CN115578039B - A warehouse location allocation method, electronic equipment and computer storage medium - Google Patents

Abstract

The embodiment of the present application proposes a method for allocating goods in a warehouse, an electronic device and a computer storage medium, which are applied in the field of logistics scheduling. In the warehouse location allocation method, the collected inventory types and shipments are first statistically sorted to obtain warehouse goods classification results. Secondly, the volume of warehouse goods and the number of warehouse goods that each type of warehouse goods need to occupy are calculated according to the cuboid inclusion method. Then, the coordinate system of the warehouse shelf is established with the origin of the warehouse location at the delivery platform of the warehouse, and the coordinates of the warehouse location are obtained, and the classification result of the warehouse location is obtained by classification. Finally, the obtained warehouse goods classification results are fitted with the warehouse location classification results to obtain the target warehouse location allocation function, and the warehouse location coordinates corresponding to the minimum value of the target warehouse location allocation function are used as the location of the warehouse to be allocated. storage location. The embodiment of the present application not only improves warehouse operation efficiency and shortens warehouse operation time, but also improves warehouse space utilization.

Description

A warehouse cargo space allocation method, electronic equipment and computer storage medium

Technical Field

The present application relates to the technical field of warehousing logistics scheduling, and in particular to a warehouse cargo space allocation method, electronic equipment and computer storage medium.

Background Art

Automated storage/retrieval system (AS/RS) is an important part of modern warehouse management system. The functions of the warehouse include storage, management, freight, dispatch, etc., which can significantly improve the utilization rate of warehouse area and space utilization and reduce management costs. The warehouse helps to achieve large-scale cargo storage and efficient logistics transportation, thus meeting the needs of modern production and life. The research of dispatching system is the focus of warehouse design. The core of the dispatching algorithm is to reduce the time of storage, speed up the turnover rate and maintain the stability of the shelf.

The characteristics of the goods stored in the warehouse determine the scale of the warehouse and the way the warehouse operates. An unreasonable distribution method of warehouse goods directly leads to low efficiency of warehouse in and out operations and long time for warehouse goods in and out. Under the condition of a certain warehouse scale, a good warehouse cargo allocation and scheduling can not only greatly improve the space utilization of the warehouse, but also shorten the warehouse operation time and improve the warehouse operation efficiency.

Summary of the invention

The embodiments of the present application provide a warehouse cargo space allocation method, an electronic device, and a computer storage medium, which improve the warehouse operation efficiency, shorten the warehouse operation time, and improve the space utilization rate of the warehouse.

To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

In a first aspect, a warehouse cargo space allocation method is provided, which classifies warehouse goods and warehouse cargo spaces, determines the priority of goods leaving the warehouse, sets an allocation coefficient, and fits the warehouse cargo entry and exit time and the impact value of the warehouse goods on the center of gravity of the shelf according to the priority of the warehouse goods to obtain a target warehouse cargo space allocation function, and the warehouse cargo space coordinates corresponding to the minimum value of the target warehouse cargo space allocation function are used to indicate the storage location of the warehouse cargo space to be allocated.

In a possible implementation, the warehouse cargo space allocation method includes the following steps:

Step 1: Count and sort the collected inventory types and shipment quantities to obtain warehouse goods classification results;

Step 2: Calculate the volume of the warehouse goods according to the rectangular containment method, and calculate the number of warehouse shelves that each type of warehouse goods needs to occupy based on the warehouse shelf grid size and the warehouse goods classification results obtained in step 1;

Step 3: Establish a warehouse shelf coordinate system with the warehouse delivery platform as the warehouse cargo location origin, obtain the warehouse cargo location coordinates, and classify the warehouse cargo locations according to the warehouse goods classification results and the number of warehouse cargo locations to obtain the warehouse cargo location classification results;

Step 4: Fit the warehouse cargo classification results and warehouse cargo location classification results, determine the priority of the warehouse cargo according to the set allocation coefficient, obtain the target warehouse cargo location allocation function, and use the warehouse cargo location coordinates corresponding to the minimum value of the target warehouse cargo location allocation function as the storage location of the warehouse cargo location to be allocated.

In a possible implementation, step 1 specifically includes the following steps:

Step 11: Set the statistical period and collect the inventory types and shipment quantities of all warehouse goods within the statistical period;

Step 12: Calculate the shipment percentage of the warehouse goods based on the collected inventory types and shipment quantities and sort them by shipment percentage;

Step 13: According to the sorting results, draw a warehouse goods classification statistical chart with the cumulative percentage of warehouse goods as the horizontal axis and the cumulative percentage of shipments as the vertical axis to obtain the warehouse goods classification results.

In a possible implementation, step 2 specifically includes the following steps:

Step 21: Collect the length, width and height of the warehouse goods, and calculate the volume of the warehouse goods according to the rectangular parallelepiped inclusion method;

Step 22: Calculate the number of warehouse shelves that each type of warehouse goods needs to occupy based on the volume of the warehouse goods, the difference coefficient, the size of the warehouse shelf grid, the average inventory during the statistical period, and the warehouse goods classification results obtained in step 1.

In a possible implementation manner, step 3 specifically includes the following steps:

Step 31: Take the warehouse delivery platform as the warehouse cargo location origin coordinate, take the warehouse shelf row extension direction as the X-axis, the warehouse shelf column extension direction as the Y-axis, and the shelf layer extension direction as the Z-axis to establish a warehouse shelf coordinate system, and obtain the warehouse cargo location coordinate;

Step 32: Increase the weight coefficient a in the horizontal direction of the warehouse shelf and the weight coefficient b in the vertical direction, calculate the actual distance between the warehouse shelf storage position and the warehouse shelf origin coordinates according to the warehouse shelf coordinates, and sort them;

Step 33: Classify the warehouse cargo locations according to the sorting results of the actual distances, the number of warehouse cargo locations, and the warehouse cargo classification results to obtain the warehouse cargo location classification results.

In a possible implementation, step 4 specifically includes the following steps:

Step 41: Determine the priority of the goods leaving the warehouse according to the allocation coefficient of the goods in the warehouse, the frequency of the goods entering and leaving the warehouse, and the quality of the goods in the warehouse;

Step 42: Calculate the storage time of the warehouse goods to be allocated according to the priority of the warehouse goods, the access frequency of the warehouse goods, and the time of transporting the warehouse goods to the warehouse cargo location origin coordinates;

Step 43: Calculate the impact of the warehouse goods on the center of gravity of the warehouse shelves according to the mass of the warehouse goods and the number of layers on which the warehouse goods are placed;

Step 44: adding a weight coefficient c to the warehouse goods entry time calculated in step 42, adding a weight d to the influence value calculated in step 43, and fitting the warehouse goods entry time and the influence value after adding the weight coefficient to obtain the target warehouse cargo space allocation function;

Step 45: Calculate the target warehouse cargo location allocation function value of the warehouse cargo to be allocated, and use the warehouse cargo location coordinates corresponding to the minimum value of the target warehouse cargo location allocation function as the storage location of the warehouse cargo location to be allocated.

In a second aspect, an embodiment of the present application further provides an electronic device, including a memory and a processor;

The memory is used to store computer programs; when the processor executes the computer program, the above-mentioned warehouse cargo space allocation method is implemented.

In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above-mentioned warehouse cargo location allocation method.

The description of the technical effects of the second and third aspects is the same as that of the first aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a warehouse cargo space allocation method provided in an embodiment of the present application.

DETAILED DESCRIPTION

It should be noted that the terms "first", "second", etc. involved in the embodiments of the present application are only used to distinguish features of the same type and cannot be understood as indicating relative importance, quantity, order, etc.

The terms "exemplary" or "for example" and the like in the embodiments of the present application are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the related concepts in a specific way.

The terms "fitting" and "connection" involved in the embodiments of the present application should be understood in a broad sense. For example, they may refer to a direct physical connection, or an indirect connection achieved through electronic devices, such as a connection achieved through resistors, inductors, capacitors or other electronic devices.

An embodiment of the present application provides a warehouse cargo space allocation method, which classifies warehouse goods and warehouse cargo spaces, determines the priority of goods leaving the warehouse, sets an allocation coefficient, and fits the warehouse entry and exit time of the warehouse goods with the impact value of the warehouse goods on the center of gravity of the shelf according to the priority of the warehouse goods to obtain a target warehouse cargo space allocation function, and the warehouse cargo space coordinates corresponding to the minimum value of the target warehouse cargo space allocation function are used to indicate the storage location of the warehouse cargo space to be allocated.

In some possible implementations, as shown in FIG1 , the method includes the following steps:

Step 1: Count and sort the collected inventory types and shipment quantities to obtain warehouse goods classification results;

Step 2: Calculate the volume of the warehouse goods according to the rectangular containment method, and calculate the number of warehouse shelves that each type of warehouse goods needs to occupy based on the warehouse shelf grid size and the warehouse goods classification results obtained in step 1;

Step 3: Establish a warehouse shelf coordinate system with the warehouse delivery platform as the warehouse cargo location origin, obtain the warehouse cargo location coordinates, and classify the warehouse cargo locations according to the warehouse goods classification results and the number of warehouse cargo locations to obtain the warehouse cargo location classification results;

Step 4: Fit the warehouse cargo classification results and warehouse cargo location classification results, determine the priority of the warehouse cargo according to the set allocation coefficient, obtain the target warehouse cargo location allocation function, and use the warehouse cargo location coordinates corresponding to the minimum value of the target warehouse cargo location allocation function as the storage location of the warehouse cargo location to be allocated.

In some possible implementations, the step 1 specifically includes the following steps:

Step 11: Set the statistical period and collect the inventory types and shipment quantities of all warehouse goods within the statistical period;

Step 12: Calculate the shipment percentage of the warehouse goods based on the collected inventory types and shipment quantities and sort them by shipment percentage;

Step 13: According to the sorting results, draw a warehouse goods classification statistical chart with the cumulative percentage of warehouse goods as the horizontal axis and the cumulative percentage of shipments as the vertical axis to obtain the warehouse goods classification results.

For example, there is correlation between warehouse goods, and warehouse goods with high correlation have similar entry and exit frequencies. Therefore, when allocating warehouse cargo spaces, goods with similar entry and exit frequencies are classified into one category for centralized allocation to facilitate the storage and retrieval of goods and improve operational efficiency. During the implementation process, warehouse goods can be divided into three categories, namely the first category, the second category and the third category, among which the inventory types of warehouse goods in the first category account for 10%-18% of the total inventory types, and the shipment volume accounts for 70%-80% of the total shipment volume; the inventory types of warehouse goods in the second category account for 25%-35% of the total inventory types, and the shipment volume accounts for 25%-35% of the total shipment volume; the inventory types of warehouse goods in the third category account for 70%-80% of the total inventory types, and the shipment volume accounts for 10%-18% of the total shipment volume; the inventory types of warehouse goods in the first category account for a small proportion, but the shipment proportion is large; the inventory types of warehouse goods in the second category account for more than the first category, but the shipment proportion is small in the first category; the inventory types of warehouse goods in the third category account for a large proportion, but the shipment proportion is small; the warehouse goods are managed in a key manner according to the warehouse goods classification results to improve work efficiency; among which, the process of calculating the shipment percentage of warehouse goods can be expressed by the following formula:

Among them, _bi is the percentage of warehouse goods shipped, x is the type of warehouse goods, and _yi is the shipment volume of warehouse goods.

In some possible implementations, the step 2 specifically includes the following steps:

Step 21: Collect the length, width and height of the warehouse goods, and calculate the volume of the warehouse goods according to the rectangular parallelepiped inclusion method;

Step 22: Calculate the number of warehouse shelves that each type of warehouse goods needs to occupy based on the volume of the warehouse goods, the difference coefficient, the size of the warehouse shelf grid, the average inventory during the statistical period, and the warehouse goods classification results obtained in step 1.

For example, warehouse shelves are all made in a uniform manner, and the cargo compartments of each shelf have a uniform size, but the specifications, shapes and sizes of the goods in the warehouse are uneven, and the storage quantity of each type of warehouse goods is also very different. Therefore, when determining the number of warehouse cargo compartments required for warehouse goods, in order to avoid wasting space, it is necessary to comprehensively consider the warehouse cargo compartment size, the size, shape and inventory quantity of the warehouse goods themselves. The principle of the rectangular containment method is that three-dimensional warehouse goods of different shapes can all be placed in a rectangular parallelepiped with a large enough volume. The length of the rectangular parallelepiped is L, the width is W, and the height is H. The volume of the rectangular parallelepiped V = L*W*H. Among them, the process of calculating the number of warehouse cargo compartments required for each type of warehouse goods can be expressed by the following formula:

Among them, _Sj represents the number of warehouse shelves that each type of warehouse goods needs to occupy, k is the difference coefficient, _Vij represents the volume of the i-th type of goods with goods category j calculated by the rectangular containment method, _Lij is the average inventory of the i-th type of goods with goods category j during the statistical period, V' is the volume of each warehouse shelf, and _xj is the number of all goods with goods category j in the warehouse.

In some possible implementations, step 3 specifically includes the following steps:

Step 31: Take the warehouse delivery platform as the warehouse cargo location origin coordinate, take the warehouse shelf row extension direction as the X-axis, the warehouse shelf column extension direction as the Y-axis, and the shelf layer extension direction as the Z-axis to establish a warehouse shelf coordinate system, and obtain the warehouse cargo location coordinate;

Step 32: Increase the weight coefficient a in the horizontal direction of the warehouse shelf and the weight coefficient b in the vertical direction, calculate the actual distance between the warehouse shelf storage position and the warehouse shelf origin coordinates according to the warehouse shelf coordinates, and sort them;

Step 33: Classify the warehouse cargo locations according to the sorting results of the actual distances, the number of warehouse cargo locations, and the warehouse cargo classification results to obtain the warehouse cargo location classification results.

For example, to improve the efficiency of warehousing and outbound operations, the time for goods to enter and exit the warehouse must be shortened, which is closely related to the distance moved by the warehousing and outbound operations. At the same time, considering the need for fitting allocation of goods and cargo locations, the cargo locations are classified according to the distance between the cargo location and the operating platform. According to the three types of goods in the first, second and third categories of warehouse goods, the warehouse cargo locations are divided into the first category warehouse cargo locations, the second category warehouse cargo locations and the third category warehouse cargo locations. The first category warehouse cargo locations are the closest to the outbound platform, and the third category warehouse cargo locations are the farthest from the outbound platform. In the actual warehouse operation process, it is impossible for goods to be transported from the origin to the cargo location or from the cargo location to the origin in a straight line, but it is determined by the coordinates of the cargo location, that is, the actual distance is the sum of the actual distances of the o row, p column and q layer. Taking into account the differences in the difficulty of cargo transportation in the horizontal and vertical directions, different weights are given to the horizontal and vertical distances when determining the actual distance between the origin and the cargo location:

L _{(o, p, q)} =a*S _o +d*S _p +b*S _q *(q _i -1)

Among them, L _{(o, p, q)} is the actual distance between the cargo coordinates and the cargo location origin coordinates, a is the weight coefficient in the horizontal direction of the warehouse shelf, b is the weight coefficient in the vertical direction of the warehouse shelf, and it satisfies a+b=1, S _o is the actual distance between each row of warehouse shelves, S _p is the actual distance between each column of warehouse shelves, and S _q is the actual distance between each layer of warehouse shelves.

In some possible implementations, step 4 specifically includes the following steps:

Step 41: Determine the priority of the goods leaving the warehouse according to the allocation coefficient of the goods in the warehouse, the frequency of the goods entering and leaving the warehouse, and the quality of the goods in the warehouse;

Step 42: Calculate the storage time of the warehouse goods to be allocated according to the priority of the warehouse goods, the access frequency of the warehouse goods, and the time of transporting the warehouse goods to the warehouse cargo location origin coordinates;

Step 43: Calculate the impact of the warehouse goods on the center of gravity of the warehouse shelves according to the mass of the warehouse goods and the number of layers on which the warehouse goods are placed;

Step 44: adding a weight coefficient c to the warehouse goods entry time calculated in step 42, adding a weight d to the influence value calculated in step 43, and fitting the warehouse goods entry time and the influence value after adding the weight coefficient to obtain the target warehouse cargo space allocation function;

Step 45: Calculate the target warehouse cargo location allocation function value of the warehouse cargo to be allocated, and use the warehouse cargo location coordinates corresponding to the minimum value of the target warehouse cargo location allocation function as the storage location of the warehouse cargo location to be allocated.

Exemplarily, the target warehouse cargo space allocation function is:

P＝cβ+dγ

Wherein, c and d are weight coefficients, and satisfy c+d=1;

After the goods are classified, the priority of each type of goods in and out of the warehouse is determined. After the cargo locations are classified, the priority of the cargo locations to accommodate the goods is determined. The fitting allocation of goods classification-cargo location classification ensures that the cargo locations closer to the outbound platform are allocated to key goods first to a certain extent. The higher the frequency of in and out of the warehouse and the greater the quality of the goods, the greater the impact on the efficiency and the center of gravity of the warehouse shelves when they are allocated to the same cargo location. In order to ensure that warehouse goods with high frequency and high quality are allocated to the best warehouse cargo location first, a distribution coefficient α is set for each type of goods. The process of determining the priority of warehouse goods can be expressed by the following formula:

α _i = _fi * m _i

Among them, _αi is the distribution coefficient of the i-th type of goods, _fi is the frequency of entry and exit of the i-th type of goods, and _mi is the mass of the i-th type of goods. It can be seen that the size of the distribution coefficient is determined by the product of the frequency of entry and exit and the mass of the goods.

The entry and exit of goods is the most tedious and labor-intensive part of warehouse cargo storage management. To improve the warehouse's operating efficiency, it is necessary to reduce the time of warehouse goods entering and exiting, that is, to reduce the handling time of warehouse goods. The process of calculating the entry time of warehouse goods to be allocated can be expressed by the following formula:

β＝f*T _iopq

Where f is the access frequency of all goods, and _Tiopq is the transportation time of the i-th type of goods assigned to the cargo location with coordinates (o, p, q).

T _iopq =(o*S _o +p*S _p )/v _H +(q-1)*S _q /v _Y

The process of calculating the impact of warehouse goods on the center of gravity of warehouse shelves can be expressed by the following formula:

γ＝m*(q _i -1)*S _q

Among them, m is the mass of the goods, and _qi is the number of layers where the i-th goods are placed.

The processor involved in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

The memory involved in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual fitting or direct fitting or communication connection shown or discussed can be an indirect fitting or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one device or distributed on multiple devices. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one device, or each module may exist physically separately, or two or more modules may be integrated into one device.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or may contain one or more servers, data centers and other data storage devices that can be integrated with a medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (7)

1. The warehouse goods location allocation method is characterized in that warehouse goods and warehouse goods locations are classified to determine the priority of the warehouse goods, allocation coefficients are set according to the priority of the warehouse goods, the warehouse in and out time of the warehouse goods is fitted with the influence value of the warehouse goods on the center of gravity of a goods shelf to obtain a target warehouse goods location allocation function, and a warehouse goods location coordinate corresponding to the minimum value of the target warehouse goods location allocation function is used for indicating the storage position of the warehouse goods location to be allocated;

the warehouse goods space allocation method comprises the following steps:

step 1: counting and sequencing the collected inventory types and the collected shipment quantity to obtain a warehouse goods classification result;

step 2: calculating the volume of the warehouse goods according to a cuboid containment method, and calculating the number of warehouse goods positions required to be occupied by each type of warehouse goods by combining the size of the warehouse shelf goods grid and the classification result of the warehouse goods obtained in the step 1;

and step 3: establishing a warehouse goods shelf coordinate system by taking the warehouse delivery platform as the origin of the warehouse goods positions to obtain coordinates of the warehouse goods positions, and classifying the warehouse goods positions according to the classification results of the warehouse goods and the number of the warehouse goods positions to obtain classification results of the warehouse goods positions;

and 4, step 4: fitting and distributing the obtained classification result of the warehouse goods and the classification result of the warehouse goods to realize that the goods position close to the delivery platform is preferentially distributed to the key goods, determining the priority of the warehouse goods by combining the set distribution coefficient, fitting the delivery time of the warehouse goods and the influence value of the warehouse goods on the center of gravity of the goods shelf according to the priority of the warehouse goods to obtain a target distribution function of the warehouse goods position, and taking the coordinates of the warehouse goods position corresponding to the minimum value of the target distribution function of the warehouse goods position as the storage position of the warehouse goods position to be distributed;

the step 4 specifically comprises the following steps:

step 41: determining the priority of the warehouse goods according to the distribution coefficient of the warehouse goods, the in-out frequency of the warehouse goods and the quality of the warehouse goods;

step 42: calculating the warehousing time of the warehouse goods to be distributed according to the priority of the warehouse goods, the access frequency of the warehouse goods and the time of the warehouse goods to be transported to the origin coordinates of the warehouse goods space;

step 43: calculating the influence value of the warehouse goods on the center of gravity of the warehouse goods shelf according to the quality of the warehouse goods and the number of layers of the warehouse goods;

and step 44: adding a weight coefficient c to the warehouse goods warehousing time calculated in the step 42, adding a weight d to the influence value calculated in the step 43, and fitting the warehouse goods warehousing time and the influence value after the weight coefficient is added to obtain a target warehouse goods location distribution function;

step 45: and calculating a target warehouse goods location distribution function value of the warehouse goods to be distributed, and taking the warehouse goods location coordinate corresponding to the minimum value of the target warehouse goods location distribution function as the storage position of the warehouse goods location to be distributed.

2. The warehouse cargo space allocation method according to claim 1, wherein the step 1 specifically comprises the following steps:

step 11: setting a statistical period, and collecting the stock types and the shipment quantity of all warehouse goods in the statistical period;

step 12: calculating the shipment volume percentage of the warehouse goods according to the collected inventory types and the shipment volume and sequencing according to the shipment volume percentage;

step 13: and drawing a warehouse goods classification statistical chart by taking the accumulated percentage of the warehouse goods as an abscissa and the accumulated percentage of the shipment quantity as an ordinate according to the sorting result to obtain a warehouse goods classification result.

3. The warehouse cargo space allocation method according to claim 2, wherein the step 2 specifically comprises the following steps:

step 21: collecting the length, width and height of the warehouse goods, and calculating the volume of the warehouse goods according to a cuboid containment method;

step 22: and (3) calculating the number of the warehouse goods positions required to be occupied by each type of warehouse goods according to the volume, the difference coefficient, the size of the warehouse shelf goods grids, the average stock in the statistical period and the classification result of the warehouse goods obtained in the step (1).

4. The warehouse cargo space allocation method according to claim 3, wherein the step 3 specifically comprises the following steps:

step 31: establishing a warehouse shelf coordinate system by taking a warehouse delivery platform as a warehouse goods location original point coordinate, taking the extending direction of warehouse shelf rows as an X axis, the extending direction of warehouse shelf columns as a Y axis and the extending direction of shelf layers as a Z axis to obtain a warehouse goods location coordinate;

step 32: adding a weight coefficient a in the horizontal direction and a weight coefficient b in the vertical direction of the warehouse shelf, calculating the actual distance between the storage position of the warehouse goods location and the original point coordinate of the warehouse goods location according to the coordinates of the warehouse goods location, and sequencing;

step 33: and classifying the warehouse goods positions according to the sorting result of the actual distance, the warehouse goods position number and the warehouse goods classification result to obtain a warehouse goods position classification result.

5. The method as claimed in claim 4, wherein the step 41 of determining the priority of the warehouse goods comprises the following specific operations:

α _i ＝f _i *m _i

wherein alpha is _i Distribution coefficient for i-th cargo, f _i The in-out frequency m of the ith cargo _i The quality of the ith cargo;

the specific operation of calculating the warehousing time of the warehouse goods to be distributed is as follows:

T _iopq ＝(o*S _o +p*S _p )/v _H +(q-1)*S _q /v _Y

β＝f*T _iopq

wherein f is the access frequency of all goods, T _iopq Assigning a transfer time to a cargo space with coordinates (o, p, q) for the ith cargo, S _o As the actual distance between each row of the warehouse rack, S _p As the actual distance between each column of the warehouse rack, S _q Is the actual distance between each layer of the warehouse rack, v _H A conveying speed in the horizontal direction, v _Y A transport speed in the vertical direction;

the concrete operation of calculating the influence value of the warehouse goods on the center of gravity of the warehouse shelf is as follows:

γ＝m*(q _i -1)*S _q

wherein m is the mass of the cargo, S _q Is the actual distance between each layer of the warehouse rack, q _i The coordinates of the cargo space of the layer number of the ith cargo;

the target warehouse cargo space allocation function is as follows:

P＝cβ+dγ

wherein c and d are weight coefficients, beta is the warehousing time of the warehouse goods to be distributed, and gamma is the influence value of the warehouse goods on the center of gravity of the warehouse shelf.

6. An electronic device comprising a memory, a processor;

the memory for storing a computer program; the processor, when executing the computer program, implements the warehouse slot allocation method as claimed in any of claims 1-5.

7. A computer-readable storage medium having stored thereon instructions for implementing the warehouse slot allocation method of any of claims 1-5 when the instructions are executed on the electronic device of claim 6.

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