Material checking machine automatic identification and counting method based on computer vision

Abstract

The invention discloses an automatic identification and counting method for a material checking machine based on computer vision, and relates to the technical field of warehouse management, and the method comprises the steps: forming identification features of materials; obtaining a target standard image; forming a top view of the cargo pile slices, and calculating the volume of the cargo pile; the recognizable number of the target materials in the cargo pile is obtained through analysis; the volume of the cargo pile is divided into a visible volume and an invisible volume; forming at least one volume distribution scheme, and generating a verification coefficient of the volume distribution scheme; and obtaining the newly added quantity of the target materials, and obtaining the real-time quantity of the target materials. According to the method, the identification features of the materials are formed, the volume of the cargo pile is obtained, the identifiable number of the target materials is obtained, and the target volume distribution scheme is formed, so that the influence of shielding on feature identification can be eliminated, the conditions of the materials which are not completely shielded and completely shielded are respectively estimated, and the number of each material which is newly warehoused is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of warehouse management, in particular to an automatic identification and counting method for a material inventory machine based on computer vision. BACKGROUND

[0002] The material inventory machine refers to a device or system that uses automation technology and information technology to replace or assist manual counting, data recording and analysis of the inventory quantity of various materials in the warehouse. The existing inventory of various materials in the warehouse is known, and only the number of newly stored materials needs to be classified and counted. However, the newly stored materials are usually stacked together, and the mutual stacking of the materials will block the features used for identification, thereby affecting the counting. In addition, some materials are completely blocked inside, and it is difficult to count them using conventional methods. SUMMARY

[0003] To solve the above technical problems, the present application provides an automatic identification and counting method for a material inventory machine based on computer vision, which solves the problems raised in the background technology.

[0004] To achieve the above purpose, the technical scheme adopted by the present application is as follows: The automatic identification and counting method for a material inventory machine based on computer vision comprises: acquiring at least one material in the warehouse, obtaining the existing quantity of the material in the warehouse, and forming an identification feature of the material; performing image acquisition on the goods pile entering the warehouse to obtain at least one target image, performing standard processing on the target image to obtain a target standard image; segmenting at least one goods pile slice contour of the target standard image, forming a top view of the goods pile slice based on the goods pile slice contour, and calculating the volume of the goods pile; based on the identification feature, obtaining at least one target material existing in the goods pile, and analyzing to obtain the identifiable number of the target material in the goods pile; based on the identifiable number, dividing the volume of the goods pile into a visible volume and an invisible volume; based on the invisible volume, forming at least one volume allocation scheme, generating a verification coefficient of the volume allocation scheme, and taking the volume allocation scheme with the smallest verification coefficient as the target volume allocation scheme; based on the target volume allocation scheme, obtaining the new quantity of the target material, and superimposing the existing quantity and the new quantity of the target material to obtain the real-time quantity of the target material.

[0005] Preferably, the formation of the identification feature of the material comprises the following steps: uniformly segmenting the surface of the material into at least one local block, and acquiring an orthographic image of the local block; at least one sampling point is uniformly taken in the interval of (0, 10), and at least one collection point is uniformly taken in the interval of (0, 360); the front view image is scaled according to the value of the sampling point to obtain at least one first image, and the first image is rotated in the plane where the first image is located according to the value of the collection point to obtain at least one second image; the second images generated by the front view images of the local blocks are summarized to form a check set of the local blocks; one of the at least one material is taken as a characteristic material, and the remaining materials are taken as non-characteristic materials, and if the intersection of the check sets of the two local blocks is empty, the two local blocks are not associated; if the local block of the characteristic material is not associated with all the local blocks of the non-characteristic materials, the local block of the characteristic material is taken as a target local block, and the check set of the target local block of the characteristic material is taken to form an identification feature of the characteristic material; when the characteristic material traverses the at least one material, the identification features of all the materials are formed.

[0006] Preferably, the image collection of the cargo pile entering the warehouse to obtain at least one target image comprises the following steps: an arbitrary horizontal line is selected as a reference line, an arrow is arranged at one end of the reference line, a horizontal plane where the reference line is located is taken as a reference plane, and the reference line is rotated in the reference plane according to the value of the collection point to obtain at least one reference line; at least one collection direction is formed, and the collection direction is along the arrow pointing direction of the reference line; the image collection of the cargo pile is performed along the collection direction to obtain at least one target image.

[0007] Preferably, the standard processing of the target image to obtain a target standard image comprises the following steps: the cargo pile contour in the target image is identified, and the height of the cargo pile contour in the target image is taken as a reference size of the target image; the maximum value of the reference size is taken as a reference value, the reference value is divided by the reference size of the target image to obtain a scaling ratio of the target image, the target image is scaled according to the scaling ratio of the target image to obtain a target standard image, and the collection direction formed by the target image is matched to the target standard image formed by the target image.

[0008] Preferably, the segmentation of at least one cargo pile slice contour of the target standard image comprises the following steps: at least one horizontal parallel line is used to segment the cargo pile contour in the target standard image to obtain at least one cargo pile slice contour, wherein the distance between adjacent parallel lines is a preset distance, and the preset distance is set based on experience. At least one cargo stack slice outline of the target standard image is numbered from top to bottom. Cargo stack slice outlines with the same number correspond to the same cargo stack slice. The acquisition direction corresponding to the target standard image is matched to the cargo stack slice outline generated by the target standard image.

[0009] Preferably, the process of forming a top view of the cargo stack slice and calculating the volume of the cargo stack includes the following steps: The maximum length of the outline of the cargo stack slice is used as a reference value to form a preset area, which is a square with a side length equal to the reference value. At least one grid point is uniformly selected within a preset area to form at least one grid point pattern. The grid point pattern is formed by connecting grid points, and the vertices of the grid point pattern contain all possible combinations of grid points. Project the grid pattern along the acquisition direction to obtain the projection length of the grid pattern in the acquisition direction; The acquisition directions are arranged in ascending order of rotation angle at the time of formation to obtain an acquisition direction sequence. The projection lengths of the grid points in the acquisition directions are then arranged in the order of the acquisition direction sequence to obtain a projection length sequence. According to the order of the acquisition direction sequence, the lengths of the cargo stack slice outlines corresponding to the cargo stack slices are arranged to obtain the outline length sequence; The position with the smallest value in the projection length sequence is taken as the first position. The part of the projection length sequence before the first position is taken as the first front part. The part of the projection length sequence after the first position is taken as the first back part. The first position, the first back part, and the first front part are reassembled in order to obtain the projection correction sequence. The position with the smallest value in the contour length sequence is taken as the second position. The part of the contour length sequence before the second position is taken as the second front part. The part of the contour length sequence after the second position is taken as the second back part. The second position, the second back part and the second front part are re-sembedded in order to obtain the contour correction sequence. The absolute values ​​of the differences between the values ​​at the same position in the projection correction sequence and the contour correction sequence are accumulated to obtain the judgment value of the grid pattern. The grid pattern with the smallest judgment value is used as the top view of the cargo stack slice. The top view of the cargo stack slice is identified in the top view of the cargo stack slice. At least one test point is uniformly selected on the top view of the cargo stack slice. The coordinate model of the test point is performed. The test point is fitted to obtain the contour function of the top view of the cargo stack slice. The area enclosed by the contour function is integrated to obtain the area of ​​the top view of the cargo stack slice. The volume of the cargo stack slice is obtained by multiplying the area of ​​the top view contour of the cargo stack slice by a preset distance. The volumes of at least one cargo stack slice are summed to obtain the image volume of the cargo stack. The actual height of the cargo stack is divided by the reference value to obtain the side length coefficient. The cube of the side length coefficient is used as the volume coefficient. The volume of the cargo stack image is multiplied by the volume coefficient to obtain the volume of the cargo stack.

[0010] Preferably, obtaining at least one target material present in the cargo pile and analyzing the identifiable number of target materials in the cargo pile includes the following steps: Two target standard images are selected as reference images, and the acquisition directions corresponding to the two reference images are parallel. If one of the images in the verification set of the target local block of the material appears in the reference image, then the material is taken as the target material. The position of the image in the verification set of the target local block of the target material in the reference image is taken as the equivalent position of the target local block; The number of equivalent locations of the target local block is taken as the number of occurrences of the target local block, and the maximum number of occurrences of the target local block is taken as the number of identifiable targets in the cargo pile.

[0011] Preferably, dividing the volume of the cargo stack into visible and invisible volumes based on the identifiable number includes the following steps: Multiply the number of identifiable target materials in the cargo pile by the volume of the target materials to obtain the first volume of the target materials. Sum the first volumes to obtain the visible volume. Subtract the volume of the cargo pile from the visible volume to obtain the invisible volume.

[0012] Preferably, the process of forming at least one volume allocation scheme and generating verification coefficients for the volume allocation scheme includes the following steps: The invisible volume is uniformly divided into at least one local volume, and the local volume is randomly allocated to the target material. At least one allocation method forms at least one volume allocation scheme. The partial volumes allocated to the target material in the volume allocation scheme are accumulated to obtain the second volume of the target material. The second volume of the target material is divided by the volume of the target material to obtain the verification value of the target material. The integer closest to the verification value of the target material is taken as the feature value of the target material; The absolute values ​​of the differences between the characteristic values ​​and the verification values ​​of the target material are summed to obtain the verification coefficient of the volume distribution scheme.

[0013] Preferably, obtaining the additional quantity of the target material based on the target volume allocation scheme includes the following steps: The number of identifiable target materials in the cargo pile is superimposed with the characteristic value of the target materials in the target volume allocation scheme to obtain the new quantity of target materials.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: By forming identification features of materials, obtaining the volume of the cargo pile, determining the identifiable number of target materials, and formulating a target volume allocation scheme, the influence of occlusion on feature recognition can be eliminated. Then, materials that are not completely occluded are counted to form an identifiable number. At the same time, the cargo pile is reconstructed in three dimensions through multi-directional image acquisition, and the size difference between the image and the actual size is eliminated to obtain the volume of the cargo pile. Through volume calculation and verification, the situation of completely occluded materials is estimated, thereby obtaining the number of each type of newly entered material, thus completing the inventory update. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the automatic identification and counting method for material inventory machines based on computer vision according to the present invention. Figure 2 This is a schematic diagram of the process for forming the identification features of materials according to the present invention; Figure 3 This is a schematic diagram illustrating the process of acquiring images of a pile of goods entering a warehouse to obtain at least one target image according to the present invention. Figure 4 This is a schematic diagram of the process of performing standard processing on a target image to obtain a target standard image according to the present invention; Figure 5 This is a schematic diagram of the process of segmenting at least one cargo stack slice outline to form a target standard image according to the present invention; Figure 6 This is a top view of the process of forming a slice of the cargo stack according to the present invention, and a schematic diagram of the process of calculating the volume of the cargo stack. Figure 7 This is a schematic diagram illustrating the process of obtaining at least one target material present in a cargo pile and analyzing the number of identifiable target materials in the cargo pile according to the present invention. Figure 8 This is a schematic diagram illustrating the process of forming at least one volume allocation scheme and generating verification coefficients for the volume allocation scheme according to the present invention. Detailed Implementation

[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0017] Reference Figure 1As shown, the automatic identification and counting method for material inventory machines based on computer vision includes: Obtain at least one type of material in the warehouse, obtain the current quantity of the material in the warehouse, and form identification features for the material; Images of the goods entering the warehouse are captured to obtain at least one target image. The target image is then processed according to standards to obtain a target standard image. Segment at least one cargo stack slice outline to form a target standard image; based on the cargo stack slice outline, form a top view of the cargo stack slice; and calculate the volume of the cargo stack. Based on the identification features, at least one target material is found in the cargo pile, and the number of identifiable target materials in the cargo pile is analyzed. Based on the number of identifiable items, the volume of the cargo stack is divided into visible volume and invisible volume; Based on the invisible volume, at least one volume allocation scheme is formed, and a verification coefficient of the volume allocation scheme is generated. The volume allocation scheme with the smallest verification coefficient is taken as the target volume allocation scheme. Based on the target volume allocation scheme, the new quantity of target materials is obtained. The existing quantity of target materials is added to the new quantity to obtain the real-time quantity of target materials.

[0018] Newly received goods are stacked together, and some materials may be partially exposed. Because the stacking obscures certain features, different features will be counted differently. Furthermore, this solution only counts partially obscured or unobscured materials. It is difficult to count materials that are completely obscured. This situation is prone to occur when goods are stacked. Therefore, this solution includes a series of steps to address this issue.

[0019] Reference Figure 2 As shown, the steps to form the identification features of materials include: The surface of the material is evenly divided into at least one local block, and a front view image of the local block is obtained; At least one sampling point is uniformly selected in the interval (0, 10), and at least one sampling point is uniformly selected in the interval (0, 360). The front view image is scaled according to the values ​​of the sampling points to obtain at least one first image. The first image is rotated in the plane where the first image is located according to the values ​​of the sampling points to obtain at least one second image. The second images generated from the frontal view images of the local blocks are summarized to form a verification set of the local blocks; Take one of the at least one materials as the feature material and the remaining materials as the non-feature materials. If the intersection of the verification sets of two local blocks is empty, then the two local blocks are not associated. If the local block of the characteristic material is not associated with any local block of the non-characteristic material, then the local block of the characteristic material is taken as the target local block, and the verification set of the target local block of the characteristic material is used to form the identification feature of the characteristic material. When the characteristic material traverses at least one material, the identification characteristics of all materials are formed.

[0020] After scaling and rotation comparison, if one local block is still different from another local block, it can be said that the two local blocks are different; otherwise, they are the same. For this purpose, a verification set of local blocks is formed, which includes the possible scaling ratios and all rotation cases of the local blocks. Therefore, when determining whether two local blocks are the same, the comparison of their verification sets is sufficient. As long as the intersection of their verification sets is not empty, it means that the two are the same. When forming the identification features of materials, it is necessary to ensure that the features used to identify materials are unique, that is, they only appear in the material and do not appear in other materials.

[0021] Reference Figure 3 As shown, acquiring images of a pile of goods entering a warehouse to obtain at least one target image includes the following steps: Choose any horizontal line as the baseline, set an arrow at one end of the baseline, take the horizontal plane where the baseline is located as the reference plane, and rotate the baseline within the reference plane according to the values ​​of the collection points to obtain at least one reference line. At least one acquisition direction is formed, and the acquisition direction is along the direction of the arrow pointing on the reference line; Images of the cargo pile are acquired along the acquisition direction to obtain at least one target image.

[0022] The acquisition direction is obtained by rotating the baseline. Images of the cargo pile are acquired along the acquisition direction, which is equivalent to taking pictures around the cargo pile. Thus, the acquired target images can be used to create a 3D model of the cargo pile.

[0023] Reference Figure 4 As shown, the standard processing of the target image to obtain the target standard image includes the following steps: The outline of the cargo stack in the target image is identified, and the height of the cargo stack outline in the target image is used as the reference size of the target image. The maximum value of the reference size is used as the baseline value. The baseline value is divided by the reference size of the target image to obtain the scaling ratio of the target image. The target image is scaled according to the scaling ratio of the target image to obtain the target standard image. The acquisition direction of the target image is matched to the target standard image formed by the target image.

[0024] Because the distance to the cargo pile varies during shooting, the image size of the cargo pile outline in the target image is different. It is necessary to standardize the target images to make the image size of the cargo pile outline in the target images the same. Since the actual height of the cargo pile is constant when shooting around the cargo pile, each target image is scaled up so that the height of the cargo pile outline in the target standard image is equal to the reference value after scaling. In this way, the image size in the target standard image is consistent.

[0025] Reference Figure 5 As shown, segmenting at least one cargo stack slice outline to form a target standard image includes the following steps: The cargo stack outline in the target standard image is segmented using at least one horizontal parallel line to obtain at least one cargo stack slice outline, wherein the spacing between adjacent parallel lines is a preset distance, which is set based on experience. At least one cargo stack slice outline of the target standard image is numbered from top to bottom. Cargo stack slice outlines with the same number correspond to the same cargo stack slice. The acquisition direction corresponding to the target standard image is matched to the cargo stack slice outline generated by the target standard image.

[0026] It is easy to know that cargo stack slices with the same number are cargo stack slices at the same height. Therefore, the only difference between the cargo stack slices is the acquisition direction.

[0027] Reference Figure 6 As shown, forming a top view of a slice of the cargo stack and calculating the volume of the cargo stack includes the following steps: The maximum length of the outline of the cargo stack slice is used as a reference value to form a preset area, which is a square with a side length equal to the reference value. At least one grid point is uniformly selected within a preset area to form at least one grid point pattern. The grid point pattern is formed by connecting grid points, and the vertices of the grid point pattern contain all possible combinations of grid points. Project the grid pattern along the acquisition direction to obtain the projection length of the grid pattern in the acquisition direction; The acquisition directions are arranged in ascending order of rotation angle at the time of formation to obtain an acquisition direction sequence. The projection lengths of the grid points in the acquisition directions are then arranged in the order of the acquisition direction sequence to obtain a projection length sequence. According to the order of the acquisition direction sequence, the lengths of the cargo stack slice outlines corresponding to the cargo stack slices are arranged to obtain the outline length sequence; The position with the smallest value in the projection length sequence is taken as the first position. The part of the projection length sequence before the first position is taken as the first front part. The part of the projection length sequence after the first position is taken as the first back part. The first position, the first back part, and the first front part are reassembled in order to obtain the projection correction sequence. The position with the smallest value in the contour length sequence is taken as the second position. The part of the contour length sequence before the second position is taken as the second front part. The part of the contour length sequence after the second position is taken as the second back part. The second position, the second back part and the second front part are re-sembedded in order to obtain the contour correction sequence. The absolute values ​​of the differences between the values ​​at the same position in the projection correction sequence and the contour correction sequence are accumulated to obtain the judgment value of the grid pattern. The grid pattern with the smallest judgment value is used as the top view of the cargo stack slice. The top view of the cargo stack slice is identified in the top view of the cargo stack slice. At least one test point is uniformly selected on the top view of the cargo stack slice. The coordinate model of the test point is performed. The test point is fitted to obtain the contour function of the top view of the cargo stack slice. The area enclosed by the contour function is integrated to obtain the area of ​​the top view of the cargo stack slice. The volume of the cargo stack slice is obtained by multiplying the area of ​​the top view contour of the cargo stack slice by a preset distance. The volumes of at least one cargo stack slice are summed to obtain the image volume of the cargo stack. The actual height of the cargo stack is divided by the reference value to obtain the side length coefficient. The cube of the side length coefficient is used as the volume coefficient. The volume of the cargo stack image is multiplied by the volume coefficient to obtain the volume of the cargo stack.

[0028] The image size of the top view of the cargo stack slice is the same as the image size of the target standard image. Therefore, if the top view of the cargo stack slice is projected in the acquisition direction, the length of the projection is the same as the length of the cargo stack slice outline in the target standard image acquired in the same acquisition direction. Therefore, this principle can be used to select the grid point pattern to obtain the top view of the cargo stack slice. Each cargo stack slice outline corresponds to a collection direction. Therefore, the lengths of the cargo stack slice outlines corresponding to the cargo stack slices can be arranged according to their corresponding collection directions and in the order of the collection direction sequence to obtain the outline length sequence. When comparing them here, it is assumed that the grid pattern is the top view of the cargo stack slice. However, since there may be a difference in the rotation angle between the grid pattern and the cargo stack slice, the sequence of its projected lengths in the acquisition direction cannot be compared with the contour length sequence. It is necessary to rotate it until there is no difference with the cargo stack slice before comparison can be performed. Therefore, after forming the projection length sequence and the contour length sequence, the two sequences are processed so that the position with the smallest length in the sequence is placed at the beginning of the sequence, and the remaining parts are changed in order. This is equivalent to adjusting the projection length sequence and the contour length sequence to be in the same state, that is, there is no rotation difference between the two. If the grid pattern is the top view of the cargo stack slice, the difference of the adjusted sequence is the smallest, that is, the judgment value is the smallest. Therefore, the required grid pattern can be selected as the top view of the cargo stack slice. However, the image size of the top view of the cargo stack slice is consistent with the image size of the target standard image. Therefore, the ratio of the image volume of the cargo stack to the actual volume of the cargo stack is equal to the cube of the ratio of its side lengths. Based on this, the actual volume of the cargo stack can be calculated.

[0029] Reference Figure 7 As shown, obtaining at least one target material present in the cargo pile and analyzing the identifiable number of target materials in the cargo pile includes the following steps: Two target standard images are selected as reference images, and the acquisition directions corresponding to the two reference images are parallel. If one of the images in the verification set of the target local block of the material appears in the reference image, then the material is taken as the target material. The position of the image in the verification set of the target local block of the target material in the reference image is taken as the equivalent position of the target local block; The number of equivalent locations of the target local block is taken as the number of occurrences of the target local block, and the maximum number of occurrences of the target local block is taken as the number of identifiable targets in the cargo pile.

[0030] For materials that are not completely obscured or are not obscured, the target partial block of the material may or may not appear because it may be obscured. As a result, the count of different target partial blocks of the material will be different. Therefore, by taking the maximum number of occurrences of the target partial block as the number of identifiable target materials in the cargo pile, we can count materials that are not completely obscured or are not obscured.

[0031] Based on the identifiable number of items, the volume of the cargo stack is divided into visible volume and invisible volume, including the following steps: Multiply the number of identifiable target materials in the cargo pile by the volume of the target materials to obtain the first volume of the target materials. Sum the first volumes to obtain the visible volume. Subtract the volume of the cargo pile from the visible volume to obtain the invisible volume.

[0032] When counting, it is also necessary to count materials that are completely obscured, as this cannot be done through feature recognition.

[0033] Reference Figure 8 As shown, forming at least one volume allocation scheme and generating verification coefficients for the volume allocation scheme includes the following steps: The invisible volume is uniformly divided into at least one local volume, and the local volume is randomly allocated to the target material. At least one allocation method forms at least one volume allocation scheme. The partial volumes allocated to the target material in the volume allocation scheme are accumulated to obtain the second volume of the target material. The second volume of the target material is divided by the volume of the target material to obtain the verification value of the target material. The integer closest to the verification value of the target material is taken as the feature value of the target material; The absolute values ​​of the differences between the characteristic values ​​and the verification values ​​of the target material are summed to obtain the verification coefficient of the volume distribution scheme.

[0034] The invisible volume is allocated to resources. Therefore, if the invisible volume is perfectly accurate, the volume allocated to resources must be an integer multiple of the resource's own volume. However, since the acquisition of the invisible volume is not perfectly accurate, the volume allocated to resources must be closest to an integer multiple of the resource's own volume. Therefore, this is used as a basis to determine the scheme for allocating the invisible volume to resources, and then, based on the allocation of volume, the number of resources that are completely obscured is determined.

[0035] Based on the target volume allocation scheme, the additional quantity of target materials is obtained through the following steps: The number of identifiable target materials in the cargo pile is superimposed with the characteristic value of the target materials in the target volume allocation scheme to obtain the new quantity of target materials.

[0036] Furthermore, this solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is invoked, the aforementioned computer vision-based automatic identification and counting method for material inventory machines is executed.

[0037] It is understandable that the storage medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid-state drive (SSD).

[0038] In summary, the advantages of this invention are as follows: by forming identification features of materials, obtaining the volume of the goods pile, obtaining the identifiable number of target materials, and forming a target volume allocation scheme, the influence of occlusion on feature recognition can be eliminated. Furthermore, materials that are not completely occluded are counted to form an identifiable number. At the same time, by acquiring images from multiple angles, the goods pile is reconstructed in three dimensions, and the size difference between the image and the actual size is eliminated, thereby obtaining the volume of the goods pile. Through volume calculation and verification, the situation of completely occluded materials is estimated, thereby obtaining the number of each type of newly entered material, thus completing the inventory update.

[0039] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A computer vision-based machine automatic identification counting method for inventory, characterized in that, The application relates to a warehouse material quantity acquisition method and device. The method comprises the following steps: acquiring at least one material in a warehouse, acquiring an existing quantity of the material in the warehouse, and forming an identification feature of the material; carrying out image acquisition on a cargo stack entering the warehouse to obtain at least one target image, and carrying out standard processing on the target image to obtain a target standard image; segmenting at least one cargo stack slice contour of the target standard image, forming a top view of the cargo stack slice based on the cargo stack slice contour, and calculating the volume of the cargo stack; based on the identification feature, obtaining at least one target material existing in the cargo stack, and analyzing to obtain an identifiable number of the target material in the cargo stack; based on the identifiable number, dividing the volume of the cargo stack into a visible volume and an invisible volume; based on the invisible volume, forming at least one volume allocation scheme, generating a check coefficient of the volume allocation scheme, and taking the volume allocation scheme with the minimum check coefficient as a target volume allocation scheme; 2. The computer vision-based machine automated identification counting method of material inventory according to claim 1, wherein, based on the target volume allocation scheme, obtaining an added quantity of the target material, and superimposing the existing quantity and the added quantity of the target material to obtain a real-time quantity of the target material. The method comprises the following steps: uniformly segmenting a surface of the material into at least one local block, and acquiring an orthographic image of the local block; uniformly taking at least one sampling point in the (0, 10) interval and at least one collection point in the (0, 360) interval; scaling the orthographic image according to the value of the sampling point to obtain at least one first image, and rotating the first image according to the value of the collection point in the plane where the first image is located to obtain at least one second image; collecting the second images generated by the orthographic images of the local blocks to form a check set of the local blocks; taking one of the at least one material as a characteristic material, taking the remaining materials as non-characteristic materials, and if the intersection of the check sets of two local blocks is empty, the two local blocks are not associated; if the local block of the characteristic material is not associated with the local blocks of all non-characteristic materials, taking the local block of the characteristic material as a target local block, and forming an identification feature of the characteristic material by using the check set of the target local block of the characteristic material; 3. The computer vision-based machine automated identification counting method of claim 2, wherein, when the characteristic material traverses the at least one material, forming the identification features of all the materials. The method comprises the following steps: selecting an arbitrary horizontal line as a reference line, setting an arrow at one end of the reference line, taking a horizontal plane where the reference line is located as a reference plane, and rotating the reference line in the reference plane according to the value of the collection point to obtain at least one reference line; forming at least one collection direction, and the collection direction is directed along the arrow direction of the reference line; 4. The computer vision-based machine automated identification counting method of material inventory according to claim 3, wherein, carrying out image acquisition on the cargo stack along the collection direction to obtain at least one target image. The method comprises the following steps: identifying the cargo stack contour in the target image, taking the height of the cargo stack contour in the target image as a reference size of the target image; The maximum value of the reference size is taken as a reference value, the reference value is divided by the reference size of the target image to obtain a scaling ratio of the target image, the target image is scaled according to the scaling ratio of the target image to obtain a target standard image, and the collection direction for forming the target image is matched to the target standard image formed by the target image.

5. The computer vision-based machine automated identification counting method of material inventory according to claim 4, wherein, The step of segmenting the at least one cargo pile slice contour to form the target standard image comprises the following steps: The cargo pile contour in the target standard image is segmented using at least one horizontal parallel line to obtain at least one cargo pile slice contour, wherein the distance between adjacent parallel lines is a preset distance, and the preset distance is set based on experience; The at least one cargo pile slice contour of the target standard image is numbered from top to bottom, the cargo pile slice contours with the same number correspond to the same cargo pile slice, and the collection direction corresponding to the target standard image is matched to the cargo pile slice contour generated by the target standard image.

6. The computer vision-based machine automated identification counting method of material inventory according to claim 5, wherein, The step of forming the top view of the cargo pile slice and calculating the volume of the cargo pile comprises the following steps: The maximum value of the length of the cargo pile slice contour is taken as a reference value, a preset region is formed, and the preset region is a square block with a side length equal to the reference value; At least one grid point is uniformly taken in the preset region to form at least one grid point pattern, the grid point pattern is formed by connecting the grid points, and the vertices of the grid point pattern include all combinations of the grid points; The grid point pattern is projected along the collection direction to obtain the projection length of the grid point pattern in the collection direction; The collection directions are arranged in ascending order according to the rotation angles when the collection directions are formed to obtain a collection direction sequence, the projection lengths of the grid point pattern in the collection directions are arranged in the order of the collection direction sequence to obtain a projection length sequence, and the length of the cargo pile slice contour corresponding to the cargo pile slice is arranged in the order of the collection direction sequence to obtain a contour length sequence; The position with the minimum value in the projection length sequence is taken as a first position, the part before the first position in the projection length sequence is taken as a first front part, the part after the first position in the projection length sequence is taken as a first rear part, the first position, the first rear part and the first front part are reconnected in order to obtain a projection correction sequence; The position with the minimum value in the contour length sequence is taken as a second position, the part before the second position in the contour length sequence is taken as a second front part, the part after the second position in the contour length sequence is taken as a second rear part, the second position, the second rear part and the second front part are reconnected in order to obtain a contour correction sequence; The absolute values of the differences between the values of the same positions in the projection correction sequence and the contour correction sequence are added to obtain a judgment value of the grid point pattern, and the grid point pattern with the minimum judgment value is taken as the top view of the cargo pile slice; The top view contour of the cargo pile slice is recognized in the top view of the cargo pile slice, at least one test point is uniformly taken on the top view contour of the cargo pile slice, the test points are subjected to coordinate modeling, the test points are fitted to obtain a contour function of the top view contour of the cargo pile slice, and the area surrounded by the contour function is integrated to obtain the area of the top view contour of the cargo pile slice. ​ The area of the top view profile of the cargo stack slice is multiplied by the preset distance to obtain the volume of the cargo stack slice, and the volumes of the at least one cargo stack slice are accumulated to obtain the image volume of the cargo stack; The actual height of the cargo stack is divided by the reference value to obtain a side length coefficient, the cube of the side length coefficient is taken as a volume coefficient, and the image volume of the cargo stack is multiplied by the volume coefficient to obtain the volume of the cargo stack.

7. The computer vision-based machine-automated identification counting method of inventorying assets of claim 6, wherein, The obtaining of the at least one target resource in the cargo stack and the analysis of the identifiable number of the target resource in the cargo stack include the following steps: Two target standard images are selected as reference images, and the collection directions corresponding to the two reference images are parallel; If one of the images in the check set of the target local block of the resource appears in the reference image, the resource is taken as the target resource; The position of the image in the check set of the target local block of the target resource appearing in the reference image is taken as the equivalent position of the target local block; The number of the equivalent positions of the target local block is taken as the occurrence number of the target local block, and the maximum value of the occurrence number of the target local block is taken as the identifiable number of the target resource in the cargo stack.

8. The computer vision-based machine-automated identification counting method of inventorying assets of claim 7, wherein, The dividing of the volume of the cargo stack into a visible volume and an invisible volume based on the identifiable number includes the following steps: The identifiable number of the target resource in the cargo stack is multiplied by the volume of the target resource to obtain a first volume of the target resource, the first volume is accumulated to obtain the visible volume, and the volume of the cargo stack is subtracted from the visible volume to obtain the invisible volume.

9. The computer vision-based machine-automated identification counting method of inventorying assets of claim 8, wherein, The forming of at least one volume allocation scheme and the generating of a check coefficient of the volume allocation scheme include the following steps: The invisible volume is uniformly divided into at least one local volume, and the local volume is randomly allocated to the target resource, at least one allocation manner forms at least one volume allocation scheme; The local volume allocated to the target resource in the volume allocation scheme is accumulated to obtain a second volume of the target resource, and the second volume of the target resource is divided by the volume of the target resource to obtain a check value of the target resource; The nearest integer to the check value of the target resource is taken as a characteristic value of the target resource; The absolute values of the differences between the characteristic values of the target resources and the check values of the target resources are accumulated to obtain a check coefficient of the volume allocation scheme.

10. The computer vision-based machine automated identification counting method of material inventory according to claim 9, wherein, The obtaining of the new number of the target resource based on the target volume allocation scheme includes the following steps: The identifiable number of the target resource in the cargo stack is superimposed with the characteristic value of the target resource in the target volume allocation scheme to obtain the new number of the target resource.

Images