step one the warehouse area includes the entire three-dimensional geographic space for goods storage and flow, and the aerial photography parameters include ground image resolution, image overlap, voyage hold, photo tilt angle, and photo rotation angle.
3. The warehouse logistics management method according to claim 1, wherein in the second step, the unmanned aerial vehicle performs aerial image acquisition according to a route by adopting aerial triangulation, and the aerial triangulation comprises encryption point selection, encryption point observation, calculation and encryption process control.
4. A warehouse logistics management method as claimed in claim 3, wherein the encryption point observation and calculation includes a relative directional limit difference and a model connection difference limit difference, the relative directional limit difference includes a standard point up-down parallax and a check point up-down parallax, the standard point up-down parallax is less than 0.003mm, and the check point up-down parallax is less than 0.007mm; the limited differences of the model connection differences comprise poor plane position and poor elevation, and the poor plane position delta S=0.5F sm -3d, said elevation being poorWherein DeltaS is poor in plane position, deltaH is poor in elevation, F sm Taking aerial photography proportion scale denominator, d is a photo base line, f l Is the focal length of the aerial camera.
5. The warehouse logistics management method as claimed in claim 1, wherein the specific process of generating DEM data according to the three-dimensional model in the fifth step includes: and extracting characteristic points, lines and surface network interpolation according to the three-dimensional point cloud to generate the DEM.
6. A warehouse logistics management system, comprising:
the unmanned aerial vehicle carrying the camera is used for aerial photography and acquiring an acquired image;
a memory for storing a computer program for implementing the warehouse logistics management method of any one of claims 1-5;
a processor for executing the computer program in the memory;
and the display is used for displaying the real-scene three-dimensional map of the storage area obtained by the processor.
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