TWI739242B - System for visualized storage configuration and method thereof - Google Patents

Abstract

The present disclosure provides a method for visualized storage configuration, the method includes: providing a product basic data and a storage space data; performing normalization of the product basic data based on the characteristics of the product to generate normalized product data; generating product color information corresponding to the product state according to the normalized product data; generating storage structure information corresponding to the storage status according to the storage space data; and combining the product color information and the storage structure information and adding the product color information to the storage structure information to obtain a visual storage configuration state. The present invention further provides a system for visualized storage configuration that performs operations for the aforementioned visualization.

Description

Visual storage location configuration system and method

The present invention relates to a storage location configuration technology, in particular to a visual storage location configuration system and method.

The issues related to commodity stocking and warehousing management in the logistics industry nowadays have attracted the attention of the industry. Different from the manual planning and management of traditional warehousing management, warehousing management nowadays is more coordinated with information processing to speed up management and reduce manpower consumption, but warehousing management is still There are many room for improvement, such as hot products and unpopular products that cannot be adjusted immediately with sales fluctuations, similar product appearances are difficult to distinguish, which leads to errors in picking, high turnover products are far away from exports, resulting in poor picking rates, and heavy products in the picking process. The above-mentioned various situations have explained the importance of storage space allocation.

On the management side, most of the current warehouse management systems only provide operation screens and statistical report data to warehouse operators and managers, but the warehousing status in text or numbers is not intuitive enough, which makes it impossible for warehouse operators or managers to directly pay attention When it comes to the storage status, for example, various types of goods are stored in the storage. As the products are exported, the quantity of each product decreases. What quantity needs to be restocked will become the primary question. The same size of space can accommodate the quantity of large and small products. The difference is that if only the inventory of goods is considered, there is a risk of distortion. Therefore, such management must consider the relationship between the size of the goods and the storage space. In addition, the inventory of goods is presented in numbers, which is only a cold for storage operators or managers. Numbers, there are 100 or 50 left in the inventory. This kind of inventory may not be intuitive. Warehouse operators or managers are easy to neglect and forget to replenish. For example, the maximum product inventory is 100 but the current product quantity is 5 and the product inventory The maximum value is 10, but the current number of products is 5, and the number of both products is only 5, but the urgency of replenishment between the two may be very different. Therefore, it is necessary to quickly and intuitively know the current status of the warehousing.

In addition, the configuration of storage space is also important for storage management. For example, new entrants cannot quickly schedule the location of the storage space because they cannot consider the multiple characteristics of the product and the arrangement of the storage space at the same time. In short, the characteristics of the product will affect the product availability. The storage location of the warehouse.

The purpose of the present invention is to use the visual system interface to enable managers to quickly know the configuration status of the storage location to determine whether the configuration status of the storage location meets the requirements, thereby solving the problem of the existing warehouse management that cannot intuitively know the configuration status of the storage location. .

The present invention discloses a visual storage location configuration system, which includes: a computing module, which is used to normalize basic commodity data based on commodity characteristics to generate commodity normalization data; and a storage module, which is used to store the commodity normalization Data; and a visualization module, which has a color conversion unit, a storage space data conversion unit, and a storage structure drawing unit, wherein the color conversion unit generates product color information corresponding to the commodity state according to the commodity normalization data, and the storage space data conversion The unit generates storage structure information corresponding to the storage state according to the storage space data, and the storage structure drawing unit combines the product color information with the storage structure information to obtain the visual storage location configuration state of the storage.

The present invention further discloses a method for visual storage allocation, which includes: providing basic commodity data and storage space data; performing normalization of the basic commodity data based on commodity characteristics to generate commodity normalization data; and generating corresponding commodities based on the commodity normalization data Product color information of the state; generate storage structure information corresponding to the storage state based on the storage space data; and combine the product color information and the storage structure information to add the product color information to the storage structure information to obtain a visual storage location configuration state.

In summary, the present invention proposes a visual storage allocation system and its method, through the finally generated visual storage allocation status to make analysis and management more intuitive, to facilitate subsequent optimization management, wherein the visual storage allocation system can be Multi-dimensional real-time analysis is performed on each aspect of commodity data, that is, the storage space data and basic commodity information are analyzed and calculated, and the attribute values of each aspect are presented in color graphics. Furthermore, artificial intelligence (AI) technology or The rule of thumb is to optimize the storage location configuration to provide visual storage location configuration and optimize warehouse management.

The following describes the technical content of the present invention with specific specific embodiments. Those familiar with the art can easily understand the advantages and effects of the present invention from the content disclosed in this specification. However, the present invention can also be implemented or applied by other different specific embodiments.

The modules, units, devices, etc. of the present invention include a microprocessor and memory, and algorithms, data, programs, etc. are stored in the memory or chip, and the microprocessor can load data from the memory or perform the algorithm or program. Data analysis or calculation and other processing are not repeated here. For example, the arithmetic module, storage module, and visualization module of the present invention include a microprocessor and a memory, etc., and each unit in each module performs analysis and calculations. Therefore, the unit or module described in the present invention The hardware details can also be implemented in the same way.

Figure 1 is a structural diagram of the visual storage allocation system of the present invention. The visual storage location configuration system of the present invention provides a visual presentation mechanism of the storage configuration status, more specifically, enables the storage management personnel to grasp the configuration characteristics of each storage location in real time, and at the same time can find the key bottleneck and change the overall storage location. Configuration, to assist warehouse managers to quickly analyze the various aspects of the current configuration, and real-time control the status of the storage space. As shown in the figure, the visual storage location configuration system 1 includes a computing module 11, a storage module 12 and a visualization module 13.

The arithmetic module 11 is used to normalize the basic data of the product based on the characteristics of the product to generate the product normalized data. The normalization is to scale the data to a small specific interval, and the processing process will eliminate the data. The unit limit is to convert different units into pure numbers without units. For example, kilograms and cubic meters are converted into pure numbers between 0 and 1 without units, so that indicators of different units or magnitudes can be compared and weighted. In order to clearly analyze the relationship between each storage location and the product, it is necessary to analyze the product. First, obtain the basic information of the product and analyze it according to the characteristics of the product. The characteristics of the product can be, for example, turnover rate, weight, or volume. Specifically, the size of the product There is a high degree of correlation with the number of storage spaces that can be placed. This will be the basis for whether the goods are restocked, and the weight will become an important reference for the picking order or the way the goods are placed, and the turnover rate is where the goods are placed. A reference basis for storage, so it is necessary to understand the product through the characteristics of the product. Therefore, the calculation module 11 can use the product characteristic normalization matrix to perform normalization on the basic data of the product, and then obtain useful data for subsequent execution of the visualization processing program. The implementation of related formalization is described in detail later.

The storage module 12 is used to store the product normalization data. In short, the product normalization data generated by the computing module 11 will be stored in the storage module 12. That is, in addition to the analysis of the product, the storage status must also be analyzed, so the product is stored temporarily. The visualization data is stored in the storage module 12 for subsequent use in the visualization processing program. The storage module 12 can be a register that can temporarily store data or a readable and writable memory (RAM).

Please also refer to Figure 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. The color conversion unit 131 generates a corresponding commodity status based on the commodity normalization data. Product color information, the storage space data conversion unit 132 generates storage structure information corresponding to the storage state according to the storage space data, and the storage structure drawing unit 133 combines the product color information with the storage structure information to obtain the visual storage space of the storage Configuration status. Specifically, the visualization module 13 is used to execute a data visualization program, which includes a color conversion unit 131, a storage space data conversion unit 132, a storage structure drawing unit 133, etc., and the color conversion unit 131 analyzes the commodity normalization data In order to make the product generate corresponding colors according to the product status, that is, the color information of the given product; the storage space data conversion unit 132 generates storage structure information based on the storage space data, and the storage structure information generates the storage status of the entire warehouse based on the storage space data The storage status is, for example, the length, width, height, aisle width, number of shelves, and number of storage spaces in the storage; the storage structure drawing unit 133 combines the product color information and the storage structure information to make the storage structure information The color corresponding to the commodity state is inserted to generate a visual storage location configuration state, which is the visual information about the storage location configuration in the warehouse.

In one embodiment, the product normalization data is an attribute value ranging from 0 to 1, and the color conversion unit 131 expresses the difference in characteristics of the product by color shades according to the attribute value. In other words, after normalization, the products in each storage location are represented by a value from 0 to 1, and these values have corresponding colors according to different attributes (for example, used to indicate turnover, weight, or volume), such as bright red, bright Red, dark red, light red, etc., and the color conversion unit 131 uses the color shade to indicate the difference in product characteristics based on the attribute value.

In another embodiment, the storage structure drawing unit 133 can incorporate the product color information representing the state of the product into the storage structure information showing the storage state, so that each storage location of the storage structure information is based on the The differences in product characteristics are presented in different shades of color. As mentioned above, the storage structure drawing unit 133 combines the color information of the product with the storage structure information. The former represents the status of the product, and the latter brings the storage status. After the storage structure drawing unit 133 is combined, a visual graph can be drawn. That is to use the color shade to indicate the commodity status of each storage location in accordance with the storage status.

Figure 2 is a structural diagram of another embodiment of the visual storage allocation system of the present invention. As shown in the figure, the calculation module 11, the storage module 12, and the visualization module 13 of the visual storage allocation system 1 are the same as those in Fig. 1, and will not be repeated here. In this embodiment, the visual storage allocation system 1 It can be further connected to the warehouse database 2, the front-end display device 3 and the optimized storage location configuration system 4.

The storage database 2 is used to store basic commodity data and storage space data. Based on the data analysis and subsequent visual processing of the system, the basic commodity data and storage space data required by the visual storage allocation system 1 can be stored in the storage database 2, and the basic commodity data can be entered by the warehouse manager. The storage space The data can be defined by drawing software to define the size of the space and the positional relationship of each storage location. Therefore, when the visual storage location allocation system 1 needs to perform storage location allocation, the storage database 2 can obtain relevant data.

The front-end display device 3 is used to display the visual storage location configuration status generated by the warehouse structure drawing unit 133 with color graphics. In other words, in order to allow the warehouse manager to intuitively and quickly understand the storage location configuration status of the entire warehouse, The present invention uses color graphics to display the visual storage position configuration status obtained after the system calculation and analysis, that is, the color indicates the commodity data status of each storage position, such as the quantity, volume, or turnover rate. In a specific embodiment, the front-end display device 3 may be various types of displays.

The optimized storage location configuration system 4 is used to perform optimization processing of storage location configuration. Specifically, the optimized storage space allocation system 4 can optimize storage space allocation through the integration of multiple information. For example, it can determine the stock volume of new products based on stock forecast data and the volume of the product on the social network, and can refer to it. Weather data and timing data comprehensively consider the required stocking volume, so as to reduce the stocking volume in the off-season and adjust the corresponding product stocking volume according to factors such as weather or festivals, or it can be based on the basic product data, appearance similarity, and Comprehensive analysis of commodity price differences, so as to achieve the purpose of avoiding problems such as the storage locations of goods with high shipping relevance that are far away, similar in appearance and difficult to distinguish, or the prices of adjacent goods are very different, or It can integrate basic product information, historical order data and other information to avoid problems such as placing products with high turnover rate far away from the entrance and exit, and placing heavy products on top of lighter products when picking, so optimize storage space The configuration system 4 can provide the purpose of effective management and configuration of commodities. Regarding the specific technology of the aforementioned optimized storage space allocation system 4, please refer to the ROC patent application filed on August 31, 1987 with application number 107130601.

The visual storage allocation system 1 of the present invention can be executed on an electronic device, such as a general computer, a tablet, or a server. It performs data analysis and calculation after obtaining data from the warehouse database 2, so the visual storage allocation system 1 performs The program can be designed and built on electronic equipment with processors, memory and other components through software to run on various electronic equipment; in addition, the computing module 11 and storage in the visual storage location system 1 can also be configured The module 12 and the visualization module 13 are respectively composed of independent components, such as a calculator, a memory, a storage, or a firmware with a processing unit, which can all be components for implementing the present invention. In addition, the aforementioned color conversion unit 131, storage space data conversion unit 132, and storage structure drawing unit 133 can also be presented in a software program, hardware, or firmware structure.

Figure 3 is a step diagram of the method of visualized storage location configuration of the present invention. As shown in the figure, in step S31, basic commodity data and storage space data are provided. This step is to understand the relationship between each storage location and the product, so it will first obtain the basic product data and storage space data. The basic product data records the product information, such as size, quantity, etc., and the storage space data is the storage space data. The status of each storage location, such as the length, width and height of the storage location, the width of the aisle, the number of shelves and the number of storage locations.

In step S32, the normalization of the basic data of the product is performed based on the characteristics of the product to generate the product normalized data. This step is to use the product characteristic normalization matrix to normalize the basic information of the product, and then obtain useful data for the subsequent execution of the visualization program. Specifically, the product characteristic can be, for example, turnover rate, weight, or volume, and the size of the product is important for storage. The number of products placed has its relevance. The weight of the product will be a reference for the picking order or the placement of the product, and the turnover rate is the basis for the placement of the product. Therefore, the basic information of the product will be normalized first. Obtaining the value will make the value easy to analyze and apply.

In one embodiment, the product normalization data is an attribute value ranging from 0 to 1, and different attribute values use color shades to indicate the difference in the characteristics of the product. That is to say, after normalization, the attribute value will be 0 to The value of 1, and each value range will correspond to the same color but with different shades. Specifically, if the attribute value is 1-0.9, it can be the darkest, the attribute value is 0.9-0.8, it can be the second darkest, and so on , And the attribute value of 0.1-0 can be the lightest color, but the value range and depth are not limited by this.

In step S33, the product color information corresponding to the product status is generated according to the product normalization data. In this step, you can analyze the product normalization data to make the product generate corresponding colors based on the product status, that is, give the product color information based on the product normalization data. For example, if the attribute is weight, the top 20% weight can be used Dark red, and the 20%-40% heavy can be used dark red, through the above color rendering, the warehouse manager can intuitively store the allocation status.

In step S34, the storage structure information corresponding to the storage state is generated according to the storage space data. In step S34, the storage space data conversion unit 132 can generate storage structure information according to the storage space data. The storage status can be, for example, the length, width, height, aisle width, number of shelves, and number of storage spaces in the storage. Color graphics to show the display of goods and storage positions, so it is necessary to obtain storage structure information.

In step S35, the product color information and the warehouse structure information are combined to add the product color information to the warehouse structure information to obtain a visual storage location configuration state. In step S35, the storage structure drawing unit 133 can be used to combine the product color information with the storage structure information, that is, to insert the color of the corresponding product state in the storage structure information, so as to generate a visual storage location configuration state, that is, Visual information about the allocation of storage locations in the warehouse.

Specifically, the above steps are to incorporate the color information of the product that represents the status of the product into the storage structure information that presents the storage status, so that each storage location of the storage structure information is based on the difference in the characteristics of the product. Different shades of color are presented.

In another embodiment, the foregoing method further includes transmitting the visual storage position configuration state to the front-end display device. Specifically, because a visual storage position configuration state represented by color graphics is generated, the visual storage position configuration state can be transmitted to the front-end display device for the warehouse manager to view.

In another embodiment, the foregoing method further includes transmitting the visual storage position configuration state to an optimized storage position configuration system to perform optimization processing of storage position configuration. In order to make the storage location configuration meet the demand, the visual storage location configuration status can be sent to the optimized storage location configuration system for storage location optimization. In addition to the product side and the storage side, the optimization considerations can also incorporate multiple information for integration. Optimize the allocation of storage locations, such as considering the sound volume, weather data, and timing data of the product on the social network, as a reference for the stock volume, and for example, based on the similarity of product appearance and product price difference to avoid high shipping relevance Goods are stored too far, the appearance of the product is difficult to distinguish, the product is too close, or the price difference of adjacent products is large. For example, the basic product information and historical order information can be integrated to avoid the high turnover rate of the product far away from the import and export, and the heavy product When picking goods are placed on the lighter ones, the visual storage position configuration state can be transmitted to the optimized storage position configuration system 4 to further provide effective management and configuration of goods.

Figures 4A-4C are schematic diagrams of the implementation architecture and operation of the visual storage allocation system of the present invention. As shown in Figure 4A, the visual storage location configuration system 1 is respectively connected to the storage database 2, the front-end display device 3, and the optimized storage location configuration system 4. The storage database 2 stores basic commodity data and storage space data.

The computing module 11 of the visual storage allocation system 1 is used to normalize the basic data of the product. Specifically, the calculation module 11 performs normalization through the product characteristic normalization matrix. The calculation of the product characteristic normalization matrix includes the attributes (such as weight, turnover rate, etc.) in the basic data of the product to obtain an N*1 product Attribute matrix, and then obtain the minimum and maximum values of all elements in the matrix from the product attribute matrix, then subtract all the elements in the product attribute matrix from the minimum value in the product attribute matrix, and then divide by the matrix The maximum value minus the minimum value can get the normalized product attribute value, all attribute values are in the interval [0, 1].

The calculation of the normalization matrix of the above commodity characteristics can be as shown in Figure 5, and the weight is taken as an example in the table. Among them, the weight of product 001 is 960 grams, the weight of product 002 is 1000 grams, the weight of product 003 is 960 grams, and the weight of product 004 is 1100 grams. The maximum value of the matrix obtained above is 1100 grams, the minimum value is 960 grams, and then the matrix Subtract the minimum value (960 grams) from all the elements in the matrix, and then divide by the maximum value (1100 grams) minus the minimum value (960 grams) in the matrix, so the product attribute value can be obtained, such as 1, 0 or 0.285, This also shows the relative size relationship of each product in terms of weight, rather than just classification or analysis with direct values.

It can be seen from the above that the commodity normalization data is an attribute value in the range of 0 to 1. The commodity normalization data can be stored in the storage module 12, and then to generate a color graphic visualization of the storage position configuration state, the visualization module can be used The visualization module 13 includes a color conversion unit 131, a storage space data conversion unit 132, and a storage structure drawing unit 133. The color conversion unit 131 uses the color shade to represent the difference in product characteristics, that is, the color conversion The unit 131 normalizes the basic information of the product to the attribute value in the interval [0, 1], inserts it into the color brightness to distinguish the color shade, and expresses the difference in the product characteristics (turnover rate, weight or volume) of the position through the color shade .

For example, the color conversion unit 131 can perform calculations based on the normalized attribute values of the basic data of the product and the attribute category of the product. Here, the HSV color space conversion method is taken as an example, as shown in Figure 4B. Explain the color conversion process (no data storage or temporary storage issues are considered here). The HSV color space includes hue value (hereinafter referred to as Hue value), saturation value (hereinafter referred to as Saturation value) and lightness value (hereinafter referred to as Value). Value), each commodity attribute is assigned its own unique Hue value (here the Hue value is the hue of the relevant color, the unit is angle, usually 0-360 degrees, and each specific color has its own unique hue angle, for example, red is 0 degrees, green is 120 degrees, etc.), the color conversion unit 131 will determine a Hue value according to the attribute category of the product to be calculated, that is, each product has its representative color, for example, brand A canned food is green, brand B The bag is bright red, and the saturation value means the saturation of the relevant color. Here, it is fixed at 1, which means the color is the most vivid. That is, the saturation value taken by the color conversion unit 131 during subsequent operations is 1. After the basic data is normalized (for example, using normalized commodity turnover matrix, normalized commodity weight matrix or normalized commodity volume matrix, the normalization operation is as described above), the normalized attribute values obtained are brought into the representative light and dark changes The value of the value (the lightness of the color), this Value value falls in the interval of [0, 1], that is, according to the characteristics of the product (turnover rate, weight or volume) to give different lightness, and finally, the color conversion unit 131 uses the foregoing to obtain The hue value, saturation value and lightness value are calculated to obtain the color used to represent the product, including a certain color to represent the product, and the brightness to represent the characteristics of the product, and finally it can be presented in different colors as shown in Figure 5. The attribute value obtained. As mentioned above, the storage status can include the length, width, height, aisle width, number of shelves, and storage digits of the storage space. Therefore, the storage space data conversion unit 132 can obtain the storage space, aisle, and shelf information in the storage space according to the storage space data. The position relationship status, for example, there are aisles between the shelves, and the shelves on both sides of the aisle have several storage positions, and the length, width and height of each storage position are further set. Based on this, the entire storage size, storage number and position relationship can be known. Specifically, as shown in Figure 4C, the storage space data conversion unit 132 also records the name of the storage compartment and the product data stored in the storage compartment. That is to say, in addition to recording the storage status, it also records the name of each storage compartment. (For example, code) and the product information stored in the storage compartment (for example, brand A cans), so that when the storage structure drawing unit 133 subsequently needs to draw the overall structure, each storage compartment and its contents can be correspondingly mapped. . In addition, the positional relationship can be defined through coordinate methods, such as setting up a starting point, and defining the coordinates of the storage position through the position of the XY axis vector.

The storage structure drawing unit 133 integrates the product color information showing the state of the goods into the storage structure information showing the state of the storage, so that each storage location can be presented in different color shades according to the difference in product characteristics. Specifically, the storage structure drawing unit 133 is used to draw shelves, insert the coordinates of the goods in the storage space according to the color brightness, and draw the storage location points, so that the positional relationship of the storage locations in the storage can be confirmed, and the product color information The nesting of the product makes the state of the product appear in color in each storage location, that is, the color depth corresponds to the difference in product characteristics (turnover rate, weight, or volume). As shown in Figure 4C, it illustrates the storage structure drawing process. The storage structure drawing unit 133 receives the name of the storage compartment, the product data stored in the storage compartment, and the storage status provided by the storage space data conversion unit 132, and obtains it accordingly The storage compartment attributes including the storage compartment height, the storage compartment width, the storage compartment depth and the storage compartment name, and then the storage structure drawing unit 133 uses the storage compartment attributes to construct the shelf attributes including the number of single-row storage compartments and the number of shelf layers , And use the storage compartment attributes and the shelf attributes to construct the storage area attributes including the number of shelves in the storage area, the width of the aisle between the shelves, and the width of the lateral aisle. Finally, the storage structure drawing unit 133 obtains relevant information based on the storage area attributes and The attribute value of each commodity obtained by the color conversion space 131 is drawn to map the storage position configuration state, that is, the (x, y, z) absolute coordinates of the storage compartment and the shelf, which are finally presented by the front-end display device 3.

In another embodiment, the visual storage location configuration system 1 is connected to the front-end display device 3, and the front-end display device 3 is used to display the visualization storage location configuration status generated by the storage structure drawing unit 133 in a color graphic to facilitate Warehouse managers and/or operators can quickly and intuitively obtain the visualized storage location configuration status for further storage location configuration 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 optimization processing of storage allocation. As shown in the figure, AI can be applied Technology is optimized, and the rule of thumb can also be used for optimization, that is, through the experience of the warehouse manager to adjust the placement of the goods in the storage space.

The following describes the visualization of the storage location configuration through different application scenarios.

Figure 6 is a schematic diagram of the first example of the visualized storage location configuration of the present invention. The figure shows the storage location configuration representation of a row/layer shelf (ie one-dimensional visualization representation), in which the color shades of the same color system represent the turnover rate of each storage location commodity (the specific calculation method has been detailed above As mentioned, the following embodiments are the same), the darker the color indicates the higher the turnover rate of the product, and the lighter the color indicates the lower the turnover rate of the product. As shown in the figure, configuration 1 and configuration 2 are two different storage allocation methods. At the arrow in configuration 1, the product turnover rate is high but the storage location is far away from the shipping area (exit), so it can be judged immediately This configuration method will lead to poor shipping efficiency, and configuration 2 shows that the higher the turnover rate, the closer the product is placed to the shipping area (exit), that is, the closer the exit is, the darker the color. This configuration Will make shipping efficiency better.

Fig. 7 is a schematic diagram of a second example of the visual storage location configuration of the present invention. The picture shows the storage location configuration representation of multiple rows/one-layer racks (ie, two-dimensional visual representation), where the color shade of the same color system represents the turnover rate of each storage location commodity, and the darker the color indicates the commodity turnover The higher the rate, the lighter the color, the lower the commodity turnover rate. As shown in the figure, at the arrow in configuration 3, the commodity turnover rate is high but it is placed deep in the warehouse (that is, far away from the exit of the shipping area). It can be immediately judged that the storage location is poorly configured, which will affect the shipping efficiency. , And configuration 4 is from far away from the exit to the exit, the overall color changes from lighter to darker (such as changing layer by layer). This shows that the higher the turnover rate, the closer the product is placed to the shipping area (exit), so the configuration Shipping efficiency will be better.

Fig. 8 is a schematic diagram of a third example of the visual storage location configuration of the present invention. The figure shows that the storage space configuration representation method can simultaneously present a variety of product characteristics, such as weight and turnover rate, and are expressed in different colors. As shown in the figure, the number in the storage space of configuration 5 represents each product number. This configuration can be separately The distribution characteristics of products are presented in different color systems. For example, the color on the left represents the turnover rate of each storage space, and the color on the right represents the unit weight of each storage space. As mentioned above, the darker the color indicates the higher the turnover rate of the product or the heavier the weight, and the lighter the color indicates Low product turnover rate or lighter weight. Similarly, when products with a high turnover rate or a heavier weight are placed at the exit, it helps to reduce picking time and labor costs.

Figure 9 is a schematic diagram of a fourth example of the visual storage configuration of the present invention. The picture is an extension of two-dimensional visual representation to three-dimensional space, which is suitable for multi-row/multi-layer shelf storage configuration representation (that is, three-dimensional visualization representation), where the same color shade represents the product of each storage location Turnover rate, the darker the color indicates the higher the turnover rate of the product, the lighter the color indicates the lower the turnover rate of the product. As shown in the figure, configuration 6 shows that the higher the turnover rate, the closer the product is placed to the shipping area (from right to left, the color changes from lighter to darker), which helps the subsequent picking process.

It can be seen from the above that through the visualization of the storage location configuration status, the product configuration distribution can be quickly viewed, so that the warehouse manager can quickly understand the configuration of the entire storage, and it is also conducive to the adjustment of the storage location configuration. In particular, the color depth indicates the attributes of the product. The difference allows the warehouse manager to intuitively understand the current status of each storage location. Compared with the traditional digital representation, it can more directly reflect how the warehouse is adjusted, and it also allows non-warehouse managers (such as the boss) to easily understand the storage situation. It can provide a lot of help for the storage location configuration and management of the existing warehouse.

In summary, the visual storage allocation system and method proposed by the present invention can facilitate the viewing and management of storage allocation by visualizing the storage allocation status, which is not only intuitive but also overcomes the traditional shortcomings of only using numerical values. Among them, the visual storage allocation system performs multi-dimensional real-time analysis of various aspects of commodity data in the warehouse, and displays the attribute values of each aspect using color graphics. In addition, the visualization of storage allocation status can also be achieved through artificial intelligence (AI) technology or It is an empirical rule to perform optimization of storage location configuration, so the present invention can provide visual storage location configuration and simplify subsequent storage management.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the relevant technical field can make some changes and modifications without departing from the spirit and scope of the present invention. The protection scope of the present invention shall be subject to those defined by the attached patent application scope.

1 Visualized storage allocation system 11 Computing module 12 Storage module 13 Visualization module 131 Color conversion unit 132 Storage space data conversion unit 133 Warehousing architecture drawing unit 2 Warehousing database 3 Front-end display device 4 Optimize the storage allocation system S31-S35 Procedure

Figure 1 is a structural diagram of the visual storage allocation system of the present invention. Figure 2 is a structural diagram of another embodiment of the visual storage allocation system of the present invention. Figure 3 is a step diagram of the method of visualized storage location configuration of the present invention. Figures 4A-4C are schematic diagrams of the implementation architecture and operation of the visual storage allocation system of the present invention. Figure 5 is a schematic diagram of the normalization of the basic commodity data of the present invention. Figure 6 is a schematic diagram of the first example of the visualized storage location configuration of the present invention. Fig. 7 is a schematic diagram of a second example of the visual storage location configuration of the present invention. Fig. 8 is a schematic diagram of a third example of the visual storage location configuration of the present invention. Figure 9 is a schematic diagram of a fourth example of the visual storage configuration of the present invention.

1 Visualized storage allocation system 11 Computing module 12 Storage module 13 Visualization module

Claims (19)

A visual storage location configuration system includes: an arithmetic module, which normalizes the basic data of the product according to the characteristics of the product to generate product normalization data; a storage module, which is used to store the product normalization data; and a visualization model The group has a color conversion unit, a storage space data conversion unit, and a storage structure drawing unit. The color conversion unit generates commodity color information corresponding to the commodity state based on the commodity normalization data, and the storage space data conversion unit uses storage space data Generate storage structure information corresponding to the storage state. The storage structure drawing unit combines the product color information with the storage structure information to obtain the storage location configuration state of the storage. The normalized data of the product is in the range of 0 to 1. The attribute value, and the color conversion unit expresses the difference in the characteristics of the product with color shades according to the attribute value. For example, the visual storage location configuration system described in item 1 of the scope of patent application, wherein the basic product information and the storage space data are from the storage database. For example, the visual storage allocation system described in item 1 of the scope of patent application, wherein the calculation module includes a product characteristic normalization matrix used to normalize the basic data of the product. The visual storage allocation system described in item 1 of the scope of patent application, wherein the commodity characteristics include turnover rate, weight or volume. For example, the visual storage allocation system described in item 1 of the scope of patent application, wherein the color conversion unit is based on the commodity attribute category of the commodity basic data Find the corresponding hue value, define the saturation value as 1, and substitute the attribute value of the product normalized data into the lightness value, and use the HSV color space conversion to generate the color used to represent the product. For example, the visual storage location configuration system described in item 1 of the scope of patent application, wherein the storage status includes the length, width, height, aisle width, number of shelves or number of storage locations in the storage. For example, the visual storage space allocation system described in item 1 of the scope of patent application, the storage space data conversion unit uses the pre-stored storage status, storage compartment name and product data stored in the storage compartment to make each storage compartment correspond to its product . For example, the visual storage space allocation system described in item 1 of the scope of patent application, wherein the storage structure drawing unit integrates the product color information representing the state of the product into the storage structure information showing the storage state, so as to Each storage location of the storage structure information is presented in different shades of color according to the difference in the characteristics of the product. For example, the visual storage location configuration system described in item 1 of the scope of patent application, wherein the storage structure drawing unit obtains the storage grid according to the storage compartment name provided by the storage space data conversion unit, the product data stored in the storage compartment, and the storage status Attributes, and construct shelf attributes according to the storage compartment attributes and construct storage area attributes according to the storage compartment attributes and the shelf attributes, so as to draw the visualization based on the storage area attributes and the attribute value of the commodity obtained by the color conversion space Storage location configuration status. The visual storage allocation system described in item 1 of the scope of patent application further includes a front-end display device for displaying the visual storage allocation status generated by the storage structure drawing unit in color graphics. According to the visual storage allocation system described in item 1 of the scope of patent application, wherein the visual storage allocation status is transmitted to the optimized storage allocation system to perform optimization processing of the storage allocation. A visual storage allocation method, which includes: providing basic product information and storage space data; performing regularization of the basic product data based on the characteristics of the product to generate product regularization data; generating product color corresponding to the status of the product based on the product regularization data Information; generate warehouse structure information corresponding to the warehouse state based on the warehouse space data; and combine the product color information and the warehouse structure information to add the product color information to the warehouse structure information to obtain a visual storage location configuration state, where, The product normalization data is attribute values ranging from 0 to 1, and different attribute values use color shades to indicate the difference in the characteristics of the product. For example, the visual storage allocation method described in item 12 of the scope of patent application, wherein the step of performing the normalization of the basic product information based on the product characteristics is to normalize the basic product information through the product characteristics normalization matrix. For example, the visual storage location allocation method described in item 12 of the scope of patent application, wherein the step of combining the product color information and the storage structure information is to The color information of the product representing the status of the product is integrated into the storage structure information showing the storage status, so that each storage location of the storage structure information is presented in different color shades according to the difference in the characteristics of the product. As described in item 12 of the scope of the patent application, the method for configuring a visual storage position further includes transmitting the state of the configuration of the visual storage position to a front-end display device. As described in item 12 of the scope of patent application, the method of visual storage allocation includes transmitting the state of the visualization storage allocation to an optimized storage allocation system to perform optimization processing of storage allocation. For example, the visual storage allocation method described in item 12 of the scope of patent application, wherein, in the step of generating product color information corresponding to the product status based on the normalized data of the product, the step of generating product color information corresponding to the product status further includes: Obtain the corresponding hue value from the reserved hue value; define the saturation value as 1; substitute the attribute value of the product normalized data into the lightness value; and use the hue value, the saturation value and the lightness value through the HSV color space To generate the color used to represent the product. For example, the visual storage location allocation method described in item 12 of the scope of patent application, wherein, in the step of generating the storage structure information corresponding to the storage state according to the storage space data, it includes the pre-stored storage state and the storage compartment name and storage. The product information stored in the grid, so that each grid corresponds to its product. For example, the method for visual storage location allocation described in item 18 of the scope of patent application, wherein the step of combining the product color information and the storage structure information to add the product color information to the storage structure information to obtain the visualization storage location allocation status The compound includes: obtaining the storage compartment attributes of the storage compartment according to the name of the storage compartment, the product information stored in the storage compartment and the storage status; constructing the shelf attributes according to the storage compartment attributes; and according to the storage compartment attributes and The shelf attribute constructs the storage area attribute, and the visual storage location configuration state is drawn through the storage area attribute and the attribute value.

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