CN115578039B - Warehouse goods space allocation method, electronic equipment and computer storage medium - Google Patents
Warehouse goods space allocation method, electronic equipment and computer storage medium Download PDFInfo
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Abstract
The embodiment of the application provides a warehouse goods space allocation method, electronic equipment and a computer storage medium, and is applied to the field of logistics scheduling. In the warehouse goods space allocation method, collected inventory types and shipment quantities are subjected to statistical sequencing to obtain a warehouse goods classification result. And then calculating the volume of the warehouse goods and the number of the warehouse goods positions occupied by each type of warehouse goods according to a cuboid containment method. And then establishing a warehouse shelf coordinate system by taking the warehouse delivery platform as the origin of the warehouse goods space to obtain the coordinates of the warehouse goods space, and classifying to obtain the classification result of the warehouse goods space. And finally, fitting the obtained warehouse goods classification result and the warehouse goods location classification result to obtain a target warehouse goods location distribution function, 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. According to the embodiment of the application, the warehouse operation efficiency is improved, the warehouse operation time is shortened, and meanwhile the space utilization rate of the warehouse is also improved.
Description
Technical Field
The application relates to the technical field of warehouse logistics scheduling, in particular to a warehouse goods location distribution method, electronic equipment and a computer storage medium.
Background
Automated storage/retrieval systems (AS/RS) are important components of modern warehouse management systems. The stereoscopic warehouse has the functions of storage, management, freight transportation, scheduling and the like, can obviously improve the utilization rate of the area and the space utilization rate of the warehouse and reduce the management cost, and is beneficial to realizing large-scale goods storage and efficient logistics transportation, thereby meeting the requirements of modern production and living. The research of the scheduling system is the key point of the design of the stereoscopic warehouse, and the core of the scheduling algorithm is to reduce the warehouse-in and warehouse-out time, accelerate the turnover rate and maintain the stability of the goods shelf.
The characteristics of the goods stored in the warehouse determine the scale of the warehouse and the operation mode of the warehouse, the unreasonable distribution mode of the goods in the warehouse directly causes the problems of low efficiency of warehouse in and out operation and long time for warehouse goods in and out, and under the condition of certain warehouse scale, good distribution scheduling of goods positions in the warehouse can greatly improve the space utilization rate of the warehouse, shorten the operation time of the warehouse and improve the operation efficiency of the warehouse.
Disclosure of Invention
The embodiment of the application provides a warehouse goods space allocation method, electronic equipment and a computer storage medium, and the warehouse space utilization rate is improved while the warehouse operation efficiency is improved and the warehouse operation time is shortened.
In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:
according to the first aspect, warehouse goods and warehouse goods locations are classified, the priority of the warehouse goods is determined, a distribution coefficient is set, the time of the warehouse goods entering and leaving the warehouse goods is fitted with the influence value of the warehouse goods on the center of gravity of a goods shelf according to the priority of the warehouse goods, a target warehouse goods location distribution function is obtained, and the warehouse goods location coordinate corresponding to the minimum value of the target warehouse goods location distribution function is used for indicating the storage position of the warehouse goods locations to be distributed.
In one possible embodiment, the warehouse 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;
and 2, step: 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: and fitting the obtained classification result of the warehouse goods and the classification result of the warehouse goods positions, determining the priority of the warehouse goods according to the set distribution coefficient to obtain a target warehouse goods position distribution function, and taking the coordinates of the warehouse goods positions corresponding to the minimum value of the target warehouse goods position distribution function as the storage positions of the warehouse goods positions to be distributed.
In a possible embodiment, step 1 specifically includes 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.
In a possible embodiment, step 2 specifically includes 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).
In a possible implementation, step 3 specifically includes 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 a warehouse shelf row extending direction as an X axis, taking a warehouse shelf column extending direction as a Y axis and taking a shelf layer extending direction 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.
In a possible implementation, step 4 specifically includes the following steps:
step 41: determining the priority of the warehouse goods according to the distribution coefficient of the warehouse goods, the warehouse-in and warehouse-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 for transporting the warehouse goods to the origin coordinates of the warehouse goods space;
step 43: calculating the influence value of the warehouse goods on the gravity center of a warehouse shelf according to the quality of the warehouse goods and the number of layers of the warehouse goods;
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.
In a second aspect, an embodiment of the present application further provides an electronic device, including a memory and a processor;
a memory for storing a computer program; when the processor executes the computer program, the method for allocating the warehouse goods space is realized.
In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement the above-mentioned warehouse goods allocation method.
The technical effects of the second and third aspects are described in the same manner as in the first aspect.
Drawings
Fig. 1 is a schematic block diagram of a flow of a warehouse cargo space allocation method according to an embodiment of the present disclosure.
Detailed Description
It should be noted that the terms "first", "second", and the like, referred to in the embodiments of the present application, are used only for distinguishing the same type of features, and are not to be construed as indicating relative importance, quantity, order, and the like.
Reference to the terms "exemplary" or "such as" in embodiments of the present application is used to indicate that an example, instance, or illustration is intended. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present relevant concepts in a concrete fashion.
The terms "fit" and "connect" as used in the embodiments of the present application should be broadly construed, and may refer to, for example, a physical direct connection, or an indirect connection via an electronic device, such as a connection via a resistor, an inductor, a capacitor, or other electronic devices.
The embodiment of the application provides a warehouse goods space allocation method, which classifies warehouse goods and warehouse goods spaces, determines the priority of the warehouse goods, sets an allocation coefficient, fits the warehouse-in and warehouse-out time of the warehouse goods and the influence value of the warehouse goods on the center of gravity of a goods shelf according to the priority of the warehouse goods, and obtains a target warehouse goods space allocation function, wherein the warehouse goods space coordinates corresponding to the minimum value of the target warehouse goods space allocation function are used for indicating the storage position of the warehouse goods space to be allocated.
In some possible embodiments, as shown in fig. 1, the method comprises the steps of:
step 1: counting and sequencing the collected inventory types and the collected shipment quantity to obtain a warehouse goods classification result;
and 2, step: calculating the volume of the warehouse goods according to a cuboid containment method, and calculating the number of warehouse goods positions occupied by each type of warehouse goods according to the size of the warehouse shelf goods grid and the classification result of the warehouse goods obtained in the step 1;
and 3, step 3: establishing a warehouse shelf coordinate system by taking a warehouse delivery platform as a warehouse goods location original point to obtain a warehouse goods location coordinate, and classifying the warehouse goods locations according to a warehouse goods classification result and the number of the warehouse goods locations to obtain a warehouse goods location classification result;
and 4, step 4: and fitting the obtained classification result of the warehouse goods and the classification result of the warehouse goods positions, determining the priority of the warehouse goods according to the set distribution coefficient to obtain a target warehouse goods position distribution function, and taking the coordinates of the warehouse goods positions corresponding to the minimum value of the target warehouse goods position distribution function as the storage positions of the warehouse goods positions to be distributed.
In some possible embodiments, the step 1 specifically includes 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.
Illustratively, the warehouse goods have correlation, and the warehouse goods with high correlation have similar in-out frequency, so when the warehouse goods allocation is carried out, the goods with similar in-out frequency are classified into one class for centralized allocation, so that the goods can be conveniently stored and taken, and the operation efficiency is improved. In the implementation process, the warehouse goods can be divided into a first large category, a second large category and a third large category, wherein the stock category of the first large category warehouse goods accounts for 10% -18% of the total stock category, and the shipment accounts for 70% -80% of the total shipment; the stock category of the second large-class warehouse goods accounts for 25% -35% of the total stock category, and the shipment amount accounts for 25% -35% of the total shipment amount; the stock type of the third major type of warehouse goods accounts for 70-80% of the total stock type, and the shipment accounts for 10-18% of the total shipment; the stock category proportion of the first major category of warehouse goods is small, but the shipment proportion is large; the second large category of warehouse goods has more stock categories than the first large category, but the shipment categories are smaller than the first large category; the stock category proportion of the third major category warehouse goods is large, but the shipment proportion is small; the method has the advantages that the key management is carried out on the warehouse goods according to the sorting result of the warehouse goods, so that the working efficiency is improved; the process of calculating the shipment volume percentage of the warehouse goods can be expressed by the following formula:
wherein, b i Is the percentage of shipment of the warehouse cargo, x is the type of warehouse cargo, y i The shipment volume of the warehouse goods.
In some possible embodiments, the step 2 specifically includes 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).
Illustratively, warehouse shelves are manufactured uniformly, the shelf grids of each shelf are uniform in size, but the specifications, shapes and sizes of goods in the warehouse are different, and the storage quantity of the goods in each warehouse is greatly different, so that when the quantity of the warehouse goods required by the warehouse goods is determined, the size of the warehouse goods, the size, the shape and the storage quantity of the warehouse goods need to be comprehensively considered to avoid space waste. The principle of the cuboid containment method is that goods in three-dimensional warehouses with different shapes can be placed into a cuboid with a large enough volume, the length of the cuboid is L, the width of the cuboid is W, the height of the cuboid is H, and the volume of the cuboid is V = L × W × H. The process of calculating the number of warehouse cargo positions that each type of warehouse cargo needs to occupy can be represented by the following formula:
wherein S is j Representing the number of warehouse goods positions occupied by each type of warehouse goods, k is a difference coefficient, V ij Indicating the volume of the ith cargo of which the cargo category is j and calculated by a cuboid containment method, L ij The average stock of the ith goods with the goods category j in the statistical period is V' the volume of each warehouse goods grid, x j All the goods of the warehouse goods category j are counted.
In some possible embodiments, the step 3 specifically includes 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 a warehouse shelf row extending direction as an X axis, taking a warehouse shelf column extending direction as a Y axis and taking a shelf layer extending direction 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 space and the original point coordinate of the warehouse goods space according to the coordinates of the warehouse goods space, 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.
For example, to improve the efficiency of the loading and unloading operation, the time for loading and unloading the goods is shortened, which is closely related to the moving distance of the loading and unloading operation. Meanwhile, considering the requirement of goods-goods position fitting distribution, classifying the goods positions according to the distance between the goods positions and the operation table, and dividing the warehouse goods positions into a first large type warehouse goods position, a second large type warehouse goods position and a third large type warehouse goods position according to three kinds of goods of a first large type, a second large type and a third large type of warehouse goods, wherein the first large type warehouse goods position is closest to the delivery table, and the third large type warehouse goods position is farthest from the delivery table. In consideration of the difference in the ease of work of cargo transportation in the horizontal and vertical directions, different weights are given to the horizontal and vertical distances, respectively, in determining the actual distance between the origin and the cargo space:
L (o，p，q) ＝a*S o +d*S p +b*S q *(q i -1)
wherein L is (o,p,q) The actual distance between the goods coordinate and the goods location origin coordinate is shown as a, a is the weight coefficient of the warehouse shelf in the horizontal direction, b is the weight coefficient of the warehouse shelf in the vertical direction, and the sum of a + b =1,S is satisfied 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 The actual distance between each layer of the warehouse shelf.
In some possible embodiments, the step 4 specifically includes the following steps:
step 41: determining the priority of the warehouse goods according to the distribution coefficient of the warehouse goods, the warehouse-in and warehouse-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 for transporting the warehouse goods 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;
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 cargo space distribution function value of the warehouse cargo to be distributed, and taking the warehouse cargo space coordinate corresponding to the minimum value of the target warehouse cargo space distribution function as the storage position of the warehouse cargo space to be distributed.
Illustratively, the target warehouse slot allocation function is:
P＝cβ+dγ
wherein c and d are weight coefficients and satisfy c + d =1;
the goods are classified, the in-out priority of each type of goods is determined, the goods positions are classified, the priority of the goods accommodated in the goods positions is determined, and the goods position close to the delivery platform is guaranteed to be preferentially allocated to the key goods through the matching allocation of the goods classification-goods position classification to a certain extent. The higher the warehousing frequency is, the larger the influence of the goods with larger quality on the efficiency and the center of gravity of the warehouse shelf in the process of distributing to the same goods location is, in order to ensure that the warehouse goods with high frequency and large quality are preferentially distributed to the optimal warehouse goods location, a distribution coefficient alpha is set for the goods of each variety, and the process of determining the priority of the warehouse goods can be represented by the following formula:
α i ＝f i *m i
wherein alpha is i Distribution coefficient for i-th cargo, f i Frequency of entry and exit of ith cargo, m i The mass of the ith goods; it can be seen that the magnitude of the distribution coefficient is determined by the product of the in-out frequency and the cargo mass.
The warehouse entry and exit of the goods is the most complicated part and the largest workload in the warehouse goods location management, the improvement of the operation efficiency of the warehouse needs to reduce the warehouse entry and exit time of the warehouse goods, namely, the carrying time of the warehouse goods is reduced, and the process of calculating the warehouse entry time of the warehouse goods to be distributed can be represented by the following formula:
β＝f*T iopq
wherein f is the access frequency of all goods, T iopq The transport time allocated to the cargo space with coordinates (o, p, q) is assigned to the ith cargo.
T iopq ＝(o*S o +p*S p )/v H +(q-1)*S q /v Y
The process of calculating the influence value of the warehouse goods on the center of gravity of the warehouse rack can be expressed by the following formula:
γ＝m*(q i -1)*S q
wherein m is the mass of the goods, q i The number of layers for the ith cargo.
The processor related to the embodiment of the application may be a chip. For example, the Field Programmable Gate Array (FPGA) may be a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), a system on chip (SoC), a Central Processing Unit (CPU), a Network Processor (NP), a Digital Signal Processing (DSP), a Microcontroller (MCU), a Programmable Logic Device (PLD) or other integrated chips.
The memory referred to in embodiments of the application may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), dynamic random access memory (dynamic RAM, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SLDRAM (synchronous DRAM), and direct rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the module described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another device, or some features may be omitted, or not executed. In addition, the shown or discussed fitting or direct fitting or communication connection between each other may be an indirect fitting or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one device, or may be 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, functional modules in the embodiments of the present application may be integrated into one device, or each module may exist alone physically, or two or more modules may be integrated into one device.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may 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. The procedures or functions described in accordance with the embodiments of the present application are all or partially generated upon loading and execution of computer program instructions on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. 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 one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or can comprise one or more data storage devices, such as servers, data centers, and the like, that can be integrated with the medium. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to 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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