CN113128923A - Storage position recommendation method and device - Google Patents
Storage position recommendation method and device Download PDFInfo
- Publication number
- CN113128923A CN113128923A CN202010042783.5A CN202010042783A CN113128923A CN 113128923 A CN113128923 A CN 113128923A CN 202010042783 A CN202010042783 A CN 202010042783A CN 113128923 A CN113128923 A CN 113128923A
- Authority
- CN
- China
- Prior art keywords
- article
- stored
- target
- target article
- storage position
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000004364 calculation method Methods 0.000 claims abstract description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 71
- 238000001514 detection method Methods 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 8
- 230000009466 transformation Effects 0.000 claims description 5
- 230000008447 perception Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 12
- 238000010586 diagram Methods 0.000 description 15
- 238000004891 communication Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 239000003086 colorant Substances 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000007474 system interaction Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
Abstract
The invention discloses a storage position recommendation method and device, and relates to the technical field of computers. One specific implementation mode of the method comprises the steps of obtaining information of currently stored articles on storage positions according to a plurality of storage position information which can be stored in target articles; based on the target article information and the stored article information, inquiring whether corresponding similarity exists, if so, positioning storage position information corresponding to the stored article which is not similar to the target article, and then recommending the storage position to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article. Therefore, the method and the device can solve the problem of poor recommendation accuracy in the existing warehousing recommendation storage process.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a storage place recommendation method and device.
Background
In the transfer robot bin, the picking process is as follows: the carrying robot carries the goods shelf to the delivery work station, stops, waits for the staff to pick out the goods to be delivered, and the staff appoints the BIN (the smallest storage unit of the goods shelf) to search for the goods according to the basic attribute and the position information of the goods, such as what name and color, and the specific position and the like, provided by the operation interface for picking the goods, then scans the goods, checks and completes the whole goods picking action. Because the BIN grids of the goods shelf can hold at least 3-4 kinds of articles, the number of the specific articles depends on the volume of the articles, and the articles with smaller volume, such as socks, underwear and the like, can be stored in a larger number. For the articles with similar outer packages, if the underwear and socks with different numbers and the same package are used, the articles can be accurately found most of the time by means of naked eyes, personal experience or multiple scanning and checking, so that the picking time is increased to a certain extent, the system interaction is increased, the overall delivery efficiency is reduced, and the articles are subject to the problem of being subject to the scaling by a plurality of warehouse workers.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the existing warehousing recommended storage position flow refers to the volume, the popularity, the ranking of storage positions and the like of articles, and does not consider whether the appearances are similar or not, so that similar articles cannot be placed in the same BIN grid, and the phenomenon is more obvious for warehouses with thousands of articles. And then additionally increased the time of choosing goods, the efficiency of choosing goods reduces, can cause certain cost loss.
Disclosure of Invention
In view of this, embodiments of the present invention provide a storage location recommendation method and apparatus, which can solve the problem of poor recommendation accuracy in the existing storage location recommendation process.
In order to achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a storage space recommendation method, including obtaining information of an item currently stored on a storage space according to a plurality of storage space information that a target item can store; based on the target article information and the stored article information, inquiring whether corresponding similarity exists, if so, positioning storage position information corresponding to the stored article which is not similar to the target article, and then recommending the storage position to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article.
Optionally, each of the storage locations is located on a different storage device, or a part of the storage locations are located on different storage devices.
Optionally, comprising:
the method comprises the steps of obtaining image information of a target article, and obtaining storage position information corresponding to the stored article which is dissimilar to the target article by combining a scale invariant feature transformation feature detection algorithm, a perception hash algorithm and a color histogram algorithm based on the stored article image.
Optionally, obtaining, by combining a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm, and a color histogram algorithm, storage location information corresponding to a stored article that is dissimilar to the target article, includes:
according to the image information of the target article and the image information of the stored article, respectively calculating a first matching value, a second matching value and a third matching value through a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm;
judging whether the first matching value is smaller than or equal to a first lowest threshold value or not, if so, judging whether the second matching value is larger than a second highest threshold value or not and whether the third matching value is larger than a third highest threshold value or not, if so, the stored article is similar to the target article, otherwise, the stored article is not similar to the target article;
if not, judging whether the first matching value is smaller than or equal to a first highest threshold value, if so, judging whether the second matching value is larger than a second highest threshold value or whether a third matching value is larger than a third highest threshold value so as to determine whether the stored articles are similar to or dissimilar to the target articles; if not, whether the second matching value is larger than the second lowest threshold value and whether the third matching value is larger than the third lowest threshold value is judged so as to determine whether the stored article is similar to or dissimilar to the target article.
Optionally, the method further comprises:
the similarity is a character string generated by adopting 0 and 1, and the character string is compressed; where 0 indicates that the target item image is not similar to the other item image, and 1 indicates that the target item image is similar to the other item image.
In addition, the invention also provides a storage place recommendation device which comprises an acquisition module, a storage place recommendation module and a storage place recommendation module, wherein the acquisition module is used for acquiring the information of the currently stored articles on the storage places according to the information of a plurality of storage places which can be stored by the target articles; the recommendation module is used for inquiring whether corresponding similarity exists or not based on the target article information and the stored article information, if so, positioning storage position information corresponding to the stored article which is not similar to the target article, and then recommending the storage position to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article.
Optionally, the recommending module is further configured to:
the method comprises the steps of obtaining image information of a target article, and obtaining storage position information corresponding to the stored article which is dissimilar to the target article by combining a scale invariant feature transformation feature detection algorithm, a perception hash algorithm and a color histogram algorithm based on the stored article image.
Optionally, the recommending module obtains, by combining with a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm, and a color histogram algorithm, storage location information corresponding to a stored article that is dissimilar to the target article, including:
according to the image information of the target article and the image information of the stored article, respectively calculating a first matching value, a second matching value and a third matching value through a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm;
judging whether the first matching value is smaller than or equal to a first lowest threshold value or not, if so, judging whether the second matching value is larger than a second highest threshold value or not and whether the third matching value is larger than a third highest threshold value or not, if so, the stored article is similar to the target article, otherwise, the stored article is not similar to the target article;
if not, judging whether the first matching value is smaller than or equal to a first highest threshold value, if so, judging whether the second matching value is larger than a second highest threshold value or whether a third matching value is larger than a third highest threshold value so as to determine whether the stored articles are similar to or dissimilar to the target articles; if not, whether the second matching value is larger than the second lowest threshold value and whether the third matching value is larger than the third lowest threshold value is judged so as to determine whether the stored article is similar to or dissimilar to the target article.
Optionally, the method further comprises:
the similarity is a character string generated by adopting 0 and 1, and the character string is compressed; where 0 indicates that the target item image is not similar to the other item image, and 1 indicates that the target item image is similar to the other item image.
One embodiment of the above invention has the following advantages or benefits: because a plurality of storage position information which can be stored according to the target object are adopted, the information of the object which is stored at the storage position currently is obtained; based on the target article information and the stored article information, inquiring whether corresponding similarity exists, if so, positioning storage position information corresponding to the stored article which is not similar to the target article, and then recommending the storage position to store the target article; if the storage position recommendation method does not exist, the image information of the target object is obtained, the storage position information corresponding to the stored object which is dissimilar to the target object is obtained through calculation by using a preset similarity calculation method based on the stored object image, and the technical means of storing the target object in the storage position is recommended, so that the technical problem of poor recommendation precision in the existing warehousing recommendation storage position process is solved, the similar objects cannot be recommended to one storage position, and the technical effect of increasing the identifiability of the objects on the same storage position is achieved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of a main flow of a stock allocation recommendation method according to a first embodiment of the invention;
FIG. 2 is a schematic diagram illustrating a main flow of a bin recommendation method according to a second embodiment of the present invention;
FIG. 3 is a diagram illustrating a main flow of a bin recommendation method according to a third embodiment of the present invention;
FIG. 4 is a schematic diagram of the main modules of a stock recommendation device according to an embodiment of the invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a stock allocation recommendation method according to a first embodiment of the present invention, as shown in fig. 1, the stock allocation recommendation method includes:
step S101, according to a plurality of storage position information which can be stored by the target object, the object information which is stored at the current storage position is obtained.
Step S102, whether corresponding similarity exists is inquired based on the target article information and the stored article information, if yes, storage position information corresponding to the stored article which is not similar to the target article is located, and then the storage position is recommended to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article.
In a preferred embodiment, the similarity is a character string generated by using 0 and 1, and the character string is compressed. Where 0 indicates that the target item image is not similar to the other item image, and 1 indicates that the target item image is similar to the other item image.
In some embodiments, image information of a target article is obtained, and storage position information corresponding to the stored article dissimilar to the target article is obtained based on the stored article image by combining a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm.
The scale invariant feature transform feature detection algorithm is also called as SIFT feature detection algorithm, and is a machine vision algorithm for detecting and describing local features in an image. The method has strong robustness to brightness change, rotation and scale change of the picture, and has good expression on matching speed and accuracy. The SIFT feature detection algorithm is processed on the basis of a gray scale image, and packages with the same appearance and different colors can be omitted by simply adopting the SIFT feature detection algorithm, so that the method is combined with a color histogram and is additionally provided with a perceptual hash algorithm for auxiliary verification.
Perceptual hash algorithms (PHA for short) are a class of hash algorithms, are mainly used for searching similar pictures, are 64-bit character strings and represent fingerprint information of images, and the similarity between pictures is the hamming distance for comparing the character strings between the pictures, wherein the larger the hamming distance is, the more different points are, the smaller the similarity of the images is, and the larger the similarity is otherwise.
The color histogram algorithm is to separate RGB three channels after the image is zoomed, compare the similarity of each channel, the value range of the single channel histogram is 0-1, the larger the value is, the higher the similarity is.
As a preferred embodiment, the obtaining, by combining a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm, and a color histogram algorithm, the storage information corresponding to the stored article that is dissimilar to the target article includes:
and calculating to obtain a corresponding first matching value, a second matching value and a third matching value respectively through a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm according to the image information of the target article and the image information of the stored article.
And judging whether the first matching value is smaller than or equal to a first lowest threshold value or not, if so, judging whether the second matching value is larger than a second highest threshold value or not and whether the third matching value is larger than a third highest threshold value or not, if so, the stored article is similar to the target article, otherwise, the stored article is not similar to the target article.
If not, judging whether the first matching value is smaller than or equal to a first highest threshold value, if so, judging whether the second matching value is larger than a second highest threshold value or whether a third matching value is larger than a third highest threshold value so as to determine whether the stored articles are similar to or dissimilar to the target articles; if not, whether the second matching value is larger than the second lowest threshold value and whether the third matching value is larger than the third lowest threshold value is judged so as to determine whether the stored article is similar to or dissimilar to the target article.
In other embodiments, the image of the target object may be acquired through an acquisition application APP of the mobile terminal. Further, the embodiment adopts the collection application program APP of the handheld mobile terminal, and has the advantages of convenience, low cost, simple operation and no need of using collection equipment such as an industrial camera with high price.
For example: the acquisition application program APP has the functions of scanning bar codes for identifying articles, photographing and uploading pictures and the like. Scanning a bar code identifying an article can query basic information of the article, such as a unique identification code of the article, an article name and the like. The shooting and uploading can upload the outer package pictures of the articles, the outer packages of the articles have longer timeliness, one SKU (short for article) corresponds to one package, the SKU is unique in the same warehouse, and the SKUs in different batches are the same and can be used for a long time only by being collected once.
It should be noted that two schemes are stored in the similar result, one scheme is to store only the feature vectors such as the SIFT feature vector, the color histogram matrix and the perceptual hash value, and calculate the similarity between the articles to be shelved in real time when the articles are put in storage and shelved. That is, in step S102, the image information of the target item is acquired, and the slot information corresponding to the stored item that is not similar to the target item is calculated by using the preset similarity calculation method based on the stored item image, so as to recommend the slot to store the target item.
Further, in order to meet the requirements of computing power and real-time performance, a part of articles can be screened before computation. The specific implementation process comprises the following steps:
the recommended storage positions can be scored, the scoring strategy can refer to the distance cost of the storage positions, the farther the distance cost is, the lower the score is, meanwhile, the congestion cost of the roadway where the storage positions are located is, the lower the congestion score is, finally, the storage position ranking is obtained based on the information, the storage position ranking with the first ranking is selected, whether stored articles similar to the target articles exist or not is determined, if yes, the storage position ranking with the second ranking can be selected, and the like.
The other scheme is that the similarity between every two articles is calculated in advance, n x (n-1)/2 times are required to be calculated, n is the number of SKU types, and the result can be quickly inquired when the real-time calculation is carried out in warehousing. That is, if there is a similarity in step S102, the slot information corresponding to the stored item that is not similar to the target item is located, and the target item is recommended to be stored in the slot.
In the similarity table, 0 and 1 are used to represent dissimilarity and similarity, respectively, so that a string consisting of 0 and 1 can be used to represent the similarity relationship of one SKU to other SKUs, and the string can be compressed (many compression algorithms, such as compressing by number, 10416 if 100001000000), and finally stored directly in the database (for example, MYSQL) as a field (preferably, BLOB data type selected by the present invention). When the character string is inquired, the result can be quickly located, and the similarity relation between each SKU and other SKUs is recorded by rows by adopting a database-dividing and table-dividing strategy under the condition that the database resources are rich. For example: the similarity condition of the article A and other articles is inquired, the similar character string of the A which is decompressed is taken out to be 1010000001, the corresponding serial number of the article B to be compared is obtained at this time, the serial number inquires the database, for example, if the serial number is 4, then the 4 th character string 1010000001 is found to be 0, namely, the similar character strings are not obtained.
In summary, the storage place recommendation method provided by the invention can prevent similar articles from being in the same storage place, and increases the identifiability of the articles on the storage place. Thereby reducing the goods picking time to a certain extent and increasing the goods picking efficiency. In addition, the method and the device are combined with three similarity algorithms to identify the images, so that the accuracy of recommending the storage positions can be improved. Meanwhile, the image acquisition cost is reduced by using the terminal application program App.
Fig. 2 is a schematic diagram of a main flow of a stock allocation recommendation method according to a second embodiment of the present invention, where the stock allocation recommendation method may include:
step S201, according to a plurality of storage position information which can be stored by the target object, the object information which is stored at the current storage position is obtained.
Step S202, based on the target article information and the stored article information, inquiring whether corresponding similarity exists, if so, performing step S203, and if not, performing step S204.
Step S203, positioning the storage position information corresponding to the stored article which is dissimilar to the target article, and then recommending the storage position to store the target article.
And step S204, acquiring image information of the target article, and obtaining storage position information corresponding to the stored article which is dissimilar to the target article by combining a scale invariant feature transformation feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm based on the stored article image so as to recommend the storage position to store the target article.
Fig. 3 is a schematic diagram of a main flow of a stock allocation recommendation method according to a third embodiment of the present invention, where the stock allocation recommendation method may include:
step S301, according to a plurality of storage position information which can be stored by the target object, the object information which is currently stored on the storage position is obtained.
Step S302, based on the target item information and the stored item information, querying whether there is a corresponding similarity, if yes, performing step S303, otherwise, performing step S304.
Step S303, locating storage location information corresponding to an already stored article that is dissimilar to the target article, and then recommending the storage location to store the target article, and exiting the process.
Step S304, according to the image information of the target article and the image information of the stored article, respectively calculating a first matching value, a second matching value and a third matching value through a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm, and performing step S305.
Step S305, determining whether the first matching value is less than or equal to the first lowest threshold, if so, performing step S306, otherwise, performing step S307.
Step S306, according to whether the second matching value is larger than the second highest threshold value and whether the third matching value is larger than the third highest threshold value, whether the stored article is similar to or dissimilar to the target article is determined.
The specific implementation process comprises the following steps:
the method comprises the following steps: and judging whether the second matching value is greater than a second highest threshold value and whether the third matching value is greater than a third highest threshold value, if so, performing the second step, and otherwise, performing the third step.
Step two: the deposited item is similar to the target item.
Step three: the deposited item is dissimilar from the target item.
Step S307, according to whether the first matching value is smaller than or equal to the first highest threshold value, whether the stored article is similar to or dissimilar to the target article is determined. The specific implementation process comprises the following steps:
the method comprises the following steps: and judging whether the first matching value is smaller than or equal to a first highest threshold value, if so, performing the second step, and otherwise, performing the third step.
Step two: and judging whether the second matching value is greater than a second highest threshold or whether the third matching value is greater than a third highest threshold, if so, performing the fourth step, and otherwise, performing the fifth step.
Step three: and judging whether the second matching value is greater than a second lowest threshold value and whether the third matching value is greater than a third lowest threshold value, if so, performing the fourth step, and otherwise, performing the fifth step.
Step four: the deposited item is similar to the target item.
Step five: the deposited item is dissimilar from the target item.
Fig. 4 is a schematic diagram of main modules of a stock position recommending apparatus according to an embodiment of the present invention, and as shown in fig. 4, the stock position recommending apparatus 400 includes an obtaining module 401 and a recommending module 402. The obtaining module 401 obtains information of an article currently stored in a storage position according to information of a plurality of storage positions where a target article can be stored; the recommending module 402 inquires whether corresponding similarity exists or not based on the target article information and the stored article information, and if so, locates storage location information corresponding to the stored article which is dissimilar to the target article, and then recommends the storage location to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article.
In some embodiments, the recommending module 402 may obtain image information of the target article, and obtain, based on the stored article image, storage location information corresponding to the stored article that is dissimilar to the target article by using a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm, and a color histogram algorithm.
As a further embodiment, the recommending module 402, in combination with the scale invariant feature transform feature detection algorithm, the perceptual hash algorithm, and the color histogram algorithm, obtains the storage location information corresponding to the stored article that is dissimilar to the target article, including:
according to the image information of the target article and the image information of the stored article, respectively calculating a first matching value, a second matching value and a third matching value through a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm;
judging whether the first matching value is smaller than or equal to a first lowest threshold value or not, if so, judging whether the second matching value is larger than a second highest threshold value or not and whether the third matching value is larger than a third highest threshold value or not, if so, the stored article is similar to the target article, otherwise, the stored article is not similar to the target article;
if not, judging whether the first matching value is smaller than or equal to a first highest threshold value, if so, judging whether the second matching value is larger than a second highest threshold value or whether a third matching value is larger than a third highest threshold value so as to determine whether the stored articles are similar to or dissimilar to the target articles; if not, whether the second matching value is larger than the second lowest threshold value and whether the third matching value is larger than the third lowest threshold value is judged so as to determine whether the stored article is similar to or dissimilar to the target article.
It should be further noted that the similarity is a character string generated by using 0 and 1, and the character string is compressed; where 0 indicates that the target item image is not similar to the other item image, and 1 indicates that the target item image is similar to the other item image.
It should be noted that the stock level recommendation method and the stock level recommendation apparatus of the present invention have a corresponding relationship in the embodied content, so the repeated content is not described again.
Fig. 5 shows an exemplary system architecture 500 to which the bin recommendation method or the bin recommendation apparatus according to the embodiments of the present invention can be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various electronic devices having a stock recommendation screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 501, 502, 503. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the stock allocation recommendation method provided by the embodiment of the present invention is generally executed by the server 505, and accordingly, the computing device is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output section 607 including a display such as a Cathode Ray Tube (CRT), a liquid crystal storage recommender (LCD), and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module and a recommendation module. Wherein the names of the modules do not in some cases constitute a limitation of the module itself.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs, and when the one or more programs are executed by the equipment, the equipment comprises a plurality of storage position information which can be stored in a target item, and the information of the currently stored item on the storage position is acquired; based on the target article information and the stored article information, inquiring whether corresponding similarity exists, if so, positioning storage position information corresponding to the stored article which is not similar to the target article, and then recommending the storage position to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article.
According to the technical scheme of the embodiment of the invention, the problem of poor recommendation accuracy in the conventional warehousing recommendation storage process can be solved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A stock allocation recommendation method is characterized by comprising the following steps:
according to a plurality of storage position information which can be stored in a target object, acquiring the information of the object which is stored at the storage position currently;
based on the target article information and the stored article information, inquiring whether corresponding similarity exists, if so, positioning storage position information corresponding to the stored article which is not similar to the target article, and then recommending the storage position to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article.
2. The method of claim 1, wherein each of said bins is located on a different storage device or a portion of said bins are located on different storage devices.
3. The method of claim 1, comprising:
the method comprises the steps of obtaining image information of a target article, and obtaining storage position information corresponding to the stored article which is dissimilar to the target article by combining a scale invariant feature transformation feature detection algorithm, a perception hash algorithm and a color histogram algorithm based on the stored article image.
4. The method of claim 3, wherein obtaining bin information corresponding to the stored item that is dissimilar to the target item in combination with a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm, and a color histogram algorithm comprises:
according to the image information of the target article and the image information of the stored article, respectively calculating a first matching value, a second matching value and a third matching value through a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm;
judging whether the first matching value is smaller than or equal to a first lowest threshold value or not, if so, judging whether the second matching value is larger than a second highest threshold value or not and whether the third matching value is larger than a third highest threshold value or not, if so, the stored article is similar to the target article, otherwise, the stored article is not similar to the target article;
if not, judging whether the first matching value is smaller than or equal to a first highest threshold value, if so, judging whether the second matching value is larger than a second highest threshold value or whether a third matching value is larger than a third highest threshold value so as to determine whether the stored articles are similar to or dissimilar to the target articles; if not, whether the second matching value is larger than the second lowest threshold value and whether the third matching value is larger than the third lowest threshold value is judged so as to determine whether the stored article is similar to or dissimilar to the target article.
5. The method of any of claims 1-4, further comprising:
the similarity is a character string generated by adopting 0 and 1, and the character string is compressed; where 0 indicates that the target item image is not similar to the other item image, and 1 indicates that the target item image is similar to the other item image.
6. A stock level recommendation device, comprising:
the acquisition module is used for acquiring the information of the currently stored articles on the storage positions according to the information of a plurality of storage positions where the target articles can be stored;
the recommendation module is used for inquiring whether corresponding similarity exists or not based on the target article information and the stored article information, if so, positioning storage position information corresponding to the stored article which is not similar to the target article, and then recommending the storage position to store the target article; if the target article does not exist, acquiring image information of the target article, and calculating to obtain storage position information corresponding to the stored article which is dissimilar to the target article by using a preset similarity calculation method based on the stored article image so as to recommend the storage position to store the target article.
7. The method of claim 6, wherein the recommendation module is further configured to:
the method comprises the steps of obtaining image information of a target article, and obtaining storage position information corresponding to the stored article which is dissimilar to the target article by combining a scale invariant feature transformation feature detection algorithm, a perception hash algorithm and a color histogram algorithm based on the stored article image.
8. The method of claim 7, wherein the recommending module combines a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm, and a color histogram algorithm to obtain bin information corresponding to the stored item that is dissimilar to the target item, comprising:
according to the image information of the target article and the image information of the stored article, respectively calculating a first matching value, a second matching value and a third matching value through a scale invariant feature transform feature detection algorithm, a perceptual hash algorithm and a color histogram algorithm;
judging whether the first matching value is smaller than or equal to a first lowest threshold value or not, if so, judging whether the second matching value is larger than a second highest threshold value or not and whether the third matching value is larger than a third highest threshold value or not, if so, the stored article is similar to the target article, otherwise, the stored article is not similar to the target article;
if not, judging whether the first matching value is smaller than or equal to a first highest threshold value, if so, judging whether the second matching value is larger than a second highest threshold value or whether a third matching value is larger than a third highest threshold value so as to determine whether the stored articles are similar to or dissimilar to the target articles; if not, whether the second matching value is larger than the second lowest threshold value and whether the third matching value is larger than the third lowest threshold value is judged so as to determine whether the stored article is similar to or dissimilar to the target article.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010042783.5A CN113128923A (en) | 2020-01-15 | 2020-01-15 | Storage position recommendation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010042783.5A CN113128923A (en) | 2020-01-15 | 2020-01-15 | Storage position recommendation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113128923A true CN113128923A (en) | 2021-07-16 |
Family
ID=76771466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010042783.5A Pending CN113128923A (en) | 2020-01-15 | 2020-01-15 | Storage position recommendation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113128923A (en) |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080071418A1 (en) * | 2006-08-30 | 2008-03-20 | Antony Felix F | Method and system for inventory placement according to expected item picking rates |
| CN104318259A (en) * | 2014-10-20 | 2015-01-28 | 北京齐尔布莱特科技有限公司 | Target picture identifying device and method for and computing device |
| CN104376052A (en) * | 2014-11-03 | 2015-02-25 | 杭州淘淘搜科技有限公司 | Same-style commodity merging method based on commodity images |
| US9230250B1 (en) * | 2012-08-31 | 2016-01-05 | Amazon Technologies, Inc. | Selective high-resolution video monitoring in a materials handling facility |
| US20160104088A1 (en) * | 2014-10-09 | 2016-04-14 | Hitachi Solutions, Ltd. | Demand-supply adjusting device and demand-supply condition consolidating method |
| US9505554B1 (en) * | 2013-09-24 | 2016-11-29 | Amazon Technologies, Inc. | Capturing packaging image via scanner |
| US20170046654A1 (en) * | 2015-08-11 | 2017-02-16 | Toyota Motor Engineering & Manufacturing North America, Inc. | Free location item and storage retrieval |
| CN106980955A (en) * | 2017-03-29 | 2017-07-25 | 北京京东尚科信息技术有限公司 | Method and apparatus for determining shelf storage space for shelf |
| CN108241645A (en) * | 2016-12-23 | 2018-07-03 | 腾讯科技（深圳）有限公司 | Image processing method and device |
| US20180218471A1 (en) * | 2017-02-02 | 2018-08-02 | Wal-Mart Stores, Inc. | Systems and methods for displaying an item in a selected storage location using augmented reality |
| CN108460098A (en) * | 2018-02-01 | 2018-08-28 | 北京百度网讯科技有限公司 | Information recommendation method, device and computer equipment |
| CN110033061A (en) * | 2019-03-28 | 2019-07-19 | 炬星科技（深圳）有限公司 | Picking task processing method, electronic equipment, robot and storage medium |
| WO2019145395A1 (en) * | 2018-01-25 | 2019-08-01 | Ocado Innovation Limited | Recommendation apparatus and method |
| CN110189076A (en) * | 2019-05-13 | 2019-08-30 | 珠海格力电器股份有限公司 | Prevent the management method and system of article mispairing |
| CN110223011A (en) * | 2019-05-22 | 2019-09-10 | 杭州海仓科技有限公司 | Intelligent storage equipment scheduling method, system, storage medium and electronic equipment |
| CN110270511A (en) * | 2018-03-13 | 2019-09-24 | 北京京东尚科信息技术有限公司 | Article sorting method, control device and system |
| CN110533351A (en) * | 2018-05-23 | 2019-12-03 | 北京京东振世信息技术有限公司 | The method and apparatus for determining target storage space |
-
2020
- 2020-01-15 CN CN202010042783.5A patent/CN113128923A/en active Pending
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080071418A1 (en) * | 2006-08-30 | 2008-03-20 | Antony Felix F | Method and system for inventory placement according to expected item picking rates |
| US9230250B1 (en) * | 2012-08-31 | 2016-01-05 | Amazon Technologies, Inc. | Selective high-resolution video monitoring in a materials handling facility |
| US9505554B1 (en) * | 2013-09-24 | 2016-11-29 | Amazon Technologies, Inc. | Capturing packaging image via scanner |
| US20160104088A1 (en) * | 2014-10-09 | 2016-04-14 | Hitachi Solutions, Ltd. | Demand-supply adjusting device and demand-supply condition consolidating method |
| CN104318259A (en) * | 2014-10-20 | 2015-01-28 | 北京齐尔布莱特科技有限公司 | Target picture identifying device and method for and computing device |
| CN104376052A (en) * | 2014-11-03 | 2015-02-25 | 杭州淘淘搜科技有限公司 | Same-style commodity merging method based on commodity images |
| US20170046654A1 (en) * | 2015-08-11 | 2017-02-16 | Toyota Motor Engineering & Manufacturing North America, Inc. | Free location item and storage retrieval |
| CN108241645A (en) * | 2016-12-23 | 2018-07-03 | 腾讯科技（深圳）有限公司 | Image processing method and device |
| US20180218471A1 (en) * | 2017-02-02 | 2018-08-02 | Wal-Mart Stores, Inc. | Systems and methods for displaying an item in a selected storage location using augmented reality |
| CN106980955A (en) * | 2017-03-29 | 2017-07-25 | 北京京东尚科信息技术有限公司 | Method and apparatus for determining shelf storage space for shelf |
| WO2019145395A1 (en) * | 2018-01-25 | 2019-08-01 | Ocado Innovation Limited | Recommendation apparatus and method |
| CN108460098A (en) * | 2018-02-01 | 2018-08-28 | 北京百度网讯科技有限公司 | Information recommendation method, device and computer equipment |
| CN110270511A (en) * | 2018-03-13 | 2019-09-24 | 北京京东尚科信息技术有限公司 | Article sorting method, control device and system |
| CN110533351A (en) * | 2018-05-23 | 2019-12-03 | 北京京东振世信息技术有限公司 | The method and apparatus for determining target storage space |
| CN110033061A (en) * | 2019-03-28 | 2019-07-19 | 炬星科技（深圳）有限公司 | Picking task processing method, electronic equipment, robot and storage medium |
| CN110189076A (en) * | 2019-05-13 | 2019-08-30 | 珠海格力电器股份有限公司 | Prevent the management method and system of article mispairing |
| CN110223011A (en) * | 2019-05-22 | 2019-09-10 | 杭州海仓科技有限公司 | Intelligent storage equipment scheduling method, system, storage medium and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11397979B2 (en) | Order processing method and device, server, and storage medium | |
| US8737737B1 (en) | Representing image patches for matching | |
| CN107506495B (en) | Information pushing method and device | |
| CN111523977B (en) | Method, device, computing equipment and medium for creating wave order set | |
| CN110020162B (en) | User identification method and device | |
| CN110348771B (en) | Method and device for order grouping of orders | |
| CN109446442B (en) | Method and apparatus for processing information | |
| CN108595448B (en) | Information pushing method and device | |
| CN107193932B (en) | Information pushing method and device | |
| CN111782841A (en) | Image searching method, device, equipment and computer readable medium | |
| EP4131100A1 (en) | Method and apparatus for positioning express parcel | |
| CN107977876B (en) | Method and device for processing order information | |
| CN111177450A (en) | Image retrieval cloud identification method and system and computer readable storage medium | |
| CN111753614A (en) | Commodity shelf monitoring method and device | |
| CN111782850A (en) | Object searching method and device based on hand drawing | |
| CN114036397B (en) | Data recommendation method, device, electronic equipment and medium | |
| CN110020131B (en) | Method and device for arranging commodities | |
| US20210357673A1 (en) | Method and device for processing feature point of image | |
| CN113128923A (en) | Storage position recommendation method and device | |
| CN115578486A (en) | Image generation method and device, electronic equipment and storage medium | |
| CN110827101A (en) | Shop recommendation method and device | |
| CN113379173B (en) | Method and device for marking warehouse goods with labels | |
| CN112861684A (en) | Article display method and device | |
| CN112785216A (en) | Storage position recommendation method and device | |
| CN107180037B (en) | Man-machine interaction method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |