Warehouse goods allocation distribution method, system and equipment and storage medium

Abstract

The invention provides a storage goods allocation distribution method, system and device and a storage medium, and belongs to the technical field of power grid material warehousing, and the method comprises the steps: obtaining the warehouse-in and warehouse-out frequency of an electric power tool according to the historical warehouse-out information of the electric power tool, and building a target function for reducing the warehouse-in and warehouse-out carrying time; counting the weight of the electric tool, and establishing a target function for reducing the shelf gravity center; constructing a storage goods allocation multi-objective function based on the objective function for reducing the warehouse-in and warehouse-out carrying time and the objective function for reducing the gravity center of the goods shelf; and solving the storage goods allocation multi-objective function through a preset algorithm to obtain an optimal goods allocation result. By solving the storage goods allocation multi-objective function, an optimal solution is obtained, and the optimal solution is an optimal storage goods allocation scheme, so that the warehouse-in and warehouse-out carrying time is shortest, the gravity center of a goods shelf is lowest, the goods picking efficiency is improved, and the stability of the goods shelf is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid material storage, and in particular relates to a storage cargo space allocation method, system, equipment and storage medium. Background Art

[0002] In order to facilitate the management of power tools, power tools are uniformly stored in the cargo space of the power grid material warehouse. However, with the continuous expansion of the types and management scale of power grid materials, the requirements for the intelligent level of warehousing operations are constantly increasing. The current cargo space allocation still has the following shortcomings: On the one hand, the unreasonable allocation of cargo space leads to a long picking time and low picking efficiency; On the other hand, due to the unreasonable allocation of cargo space, the stability of the shelves is also poor.

[0003] To this end, the present invention provides a storage space allocation method, system, device and storage medium. Summary of the invention

[0004] The present invention provides a warehouse cargo space allocation method, system, device and storage medium to at least solve the problems in the prior art that the cargo space allocation is not rational, resulting in a long picking time and low picking efficiency, and the stability of the shelf is also poor due to the unreasonable cargo space allocation.

[0005] In a first aspect, an embodiment of the present invention provides a storage space allocation method, the storage space allocation method comprising: The frequency of in-and-out of power tools is obtained based on the historical out-and-out information of power tools, and an objective function for reducing the in-and-out handling time is established; Count the weight of power tools and establish an objective function to lower the center of gravity of the shelf; A multi-objective function for warehouse storage allocation is constructed based on the objective function of reducing the time of inbound and outbound transportation and the objective function of reducing the center of gravity of the shelf; The multi-objective function of storage location allocation is solved by the preset algorithm to obtain the optimal location allocation result. The picking time from each location where power tools are stored to the storage area exit and the total center of gravity of the shelf are obtained; the multi-objective function of storage location allocation is established with the shortest total picking time and the lowest total center of gravity of the shelf as the goal; the multi-objective function of storage location allocation is solved to obtain the optimal multi-objective storage location allocation plan; each location where power tools are stored is reasonably allocated, the picking time is shortened, the picking efficiency is improved, and the stability of the shelf is improved.

[0006] Furthermore, the objective function of reducing the time of inbound and outbound transportation is expressed as:

[0007] In the formula, Indicates the layer where the power tool is located. , Indicates the column where the power tool is located. , Indicates layer The frequency of entering and leaving the warehouse of the power tools listed, Indicates the vertical running speed of the AGV car. Indicates the horizontal running speed of the AGV car. Indicates the lateral running speed of the AGV car. Indicates the length of the cargo space. Indicates the height of the cargo space. Indicates the depth of the cargo space.

[0008] Furthermore, the objective function of reducing the center of gravity of the shelf is expressed as:

[0009] In the formula, Indicates layer The weight of the power tool.

[0010] Furthermore, the expression of the multi-objective function of storage location allocation is:

[0011] In the formula, represents the single objective function category, , represents the weight of a single objective function, , represents the objective function after a power tool is assigned, Represents the objective function The optimal value of .

[0012] Furthermore, the preset algorithm is: genetic algorithm.

[0013] Furthermore, the multi-objective function of storage location allocation is solved by a preset algorithm to obtain the optimal location allocation result, including the following specific steps: Step 1: Set the evolution iteration counter , set the maximum number of iterations , randomly generated individuals as the initial population ; Step 2: Calculate the current population based on the multi-objective function of warehouse storage allocation The fitness value of each individual in , find the individual with the largest fitness value, and record this individual as the current optimal individual; Step 3: In the current population In the process, the proportional selection method is used for selection operation, the single-point crossover method is used for crossover operation, and the uniform mutation operator is used for mutation operation to obtain a new generation of population; Step 4: Calculate the new generation population based on the multi-objective function of warehouse storage allocation The fitness value of each individual in the population is calculated, and the individual with the largest fitness value is found as the optimal individual of the population; the fitness value of the optimal individual of the population is compared with the fitness value of the current optimal individual, and the individual with the larger fitness value between the fitness value of the optimal individual of the population and the fitness value of the current optimal individual is taken as the current optimal individual; the number of iterations is increased by 1; Step 5: Determine whether the number of iterations has reached the maximum number of iterations ; If the maximum number of iterations is not reached , then return to step 3; If the maximum number of iterations is reached , then execute step 6: output the current optimal individual and obtain the corresponding storage location allocation plan. By solving the multi-objective function of storage location allocation, an optimal solution is obtained. The optimal solution is the optimal storage location allocation plan, which makes the in-and-out handling time the shortest and the shelf center of gravity the lowest, which not only improves the picking efficiency but also ensures the stability of the shelf.

[0014] Furthermore, the maximum number of iterations Set to 400~500.

[0015] In a second aspect, an embodiment of the present invention further provides a storage cargo space allocation system applied to the storage cargo space allocation method as described in the above aspects, the system comprising: An efficiency model building unit, used to obtain the in-and-out frequency of the power tools according to the historical out-and-out information of the power tools, and to establish an objective function for reducing the in-and-out handling time; A center of gravity model building unit is used to calculate the weight of power tools and establish an objective function for lowering the center of gravity of the shelf; A multi-objective storage cargo space allocation model building unit is used to build a multi-objective function for storage cargo space allocation based on the objective function of reducing the in-and-out handling time and the objective function of reducing the center of gravity of the shelf; The cargo space allocation result acquisition unit is used to solve the multi-objective function of storage cargo space allocation through a preset algorithm to obtain the optimal cargo space allocation result.

[0016] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the warehouse storage location allocation method as described in the above aspects are implemented.

[0017] In a fourth aspect, a storage medium stores a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the warehouse storage location allocation method as described in the above aspects.

[0018] It can be seen from the above technical solutions that the present invention has the following advantages: The storage cargo space allocation method provided by the present invention obtains the picking time from each cargo space for storing power tools to the storage area exit and the total center of gravity of the shelf; establishes a multi-objective function for storage cargo space allocation with the shortest total picking time and the lowest total center of gravity of the shelf as the goals; solves the multi-objective function for storage cargo space allocation to obtain the optimal multi-objective storage cargo space allocation plan; so that each cargo space for storing power tools is reasonably allocated, the picking time is shortened, the picking efficiency is improved, and the stability of the shelf is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 The figure is a schematic diagram of the method flow of the warehouse storage location allocation method of the present invention. DETAILED DESCRIPTION

[0021] Various embodiments of the present disclosure will be described more fully in the following detailed description of the storage cargo allocation method, system, device and storage medium. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but rather the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0022] Hereinafter, the terms "include" or "may include" used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or a combination of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or a combination of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or a combination of the foregoing.

[0023] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the words listed at the same time. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0024] The expressions (such as "first", "second", etc.) used in the various embodiments of the present disclosure may modify the various constituent elements in the various embodiments, but may not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only used for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of the present disclosure, the first element may be referred to as the second element, and similarly, the second element may also be referred to as the first element.

[0025] It should be noted that if it is described that one component element is “connected” to another component element, the first component element may be directly connected to the second component element, and a third component element may be “connected” between the first component element and the second component element. Conversely, when one component element is “directly connected” to another component element, it can be understood that there is no third component element between the first component element and the second component element.

[0026] The term “user” used in various embodiments of the present disclosure may indicate a person who uses an electronic device, and may be a monitoring person, a test person, or an operator.

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The embodiment of the present invention provides a method for allocating storage cargo spaces, which solves the currently urgently needed technical problems that the existing cargo space allocation is not rational, resulting in a long picking time and low picking efficiency, and the unreasonable cargo space allocation leads to poor shelf stability.

[0029] The technical solution proposed in the embodiment of the present invention is described in detail below with reference to the accompanying drawings.

[0030] Figure 1 A flow chart of a storage space allocation method provided by an embodiment of the present invention. Figure 1 As shown, a storage location allocation method provided by an embodiment of the present invention specifically includes the following steps: A method for allocating storage space, comprising: The frequency of in-and-out of power tools is obtained based on the historical out-and-out information of power tools, and an objective function for reducing the in-and-out handling time is established; Count the weight of power tools and establish an objective function to lower the center of gravity of the shelf; A multi-objective function for warehouse storage allocation is constructed based on the objective function of reducing the time of inbound and outbound transportation and the objective function of reducing the center of gravity of the shelf; The multi-objective function of warehouse storage location allocation is solved through a preset algorithm to obtain the optimal storage location allocation result.

[0031] The storage cargo space allocation method of the present invention obtains the picking time from each cargo space for storing power tools to the storage area exit and the total center of gravity of the shelf; establishes a multi-objective function for storage cargo space allocation with the shortest total picking time and the lowest total center of gravity of the shelf as the goals; solves the multi-objective function for storage cargo space allocation to obtain the optimal multi-objective storage cargo space allocation plan; so that each cargo space for storing power tools is reasonably allocated, the picking time is shortened, the picking efficiency is improved, and the stability of the shelf is improved.

[0032] In an exemplary embodiment, the objective function for reducing the inbound and outbound handling time is expressed as:

[0033] In the formula, Indicates the layer where the power tool is located. , Indicates the column where the power tool is located. , Indicates layer The frequency of entering and leaving the warehouse of the power tools listed, Indicates the vertical running speed of the AGV car. Indicates the horizontal running speed of the AGV car. Indicates the lateral running speed of the AGV car. Indicates the length of the cargo space. Indicates the height of the cargo space. Indicates the depth of the cargo space.

[0034] According to yet another embodiment of the present invention, the objective function for lowering the center of gravity of the shelf is expressed as:

[0035] In the formula, Indicates layer The weight of the power tool.

[0036] According to an embodiment of the present invention, the expression of the multi-objective function of storage location allocation is:

[0037] In the formula, represents the single objective function category, , Indicates that the single objective function category is the objective function of reducing the time of inbound and outbound transportation. Indicates that the single objective function category is the objective function of lowering the center of gravity of the shelf, represents the weight of a single objective function, ,and and are all constants greater than 0. represents the weight of the objective function whose single objective function category is to reduce the time of handling in and out of the warehouse, represents the weight of the objective function whose single objective function category is to reduce the center of gravity of the shelf, represents the objective function after a power tool is assigned, Represents the objective function In this embodiment, , .

[0038] As an example, the preset algorithm is a genetic algorithm. The genetic algorithm uses the process of biological evolution to solve problems. It gradually finds better solutions in the population through operations such as crossover and mutation, and eliminates poor solutions. After multiple evolutions, it obtains an approximate optimal solution. It includes: coding design, population initialization, fitness function establishment, operator design and control parameter termination conditions.

[0039] Coding design is one of the key steps in the design of genetic algorithms. It is a bridge for the conversion between solution space and problem space. The coding method directly affects the operation results of genetic algorithms. Currently, the commonly used coding methods are real number coding, binary coding, floating point coding, etc. Through coding, the cargo information is represented as chromosomes, and the cargo information is used as a research variable to participate in subsequent operations. In the established multi-objective function of warehouse cargo location allocation, the cargo location coordinates are encoded. In the selection of the coding method, binary coding needs to be decoded, which is cumbersome. Therefore, the decimal integer coding method is selected to encode the cargo location coordinates. When integer coding is used, a gene in the chromosome stores a cargo location information, and the cargo location coordinates are three-dimensional, so each gene is represented by 3 integers. In addition, the gene content and quantity of each chromosome correspond to the cargo location allocation plan.

[0040] The population size has a certain impact on the speed and accuracy of the calculation. When the population size is small, the calculation will end too quickly and the result cannot be guaranteed to be the global optimal solution. When the population size is too large, the difficulty of the calculation will increase significantly, the number of iterations will increase, and the calculation time and difficulty will also increase significantly.

[0041] The role of the fitness function is to set standards and eliminate unsuitable individuals. The higher the fitness function value, the greater the probability of being retained. In the process of cargo allocation, the objective function pursues the minimum value such as the shortest time and the lowest center of gravity, while the fitness function needs to solve the maximum value.

[0042] Operator design mainly includes selection operator, crossover operator, and mutation operator design. In view of the characteristic of the selection operator to eliminate poor individuals, the proportional selection method is used to select individuals in the process of solving the model. The individuals with higher fitness have a greater probability of being retained; for the crossover operator, a single-point crossover method is used in the process of solving the model, and a point in the two chromosome bit strings is randomly selected for crossover operation and gene exchange to become a new individual; for the mutation operator, the uniform mutation operator is used for mutation operation to increase individual diversity and avoid premature convergence of the calculation process.

[0043] In one embodiment, solving the multi-objective function of storage location allocation by a preset algorithm to obtain the optimal location allocation result includes the following specific steps: Step 1: Set the evolution iteration counter , , set the maximum number of iterations , randomly generated individuals as the initial population ; Step 2: Calculate the current population based on the multi-objective function of warehouse storage allocation The fitness value of each individual in , find the individual with the largest fitness value, and record this individual as the current optimal individual; Step 3: In the current population In the process, the proportional selection method is used for selection operation, the single-point crossover method is used for crossover operation, and the uniform mutation operator is used for mutation operation to obtain a new generation of population; It should be noted that the selection operation using the proportional selection method is based on the fitness evaluation of individuals in the group. The selection operator is applied to the group to select superior individuals from the group and eliminate inferior individuals. The crossover operation using the single-point crossover method is to apply the crossover operator to the group, replace and recombine part of the structure of the two parent individuals to generate new individuals. The mutation operation using the uniform mutation operator is to apply the mutation operator to the group to change the gene values ​​on some loci of the individual strings in the group.

[0044] Step 4: Calculate the new generation population based on the multi-objective function of warehouse storage allocation The fitness value of each individual in the population is calculated, and the individual with the largest fitness value is found as the optimal individual of the population; the fitness value of the optimal individual of the population is compared with the fitness value of the current optimal individual, and the individual with the larger fitness value between the fitness value of the optimal individual of the population and the fitness value of the current optimal individual is taken as the current optimal individual; the number of iterations is increased by 1; Step 5: Determine whether the number of iterations has reached the maximum number of iterations ; If the maximum number of iterations is not reached , then return to step 3; If the maximum number of iterations is reached , then execute step 6: output the current optimal individual and obtain the corresponding warehouse storage location allocation plan.

[0045] By solving the multi-objective function of storage location allocation, an optimal solution is obtained. This optimal solution is the optimal storage location allocation plan, which shortens the in-and-out handling time and minimizes the center of gravity of the shelf, which not only improves the picking efficiency but also ensures the stability of the shelf.

[0046] The storage cargo space allocation method of the present invention obtains the picking time from each cargo space for storing power tools to the storage area exit and the total center of gravity of the shelf; establishes a multi-objective function for storage cargo space allocation with the shortest total picking time and the lowest total center of gravity of the shelf as the goals; solves the multi-objective function for storage cargo space allocation to obtain the optimal multi-objective storage cargo space allocation plan; so that each cargo space for storing power tools is reasonably allocated, the picking time is shortened, the picking efficiency is improved, and the stability of the shelf is improved.

[0047] For example, the maximum number of iterations Set to 400~500, population The number of individuals is 50.

[0048] On the basis of the above embodiment, in order to further improve the storage cargo allocation method provided by the above embodiment, as an implementable method, in one embodiment, the crossover probability is 0.9, so that the genetic algorithm has a higher search ability. The mutation probability is 0.1, so that the genetic algorithm has a stronger random search ability and accelerates the convergence to the optimal solution.

[0049] The present invention also provides a storage cargo space allocation system, which includes: an efficiency model construction unit, a center of gravity model construction unit, a multi-objective storage cargo space allocation model construction unit and a cargo space allocation result acquisition unit.

[0050] The efficiency model building unit is used to obtain the in-and-out frequency of the power tools according to the historical out-and-out information of the power tools, and to establish an objective function for reducing the in-and-out handling time.

[0051] The center of gravity model building unit is used to count the weight of power tools and establish an objective function for lowering the center of gravity of the shelf.

[0052] The multi-objective storage cargo space allocation model construction unit is used to construct a multi-objective function for storage cargo space allocation based on the objective function of reducing the in-and-out handling time and the objective function of reducing the center of gravity of the shelf.

[0053] The cargo space allocation result acquisition unit is used to solve the multi-objective function of storage cargo space allocation through a preset algorithm to obtain the optimal cargo space allocation result.

[0054] It should be noted that the storage space allocation system and the storage space allocation methods of the above-mentioned embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the storage space allocation system, reference can be made to the embodiments of the above-mentioned storage space allocation method.

[0055] The storage cargo space allocation method provided in the embodiment of the present invention can be applied to electronic devices. Those skilled in the art will appreciate that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown, or combine certain components, or arrange different components. In the embodiment of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present invention described and / or required herein.

[0056] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0057] It is to be understood that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on the electronic device. In other embodiments of the present invention, the electronic device may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0058] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0059] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0060] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0061] The external memory interface can be used to connect an external memory card, such as a MicroSD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor through the external memory interface to implement data storage functions. For example, files such as music and videos can be saved in the external memory card.

[0062] The internal memory can be used to store computer executable program codes, which include instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area. The internal memory may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0063] The wireless communication function of an electronic device can be realized through an antenna, a wireless communication module, a modem processor, and a baseband processor.

[0064] The wireless communication module can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0065] Electronic devices can implement audio functions, etc. through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0066] Electronic devices can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0067] Electronic devices can achieve display functions through GPU, display screen and application processor.

[0068] The GPU is a microprocessor for image processing that connects the display screen and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs that execute program instructions to generate or change display information.

[0069] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0070] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. 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 the present invention.

[0071] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the equipment, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. Exemplarily, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0072] In the several embodiments provided by the present invention, 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 units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0073] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, those skilled in the art will appreciate that the technical solution of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0074] The above-mentioned electronic device realizes the storage cargo space allocation method of the present invention, which obtains the in-and-out frequency of the power tools according to the historical out-and-out information of the power tools, and establishes the objective function of reducing the in-and-out transportation time; counts the weight of the power tools, and establishes the objective function of reducing the center of gravity of the shelf; constructs a multi-objective function for storage cargo space allocation based on the objective function of reducing the in-and-out transportation time and the objective function of reducing the center of gravity of the shelf; solves the multi-objective function for storage cargo space allocation through a preset algorithm to obtain the optimal cargo space allocation result. The storage cargo space allocation method of the present invention obtains the picking time from each cargo space where the power tools are stored to the storage area exit and the total center of gravity of the shelf; establishes a multi-objective function for storage cargo space allocation with the shortest total picking time and the lowest total center of gravity of the shelf as the goals; solves the multi-objective function for storage cargo space allocation to obtain the optimal multi-objective storage cargo space allocation plan; so that each cargo space where the power tools are stored is reasonably allocated, the picking time is shortened, the picking efficiency is improved, and the stability of the shelf is improved.

[0075] The storage medium provided by the present invention stores a program product capable of implementing the warehouse storage location allocation method.

[0076] The method for allocating storage cargo space includes: obtaining the in-and-out frequency of electric tools according to the historical out-and-out information of electric tools, and establishing an objective function for reducing the in-and-out transportation time; counting the weight of the electric tools, and establishing an objective function for reducing the center of gravity of the shelf; constructing a multi-objective function for allocating storage cargo space based on the objective function of reducing the in-and-out transportation time and the objective function of reducing the center of gravity of the shelf; solving the multi-objective function for allocating storage cargo space through a preset algorithm to obtain the optimal cargo space allocation result.

[0077] By solving the multi-objective function of storage location allocation, an optimal solution is obtained. This optimal solution is the optimal storage location allocation plan, which shortens the in-and-out handling time and minimizes the center of gravity of the shelf, which not only improves the picking efficiency but also ensures the stability of the shelf.

[0078] In some possible implementations, the warehouse storage location allocation method of the present disclosure may be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above “Exemplary Method” section of this specification according to various exemplary implementations of the present disclosure.

[0079] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0080] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0081] For those skilled in the art, designing different forms of control circuits according to the teachings of the present invention does not require creative work. These changes, modifications, substitutions and variations of the embodiments without departing from the principles and spirit of the present invention still fall within the scope of protection of the present invention.

Claims

1. A method for allocating storage space, characterized in that: The storage space allocation method comprises: The frequency of in-and-out of power tools is obtained based on the historical out-and-out information of power tools, and an objective function for reducing the in-and-out handling time is established; Count the weight of power tools and establish an objective function to lower the center of gravity of the shelf; A multi-objective function for warehouse storage allocation is constructed based on the objective function of reducing the time of inbound and outbound transportation and the objective function of reducing the center of gravity of the shelf; The multi-objective function of warehouse storage location allocation is solved through the preset algorithm to obtain the optimal storage location allocation result.

2. The method for allocating storage space according to claim 1, characterized in that: The expression of the objective function for reducing the time of inbound and outbound transportation is: In the formula, Indicates the layer where the power tool is located. , Indicates the column where the power tool is located. , Indicates layer The frequency of entering and leaving the warehouse of the power tools listed, Indicates the vertical running speed of the AGV car. Indicates the horizontal running speed of the AGV car. Indicates the lateral running speed of the AGV car. Indicates the length of the cargo space. Indicates the height of the cargo space. Indicates the depth of the cargo space.

3. The method for allocating storage space according to claim 2, characterized in that: The expression of the objective function for lowering the center of gravity of the shelf is: In the formula, Indicates layer The weight of the power tool.

4. The method for allocating storage space according to claim 3, characterized in that: The expression of the multi-objective function of storage location allocation is: In the formula, represents the single objective function category, , represents the weight of a single objective function, , represents the objective function after a power tool is assigned, Represents the objective function The optimal value of .

5. The method for allocating storage space according to claim 1, characterized in that: The preset algorithm is: genetic algorithm.

6. The method for allocating storage space according to claim 5, characterized in that: The multi-objective function of warehouse storage location allocation is solved by a preset algorithm to obtain the optimal storage location allocation result, including the following specific steps: Step 1: Set the evolution iteration counter , set the maximum number of iterations , randomly generated individuals as the initial population ; Step 2: Calculate the current population based on the multi-objective function of warehouse storage allocation The fitness value of each individual in , find the individual with the largest fitness value, and record this individual as the current optimal individual; Step 3: In the current population In the process, the proportional selection method is used for selection operation, the single-point crossover method is used for crossover operation, and the uniform mutation operator is used for mutation operation to obtain a new generation of population; Step 4: Calculate the new generation population based on the multi-objective function of warehouse storage allocation The fitness value of each individual in the population is calculated, and the individual with the largest fitness value is found as the optimal individual of the population; the fitness value of the optimal individual of the population is compared with the fitness value of the current optimal individual, and the individual with the larger fitness value between the fitness value of the optimal individual of the population and the fitness value of the current optimal individual is taken as the current optimal individual; The number of iterations increases by 1; Step 5: Determine whether the number of iterations has reached the maximum number of iterations ; If the maximum number of iterations is not reached , then return to step 3; If the maximum number of iterations is reached , then execute step 6: output the current optimal individual and obtain the corresponding warehouse storage location allocation plan.

7. The method for allocating storage space according to claim 6, characterized in that: Maximum number of iterations Set to 400~500.

8. A storage space allocation system applied to the storage space allocation method according to any one of claims 1 to 7, characterized in that: The system comprises: An efficiency model building unit, used to obtain the in-and-out frequency of the power tools according to the historical out-and-out information of the power tools, and to establish an objective function for reducing the in-and-out handling time; A center of gravity model building unit is used to calculate the weight of power tools and establish an objective function for lowering the center of gravity of the shelf; A multi-objective storage cargo space allocation model building unit is used to build a multi-objective function for storage cargo space allocation based on the objective function of reducing the in-and-out handling time and the objective function of reducing the center of gravity of the shelf; The cargo space allocation result acquisition unit is used to solve the multi-objective function of storage cargo space allocation through a preset algorithm to obtain the optimal cargo space allocation result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the warehouse storage space allocation method as described in any one of claims 1-7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the storage space allocation method as described in any one of claims 1 to 7 are implemented.

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