CN113011802A - Visual storage position configuration system and method thereof - Google Patents
Visual storage position configuration system and method thereof Download PDFInfo
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Abstract
The invention provides a visual storage position configuration system and a method thereof, wherein the visual storage position configuration system comprises the following steps: providing basic data of the goods and warehousing space data; performing normalization of the basic data of the commodity based on the characteristics of the commodity to generate commodity normalization data; generating commodity color information corresponding to the commodity status according to the commodity normalization data; generating warehousing structure information corresponding to the warehousing status according to the warehousing space data; and combining the commodity color information and the warehousing structure information to add the commodity color information into the warehousing structure information to obtain a visual storage allocation state.
Description
Technical Field
The present invention relates to a storage location configuration technology, and more particularly, to a visual storage location configuration system and method thereof.
Background
The current logistics industry concerns the problem of commodity stock and storage management, which is different from the manual planning and management of the traditional storage management, the current storage management is often matched with informatization processing to accelerate the management speed and reduce the manpower consumption, the storage management still faces various improvement spaces, such as hot commodities and cold commodities which cannot be adjusted in real time along with the fluctuation of sales volume, the similar and difficult distinguishing of commodity appearance leads to the easy error of picking, the high turnover rate commodities are far away from the export, the poor picking rate is caused by the heavy commodities pressing on the light commodities in the picking process, and the like, and the above various situations illustrate the importance of storage position allocation.
On the management side, most of the existing warehousing management systems only provide operation pictures and statistical report data for warehousing operators and managers, but the warehousing status of characters or numbers is not intuitive enough, which results in that the warehousing operators or managers cannot directly notice the warehousing status problem, for example, the warehousing stores various types of commodities, the quantity of each commodity is reduced along with the output of the commodities, which quantity needs to be supplemented becomes the first problem, the same space size can be used for storing large commodities and small commodities with different quantities, if only considering the inventory quantity of the commodities, the distorted state is probably caused, therefore, the management needs to consider the relation between the volumes of the commodities and the warehousing space, in addition, the inventory quantity of the commodities is presented by numbers, the warehousing operators or managers only have one cold number, and the inventory quantity of 100 or 50 remains, and the inventory presentation is feared, it is necessary for the warehouse operator or manager to be able to quickly and intuitively know the current status of the warehouse because, for example, the maximum inventory value of 100 but the current quantity of 5 and the maximum inventory value of 10 but the current quantity of 5 are both left, but the urgency of replenishment may be very different.
In addition, the storage space configuration is also important for storage management, such as that the new people cannot quickly schedule the shelf position of the storage position because they cannot simultaneously consider the multiple characteristics of the goods and the arrangement of the storage positions in the storage, in short, the characteristics of the goods will affect the storage position of the goods in the storage.
Disclosure of Invention
The invention aims to enable a manager to quickly know the storage allocation state by means of a visual system interface so as to determine whether the storage allocation state meets the requirement or not, thereby solving the problem that the storage allocation state cannot be intuitively known in the existing storage management.
The invention discloses a visual storage position configuration system, which comprises: the computing module is used for normalizing the basic data of the commodity based on the characteristics of the commodity so as to generate commodity normalized data; a storage module for storing the commodity normalization data; and the visual module is provided with a color conversion unit, a storage space data conversion unit and a storage architecture drawing unit, wherein the color conversion unit generates commodity color information corresponding to a commodity state according to the commodity normalization data, the storage space data conversion unit generates storage architecture information corresponding to a storage state according to the storage space data, and the storage architecture drawing unit combines the commodity color information with the storage architecture information to obtain a visual storage position configuration state of storage.
The invention also discloses a visual storage position configuration method, which comprises the following steps: providing basic data of the goods and warehousing space data; performing normalization of the basic data of the commodity based on the characteristics of the commodity to generate commodity normalization data; generating commodity color information corresponding to the commodity status according to the commodity normalization data; generating warehousing structure information corresponding to the warehousing status according to the warehousing space data; and combining the commodity color information and the warehousing structure information to add the commodity color information into the warehousing structure information to obtain a visual storage allocation state.
In summary, the present invention provides a visual storage allocation system and method thereof, which makes analysis management more intuitive through the finally generated visual storage allocation state to facilitate subsequent optimization management, wherein the visual storage allocation system can perform multi-dimensional real-time analysis on each orientation of the commodity data in the warehouse, i.e. the warehouse space data and the commodity basic information are analyzed and operated, so that the attribute values of each orientation are presented by color graphics, and further, the storage allocation can be optimized through Artificial Intelligence (AI) technology or experience rule, so as to provide visual storage allocation and optimized warehouse management.
Drawings
Fig. 1 is an architecture diagram of a visual magazine configuration system of the present invention.
Fig. 2 is an architecture diagram of another embodiment of the visual storage configuration system of the present invention.
FIG. 3 is a step diagram of a visual storage allocation method according to the present invention.
Fig. 4A-4C are schematic diagrams illustrating an implementation structure and operation of the visual storage allocation system according to the present invention.
FIG. 5 is a diagram illustrating an operation of performing normalization on basic merchandise data according to the present invention.
FIG. 6 is a diagram illustrating a first example of a visual storage location configuration according to the present invention.
FIG. 7 is a diagram illustrating a second example of a visual storage location configuration according to the present invention.
FIG. 8 is a diagram illustrating a third example of a visual storage location configuration according to the present invention.
FIG. 9 is a diagram illustrating a fourth example of a visual storage location configuration according to the present invention.
Description of the symbols
1 visual storage position configuration system
11 operation module
12 storage module
13 visualization module
131 color conversion cell
132 warehouse space data conversion unit
133 warehouse architecture drawing unit
2 warehouse database
3 front end display device
4 optimize and store up position configuration system
S31-S35.
Detailed Description
The present invention is described in terms of specific embodiments, and those skilled in the art will readily appreciate the advantages and utilities of the present invention from the disclosure herein. The invention is capable of other and different embodiments and of being practiced or being carried out in various ways.
The modules, units, devices, etc. of the present invention include a microprocessor and a memory, and algorithms, data, programs, etc. are stored in the memory or chip, and the microprocessor can load the data, algorithms, or programs from the memory to perform data analysis or calculation, etc., which will not be described herein. For example, the operation module, the storage module and the visualization module of the present invention include a microprocessor, a memory, etc., and each unit in each module performs the analysis operation, so that the hardware detailed structures of the units or modules of the present invention can be implemented in the same manner.
Fig. 1 is an architecture diagram of a visual magazine configuration system of the present invention. The visual storage position configuration system provided by the invention provides a visual presentation mechanism of the storage configuration state, and more particularly, enables storage management personnel to immediately master the configuration characteristics of each storage position, finds key bottlenecks and changes the overall storage position configuration, assists the storage management personnel to quickly analyze the characteristics of the current configuration and immediately control the use situation of the storage positions. As shown in the figure, the visualization storage allocation system 1 includes an operation module 11, a storage module 12, and a visualization module 13.
The operation module 11 is configured to normalize the basic data of the commodity based on the characteristics of the commodity to generate normalized data of the commodity, where the normalization is to scale the data in proportion so that the normalized data falls into a small specific interval, and the unit limitation of the data is eliminated in the processing process, and different units are converted into unit-free pure numbers, for example, each kilogram and each cubic meter is converted into a pure number between 0 and 1 unit-free units, so that indexes of different units or orders of magnitude can be compared and weighted conveniently. In order to clearly analyze the relationship between each storage position and each commodity, the analysis is performed on the commodity, first, basic data of the commodity is obtained, and the analysis is performed according to characteristics of the commodity, such as turnover rate, weight or volume, and the like. Therefore, the computing module 11 can normalize the basic data of the product by using the product characteristic normalization matrix, and further obtain useful data for performing the visualization processing procedure subsequently. The normalization is performed as described later.
The storage module 12 is used for storing the commodity normalization data. In short, the commodity normalization data generated by the operation module 11 is stored in the storage module 12, that is, in addition to the analysis of the commodity, the storage status is also analyzed, so the commodity normalization data is temporarily stored in the storage module 12 for the subsequent visual processing procedure. The storage module 12 may be a Register (Register) capable of temporarily storing data or a read-write memory (RAM).
Referring to fig. 4A, the visualization module 13 includes a color conversion unit 131, a storage space data conversion unit 132 and a storage structure drawing unit 133, wherein the color conversion unit 131 generates commodity color information corresponding to a commodity state according to the commodity normalization data, the storage space data conversion unit 132 generates storage structure information corresponding to a storage state according to the storage space data, and the storage structure drawing unit 133 combines the commodity color information with the storage structure information to obtain a storage visual storage allocation state. Specifically, the visualization module 13 is used for performing data visualization, and includes a color conversion unit 131, a storage space data conversion unit 132, a storage structure drawing unit 133, and the like, wherein the color conversion unit 131 analyzes the commodity normalization data to generate corresponding colors for the commodity according to the commodity state, that is, the given commodity color information; the storage space data conversion unit 132 generates storage structure information according to the storage space data, and the storage structure information generates storage states in the whole storage according to the storage space data, such as the length, width, height, track width, number of shelves and number of storage positions in the storage; the warehouse architecture drawing unit 133 combines the commodity color information and the warehouse architecture information, so that the warehouse architecture information is embedded with the colors corresponding to the commodity states to generate a visual storage allocation state, which is the visual information related to the storage allocation in the warehouse.
In one embodiment, the commodity normalization data is an attribute value in a range from 0 to 1, and the color conversion unit 131 represents the difference of the commodity characteristics by color shades according to the attribute value. In other words, after normalization, the goods in each bin are represented by values from 0 to 1, and the values have corresponding colors, such as bright red, dark red, light red, etc., according to different attributes (for example, for representing turnover rate, weight or volume), and the color conversion unit 131 represents the difference of the characteristics of the goods by using the color depth according to the attribute values.
In another embodiment, the warehouse architecture drawing unit 133 can embed the commodity color information representing the commodity status into the warehouse architecture information representing the warehouse status, so as to present the commodity color information in different shades in each storage position of the warehouse architecture information according to the difference of the commodity characteristics. As mentioned above, the warehouse structure drawing unit 133 combines the commodity color information with the warehouse structure information, the former represents the commodity status, and the latter with the warehouse status, and the warehouse structure drawing unit 133 can draw the visual graph, that is, the color depth is used to represent the commodity status of each storage position in cooperation with the warehouse status.
Fig. 2 is an architecture diagram of another embodiment of the visual storage configuration system of the present invention. As shown in the figure, the operation module 11, the storage module 12 and the visualization module 13 of the visualization storage allocation system 1 are the same as those in fig. 1, and are not described herein again, in this embodiment, the visualization storage allocation system 1 may be further connected to the warehousing database 2, the front-end display device 3 and the optimized storage allocation system 4.
The warehouse database 2 is used to store the basic data of the goods and the warehouse space data. Based on the data analysis and subsequent visualization processing performed by the system, the basic data of the goods and the storage space data required by the visual storage allocation system 1 can be stored in the storage database 2, the basic data of the goods can be input by a storage manager, and the storage space data can define the relation between the space size and the position of each storage position through drawing software, so that when the visual storage allocation system 1 needs to perform storage allocation, the storage database 2 can obtain the relevant data.
The front-end display device 3 is used for displaying the visual storage allocation status generated by the storage structure drawing unit 133 by using a color graph, and in order to enable a storage manager to intuitively and quickly know the storage allocation status of the whole storage, the invention displays the visual storage allocation status obtained by the calculation and analysis of the system by using the color graph, that is, the commodity data status of each storage, such as the quantity, the volume or the turnover rate, is expressed by using the color. In one embodiment, the front-end display device 3 may be a variety of displays.
The optimized bin allocation system 4 is used for performing optimization processing of bin allocation. Specifically, the optimized storage allocation system 4 can optimize the storage allocation by integrating a plurality of messages, for example, the stock quantity of a new commodity can be determined by referring to the volume of the commodity on the social network according to stock forecast data, and the stock quantity required by comprehensively considering weather data and timing data, so as to achieve the purpose of reducing the stock quantity in off seasons and adjusting the corresponding stock quantity of the commodity according to factors such as weather or festivals, or the storage allocation system can comprehensively analyze the commodity basic data, the appearance similarity and the commodity price difference, thereby achieving the purpose of avoiding the problems of very distant storage positions of the commodities with high shipment correlation, very close separation of the commodities with similar appearance and difficult discrimination, or large price difference of the adjacent commodities, or integrating messages such as the commodity basic data, historical order data and the like, so as to avoid the commodities with high turnover rate from being placed at a place far away from the gateway, The goods with heavy weight are placed above the light goods when picking the goods, so the optimized storage position configuration system 4 can provide the purpose of effective management configuration of the goods. For a specific technique for optimizing the storage allocation system 4, please refer to taiwan patent application No. 107130601 filed by the applicant at 31/8/2018.
The visual storage allocation system 1 of the present invention can be executed on electronic equipment, such as a general computer, a tablet or a server, and performs data analysis and calculation by obtaining data from the storage database 2, so that the program executed by the visual storage allocation system 1 can be designed and configured on electronic equipment having elements such as a processor, a memory, etc. through software to run on various electronic equipment; in addition, the operation module 11, the storage module 12 and the visualization module 13 in the visualization bit allocation system 1 may be respectively composed of independent components, such as a calculator, a memory, a storage or a firmware with a processing unit, which may be components for implementing the present invention. In addition, the color conversion unit 131, the warehouse space data conversion unit 132 and the warehouse architecture drawing unit 133 can be presented by software program, hardware or firmware architecture.
FIG. 3 is a step diagram of a visual storage allocation method according to the present invention. As shown in the figure, in step S31, the basic data of the merchandise and the data of the storage space are provided. This step is to know the relationship between each storage location and the goods, so the basic data of the goods and the storage space data are obtained first, the basic data of the goods record the goods information, such as size, quantity, etc., and the storage space data is the state of each storage location in the storage, such as the length, width, height, track width, number of shelves and the number of storage locations.
In step S32, the commodity normalization data is generated by performing the normalization of the commodity basic data based on the commodity characteristics. In the step, the commodity characteristic normalization matrix is used for normalizing the basic commodity data to obtain useful data of a subsequent visual program, specifically, the commodity characteristic can be, for example, turnover rate, weight or volume, the volume of the commodity has relevance to the quantity of commodities placed in the storage space, the weight of the commodity can be used as a picking sequence or a reference for commodity placement, the turnover rate is a basis for commodity placement, the basic commodity data can be normalized firstly, and the numerical value obtained after normalization can be easily analyzed and applied.
In one embodiment, the article normalization data is attribute values in the range of 0 to 1, and different attribute values represent differences in the characteristics of the article by shades of color, that is, after normalization, the attribute values are 0 to 1 values, and each value range corresponds to a color with different shades, specifically, if the attribute values are 1 to 0.9, the attribute values are darkest, the attribute values are 0.9 to 0.8, the attribute values are darkest, and so on, and the attribute values are 0.1 to 0, the attribute values are lightest, but the value range and the shades are not limited thereto.
In step S33, the color information of the corresponding merchandise is generated according to the merchandise normalization data. In this step, the commodity normalization data can be analyzed to generate corresponding colors according to the commodity status, that is, the commodity color information is given according to the commodity normalization data, for example, if the attribute is weight, the top 20% of the commodities can be used in dark red, and the 20% -40% of the commodities can be used in dark red.
In step S34, warehouse structure information corresponding to the warehouse status is generated according to the warehouse space data. In step S34, the warehouse space data transformation unit 132 generates the warehouse structure information according to the warehouse space data, such as the length, width, track width, shelf number and storage quantity of the storage location in the warehouse, which is necessary to obtain the warehouse structure information because the display of the goods and storage locations is shown in color.
In step S35, the commodity color information and the warehouse structure information are combined to add the commodity color information to the warehouse structure information to obtain a visual storage allocation status. In step S35, the warehouse architecture drawing unit 133 combines the commodity color information and the warehouse architecture information, i.e. the warehouse architecture information is embedded with the colors corresponding to the commodity status, so as to generate the visual storage location configuration status, i.e. the visual information related to the storage location configuration in the warehouse.
Specifically, the above steps are to fit the commodity color information representing the commodity status into the warehousing structure information representing the warehousing status, so as to represent the warehousing structure information in different colors according to the difference of the commodity characteristics.
In another embodiment, the method further comprises transmitting the visual storage configuration status to a front-end display device. Specifically, because the visual bin allocation status is generated in a color graphic representation, the visual bin allocation status can be transmitted to the front-end display device for viewing by the warehousing manager.
In another embodiment, the method further comprises transmitting the visualized bin allocation status to an optimized bin allocation system for performing optimization of the bin allocation. In order to make the storage allocation meet the requirement, the visual storage allocation status can be transmitted to the optimized storage allocation system for storage optimization, and the point of optimization consideration can be integrated with a plurality of messages in addition to the commodity surface and the storage surface to optimize the storage allocation, such as taking into account the sound volume, weather data and timing data of the commodities on the social network as the stock quantity reference, and for example, according to the commodity appearance similarity and the commodity price difference, to avoid the goods picking problems of over-long storage, over-close appearance, or large price difference of the adjacent commodities with high delivery correlation, and for example, integrating the basic commodity data and historical order data to avoid the problems of placing on lighter goods when the commodities with high turnover rate are far away from the entrance and exit, and heavy goods are picked, so the visual storage allocation status can be transmitted to the optimized storage allocation system 4, to further provide an efficient management configuration of the goods.
Fig. 4A-4C are schematic diagrams illustrating an implementation structure and operation of the visual storage allocation system according to the present invention. As shown in fig. 4A, the visual storage allocation system 1 is connected to the warehousing database 2, the front-end display device 3 and the optimized storage allocation system 4, wherein the warehousing database 2 stores basic data of goods and warehousing space data.
The operation module 11 of the visual storage allocation system 1 is used for normalizing the basic data of the commodity. Specifically, the operation module 11 performs normalization through a commodity characteristic normalization matrix, where the operation of the commodity characteristic normalization matrix includes obtaining a commodity attribute matrix of N × 1 from attributes (such as weight, turnover rate, and the like) in the commodity basic data, obtaining a minimum value and a maximum value of all elements in the matrix from the commodity attribute matrix, subtracting the minimum value in the commodity attribute matrix from all elements in the commodity attribute matrix, and dividing by the value obtained by subtracting the minimum value from the maximum value in the matrix to obtain normalized commodity attribute values, where all the attribute values are values in the interval of [0, 1 ].
The above calculation of the commodity characteristic normalization matrix is shown in fig. 5, in which weight is taken as an example. The weight of the product 001 is 960 grams, the weight of the product 002 is 1000 grams, the weight of the product 003 is 960 grams, the weight of the product 004 is 1100 grams, the maximum value in the matrix obtained by the above is 1100 grams, the minimum value in the matrix is 960 grams, the minimum value (960 grams) is subtracted from all elements in the matrix, and the value obtained by subtracting the minimum value (960 grams) from the maximum value (1100 grams) in the matrix is divided, so that the product attribute value, such as 1, 0 or 0.285, can be obtained, and the product attribute value also represents the relative size relationship of the products in terms of weight, but is not classified or analyzed only by direct values.
As can be seen from the above, the commodity normalization data is an attribute value in the interval of 0 to 1, the commodity normalization data can be stored in the storage module 12, and then if a visual storage location configuration state of a color graph is to be generated, the visualization module 13 can be executed by the visualization module 13, the visualization module 13 includes a color conversion unit 131, a storage space data conversion unit 132 and a storage architecture drawing unit 133, and the color conversion unit 131 represents the difference of the commodity characteristics by the color depth, that is, the color conversion unit 131 normalizes the commodity basic data to the attribute value in the interval of [0, 1], and fits into the color brightness to distinguish the color depth, so as to represent the difference of the commodity characteristics (turnover rate, weight or volume) of the location by the color depth.
For example, the color conversion unit 131 performs operations by calculating the attribute Value normalized by the basic data of the product and the attribute type of the product, as shown in fig. 4B, a color conversion process is described (no data storage or temporary storage problem is considered here), wherein the HSV color space includes a Hue Value (hereinafter referred to as Hue Value), a Saturation Value (hereinafter referred to as Saturation Value) and a brightness Value (hereinafter referred to as Value), each product attribute is assigned with a unique Hue Value (here, Hue Value is Hue of the relevant color, and the unit is an angle, usually 0-360 degrees, and each specific color has its own unique Hue angle, for example, red is 0 degree, green is 120 degrees, etc.), the color conversion unit 131 determines a Hue Value, that is, each product has its representative color according to the attribute type of the product to be calculated, for example, the first brand can is green, the second brand bag is bright red, and the Saturation Value means the Saturation of the color, fixed as 1, representing the most bright color, that is, the Saturation Value obtained by the subsequent operation of the color conversion unit 131 is 1, in addition, the normalized attribute Value obtained by normalizing the basic data of the commodity (for example, using the normalized commodity turnover rate matrix, the normalized commodity weight matrix or the normalized commodity volume matrix, the normalization operation of which is as described above) is substituted into the Value representing the brightness change (brightness of the color), the Value falls in the interval of [0, 1], that is, different brightness is given according to the characteristics of the commodity (turnover rate, weight or volume), finally, the color conversion unit 131 uses the obtained hue Value, Saturation Value and brightness Value to calculate the color for representing the commodity, including representing the commodity with a certain color, the brightness level represents the level of the product characteristics, and finally the attribute values obtained in fig. 5 can be presented in different colors. As mentioned above, the storage status may include the length, width, height, lane width, number of shelves and number of storage positions, so the storage space data conversion unit 132 can obtain the position relationship status of the storage positions, the lane and the shelves in the storage according to the storage space data, for example, the lane is set between the shelves, the shelves on both sides of the lane have several storage positions, and further set the length, width and height of each storage position, so as to know the size, number and position relationship of the whole storage. Specifically, as shown in fig. 4C, the storage space data converting unit 132 records the storage name and the commodity data stored in the storage, that is, records each storage name (for example, a code number) and the commodity data (for example, a brand a can) stored in the storage in addition to the storage status, so that each storage and its content can be associated when the storage structure drawing unit 133 performs the whole structure drawing later. In addition, the positional relationship may be defined by a coordinate method, such as establishing a starting point, defining the coordinates of the bin by the XY axis vector position.
The warehouse architecture drawing unit 133 embeds the commodity color information showing the commodity status into the warehouse architecture information showing the warehouse status, so that each storage position can be shown in different colors according to the characteristic difference of the commodity. Specifically, the warehouse architecture drawing unit 133 is used to draw the storage shelves, and the coordinates of the goods in the warehouse space are nested according to the color brightness, and the storage locations are drawn, so as to confirm the position relationship of each storage location in the warehouse, and the goods status is presented in each storage location by color through the nesting of the goods color information, that is, the difference of the goods characteristics (turnover rate, weight or volume) is mapped by the color depth. As shown in fig. 4C, to illustrate the storage structure drawing process, the storage structure drawing unit 133 receives the storage space data conversion unit 132 provided with the storage name, the storage data stored in the storage, and the storage status, and accordingly obtains the storage attributes including the storage height, the storage width, the storage depth, and the storage name, the storage structure drawing unit 133 constructs the shelf attributes including the number of single-row shelves and the number of shelves by using the storage attributes, and constructs the storage region attributes including the number of shelves in the storage region, the aisle width between shelves, and the lateral aisle width by using the storage attributes and the shelf attributes, and finally, the storage structure drawing unit 133 draws the storage location configuration status, that is, the storage space and shelf (x, y, z) absolute coordinates, which are finally presented by the front-end display device 3.
In another embodiment, the visual storage allocation system 1 is connected to the front-end display device 3, and the front-end display device 3 displays the visual storage allocation status generated by the storage structure drawing unit 133 in a color graph, so that the storage manager and/or operator can quickly and intuitively obtain the visual storage allocation status for further storage allocation and adjustment.
In another embodiment, the visual storage allocation system 1 is connected to the optimized storage allocation system 4, so that the optimized storage allocation system 4 performs the optimization of the storage allocation, as shown in the figure, AI technology can be applied for optimization, and in addition, the optimization can also be performed by using rule of thumb, that is, the allocation position of the goods in the storage is adjusted by the experience of the warehousing manager.
The following description is directed to the visualization of the bin configuration, and may be applied to different situations.
FIG. 6 is a diagram illustrating a first example of a visual storage location configuration according to the present invention. The figure shows a commodity storage position arrangement representation (i.e. a one-dimensional visual representation) of a row/layer of racks, wherein the transfer rate of each storage position commodity is represented by the shade of the same color system (the specific calculation method is described in detail above, and all the following embodiments are the same), and the darker the color indicates that the commodity transfer rate is higher, and the lighter the color indicates that the commodity transfer rate is lower. As shown in the figure, the configuration 1 and the configuration 2 are two different storage location configurations, respectively, the arrow in the configuration 1 has a high commodity turnover rate but the storage location is far from the delivery area (exit), so that it can be immediately determined that the delivery efficiency is poor, and the configuration 2 displays that the higher the turnover rate is, the closer the commodity is placed to the delivery area (exit), that is, the closer the color is, the deeper the product is, the better the delivery efficiency is.
FIG. 7 is a diagram illustrating a second example of a visual storage location configuration according to the present invention. The figure is a commodity storage position arrangement representation method (namely, a two-dimensional visual representation method) for displaying a plurality of rows of material racks/one layer of material racks, wherein the color depth of the same color system represents the turnover rate of commodities of each storage position, the deeper the color, the higher the commodity turnover rate, and the lighter the color, the lower the commodity turnover rate. As shown in the figure, the arrow in configuration 3 shows that the commodity turnover rate is high but the commodity turnover rate is placed in the storage position deep in the warehouse (i.e. far away from the outlet of the delivery area), so that the storage position configuration is judged to be poor immediately, which affects the delivery efficiency, while configuration 4 shows that the commodity turnover rate is higher and the commodity is placed closer to the delivery area (outlet), so that the configuration delivery efficiency is better, since the overall color changes from light to dark (e.g. layer by layer) from the position far away from the outlet to the outlet.
FIG. 8 is a diagram illustrating a third example of a visual storage location configuration according to the present invention. The figure shows that the configuration representation method of the commodity storage position can simultaneously present various commodity characteristics, such as weight and turnover rate, and is represented by different color systems, and as shown in the figure, 5 is configured, the numbers in the storage position represent the serial numbers of each commodity, and the configuration can respectively present the commodity distribution characteristics by different color systems. For example, the darker the color on the left represents the turnover rate of each store item, and the lighter the color on the right represents the unit weight of each store item, as described above, the darker the color represents the higher the turnover rate or the heavier the item, and the lighter the color represents the lower the turnover rate or the lighter the item. Similarly, when the outlet is used for placing the commodities with high commodity turnover rate or heavier commodity weight, the goods picking time and the labor cost are reduced.
FIG. 9 is a diagram illustrating a fourth example of a visual storage location configuration according to the present invention. The figure is a commodity storage position configuration representation method (namely, a three-dimensional visual representation method) which extends a two-dimensional visual representation method to a three-dimensional space and is suitable for a multi-row/multi-layer material rack, wherein the color depth of the same color system represents the turnover rate of commodities of each storage position, the deeper the color, the higher the turnover rate of the commodities, and the lighter the color, the lower the turnover rate of the commodities. As shown, configuration 6 shows that the higher the turnover rate, the closer the goods are placed to the shipment area (from right to left, the color is changed from light to dark), which facilitates the subsequent pick process.
Therefore, the configuration distribution of the commodities can be quickly inspected through the visual configuration state of the storage positions, so that a storage manager can quickly know the configuration condition of the whole storage conveniently, the adjustment of the configuration of the storage positions is facilitated, particularly, the difference of the attributes of the commodities is represented by the color depth, the storage manager can visually know the state of each storage position at present, compared with the traditional digital representation, the adjustment of the storage position can be reflected more directly, and a non-storage manager (such as a boss) can easily know the storage condition, so that the configuration and the management of the storage positions of the existing storage can be helped.
In summary, the present invention provides a visual storage allocation system and method thereof, which can facilitate checking and managing storage allocation of warehouse through visual storage allocation status, not only intuitively but also overcome the disadvantages of traditional numerical representation, wherein the visual storage allocation system performs multidimensional real-time analysis on each surface of commodity data in warehouse, and displays each surface attribute value by using color graphics, and in addition, the visual storage allocation status can also perform optimization of storage allocation through Artificial Intelligence (AI) technology or experience rules, thereby providing visual storage allocation and simplifying subsequent warehouse management.
Although the present invention has been described with reference to the above embodiments, it should be understood that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (21)
1. A visual storage configuration system, comprising:
the operation module normalizes the basic data of the commodity according to the characteristics of the commodity to generate normalized data of the commodity;
a storage module for storing the commodity normalization data; and
the visual module is provided with a color conversion unit, a storage space data conversion unit and a storage architecture drawing unit, wherein the color conversion unit generates commodity color information corresponding to a commodity state according to the commodity normalization data, the storage space data conversion unit generates storage architecture information corresponding to a storage state according to the storage space data, and the storage architecture drawing unit combines the commodity color information with the storage architecture information to obtain a visual storage position configuration state of storage.
2. The visual stocker configuration system of claim 1, wherein the basic data of the product and the warehousing space data are from a warehousing database.
3. The visual stocker configuration system according to claim 1, wherein the computing module further comprises a commodity characteristic normalization matrix for normalizing the commodity basic data.
4. The visual storage space configuration system of claim 1, wherein the commodity characteristic comprises a turnover, a weight, or a volume.
5. The visual storage allocation system of claim 1, wherein the commodity normalization data is an attribute value between 0 and 1, and the color conversion unit represents the difference of the commodity characteristics by the color shade according to the attribute value.
6. The visual storage allocation system of claim 1, wherein the color transformation unit finds out the corresponding hue value according to the commodity attribute category of the commodity basic data, defines the saturation value as 1, and substitutes the attribute value of the commodity normalization data into the brightness value to generate the color representing the commodity by HSV color space transformation.
7. The visual bin configuration system of claim 1, wherein the warehousing status includes the length, width, height, lane width, number of shelves or number of bins within a warehouse.
8. The visual storage allocation system of claim 1, wherein the storage space data transformation unit is configured to make each storage cell correspond to its corresponding product by pre-storing the storage status, the storage cell name and the product data stored in the storage cell.
9. The visual storage allocation system of claim 1, wherein the storage structure drawing unit is configured to fit the color information of the product representing the status of the product into the storage structure information representing the storage status, so as to present the storage structure information in different shades according to the difference of the characteristics of the product in each storage space.
10. The visual storage allocation system of claim 1, wherein the storage structure drawing unit obtains storage attributes according to the storage names provided by the storage space data conversion unit, the commodity data stored in the storage and the storage status, constructs shelf attributes according to the storage attributes, constructs storage area attributes according to the storage attributes and the shelf attributes, and draws the visual storage allocation status according to the storage area attributes and the attribute values of the commodities obtained from the color conversion space.
11. The visual storage space allocation system of claim 1, further comprising a front-end display device for displaying the visual storage space allocation status generated by the warehouse rack mapping unit in a color graph.
12. The visual storage bin configuration system of claim 1 wherein the visual storage bin configuration status is transmitted to an optimized storage bin configuration system for performing optimization of the storage bin configuration.
13. A visual storage position configuration method is characterized by comprising the following steps:
providing basic data of the goods and warehousing space data;
performing normalization of the basic data of the commodity based on the characteristics of the commodity to generate commodity normalization data;
generating commodity color information corresponding to the commodity status according to the commodity normalization data;
generating warehousing structure information corresponding to the warehousing status according to the warehousing space data; and
combining the commodity color information and the warehouse structure information to add the commodity color information into the warehouse structure information to obtain a visual storage allocation status.
14. The method of claim 13, wherein the step of normalizing the commodity base data based on commodity characteristics normalizes the commodity base data by a commodity characteristic normalization matrix.
15. The visual storage allocation method of claim 13, wherein the commodity normalization data is an attribute value between 0 and 1, and different attribute values represent the difference of the commodity characteristics by shades of color.
16. The visual storage allocation method of claim 13, wherein the step of combining the merchandise color information and the storage structure information is to fit the merchandise color information representing the merchandise status into the storage structure information representing the storage status, so as to be represented in different colors in each storage of the storage structure information according to the difference of the merchandise characteristics.
17. The visual storage configuration method of claim 13, further comprising communicating the visual storage configuration status to a front-end display device.
18. The method of claim 13, further comprising transmitting the status of the visual bucket configuration to an optimized bucket configuration system for performing optimization of the bucket configuration.
19. The visual storage allocation method of claim 13, wherein the step of generating commodity color information corresponding to commodity status according to the commodity normalization data further comprises:
obtaining corresponding hue value from the reserved hue value according to the commodity attribute type of the commodity basic data;
defining the saturation value as 1;
substituting the attribute value of the commodity normalization data into the brightness value; and
and generating a color for representing the commodity by utilizing the hue value, the saturation value and the lightness value through the HSV color space.
20. The method as claimed in claim 13, wherein the step of generating the warehousing structure information corresponding to the warehousing status according to the warehousing space data further comprises associating each bin with its corresponding product by pre-storing the warehousing status, the bin name and the product data stored in the bin.
21. The visual storage allocation method of claim 20, wherein the step of combining the merchandise color information and the warehouse architecture information to add the merchandise color information to the warehouse architecture information to obtain a visual storage allocation status further comprises:
obtaining the storage cell attribute of the storage cell according to the storage cell name, the commodity data stored in the storage cell and the storage state;
constructing the property of the goods shelf according to the property of the storage cell; and
constructing a storage area attribute according to the storage attribute and the shelf attribute, so as to draw the visual storage allocation state through the storage area attribute and the attribute value.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002183649A (en) * | 2000-12-11 | 2002-06-28 | C0Smos Sangyo Kk | Warehouse-managing method and system |
| US7007029B1 (en) * | 1999-01-15 | 2006-02-28 | Metaedge Corporation | System for visualizing information in a data warehousing environment |
| US20060129577A1 (en) * | 2004-12-15 | 2006-06-15 | Fujitsu Limited | Apparatus, program and method for assisting with product design |
| US20100218131A1 (en) * | 2009-02-23 | 2010-08-26 | Microsoft Corporation | Multiple views of multi-dimensional warehouse layout |
| KR101234761B1 (en) * | 2012-11-16 | 2013-02-19 | 인하대학교 산학협력단 | Postprocessing system for a gis based visualization of massive results data from the efdc model |
| US20140244340A1 (en) * | 2013-02-22 | 2014-08-28 | Entercoms, Inc. | System and method for service chain visualization and monitoring of reusable commodities |
| US20170300878A1 (en) * | 2000-06-28 | 2017-10-19 | Buymetrics, Inc. | System and method for adapting market data and evaluating alternative configurations |
| WO2018069518A1 (en) * | 2016-10-13 | 2018-04-19 | Kircheis Thomas | Computer-implemented warehouse management method |
| US20190318371A1 (en) * | 2018-04-13 | 2019-10-17 | Walmart Apollo, Llc | Computing systems and methods for improving content quality for internet webpages |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200812886A (en) * | 2006-09-15 | 2008-03-16 | China Steel Corp | 3D operating and managing system for plane warehouse |
| JP2009067526A (en) * | 2007-09-13 | 2009-04-02 | Hitachi Ltd | Inventory visualization service system between bases |
| US20180060943A1 (en) * | 2016-08-26 | 2018-03-01 | Wal-Mart Stores, Inc. | Apparatus and method for management of a hybrid store |
| CN110458489A (en) * | 2019-07-05 | 2019-11-15 | 宁波海上鲜信息技术有限公司 | Chief storekeeper's method, system, storage medium and its intelligent terminal |
-
2019
- 2019-12-19 TW TW108146756A patent/TWI739242B/en active
-
2020
- 2020-01-13 CN CN202010034353.9A patent/CN113011802A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7007029B1 (en) * | 1999-01-15 | 2006-02-28 | Metaedge Corporation | System for visualizing information in a data warehousing environment |
| US20170300878A1 (en) * | 2000-06-28 | 2017-10-19 | Buymetrics, Inc. | System and method for adapting market data and evaluating alternative configurations |
| JP2002183649A (en) * | 2000-12-11 | 2002-06-28 | C0Smos Sangyo Kk | Warehouse-managing method and system |
| US20060129577A1 (en) * | 2004-12-15 | 2006-06-15 | Fujitsu Limited | Apparatus, program and method for assisting with product design |
| JP2006172058A (en) * | 2004-12-15 | 2006-06-29 | Fujitsu Ltd | Information processor, product design support program and product design supporting method |
| US20100218131A1 (en) * | 2009-02-23 | 2010-08-26 | Microsoft Corporation | Multiple views of multi-dimensional warehouse layout |
| KR101234761B1 (en) * | 2012-11-16 | 2013-02-19 | 인하대학교 산학협력단 | Postprocessing system for a gis based visualization of massive results data from the efdc model |
| US20140244340A1 (en) * | 2013-02-22 | 2014-08-28 | Entercoms, Inc. | System and method for service chain visualization and monitoring of reusable commodities |
| WO2018069518A1 (en) * | 2016-10-13 | 2018-04-19 | Kircheis Thomas | Computer-implemented warehouse management method |
| US20190318371A1 (en) * | 2018-04-13 | 2019-10-17 | Walmart Apollo, Llc | Computing systems and methods for improving content quality for internet webpages |
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