Intelligent warehouse management method and system

Abstract

The invention discloses an intelligent warehouse management method and system, relates to the technical field of warehouse management, and enhances the market response speed and decision-making intelligence of enterprises by scientifically evaluating the warehouse reputation and capability, optimizing resource configuration, reducing the logistics cost and improving the sales data analysis capability. The method helps enterprises to quickly identify community storage meeting cooperation standards, optimizes resource configuration, reduces unnecessary storage cost, utilizes existing small merchant storage space, reduces investment and operation cost of newly-built warehouses, realizes optimal configuration of resources, improves overall efficiency, and improves social benefits through cooperation with local merchants and enhancement of contact between the enterprises and communities. And by analyzing sales data, inventory turnover rate and the like of each community, the system can realize reasonable allocation of resources, utilize the existing storage space to the greatest extent, select storage closer to customers, reduce transportation time and cost and improve transportation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular to an intelligent warehouse management method and system. Background Art

[0002] With the accelerated development of urbanization and dense urban population, the demand for warehousing and logistics services in the community is increasing. Community warehousing can effectively solve the "last mile" distribution problem, improve logistics efficiency, and meet residents' demand for fast services. Intelligent warehousing management system can help community warehousing to effectively allocate and manage resources, so an intelligent warehousing management system came into being.

[0003] The prior art, such as a sales data processing method and device based on a data warehouse disclosed in the invention patent application with announcement number CN115187308A, belongs to the field of data processing technology and is used to solve the technical problems that the existing decision-making system has simple evaluation factors, non-standard standards, and a single data source, resulting in poor decision rationality and effectiveness. The method includes: obtaining comprehensive sales data related to various commodities operated by the enterprise; wherein the comprehensive sales data at least includes: the enterprise's own sales data, distributor sales data, and industry sales data; pre-processing the comprehensive sales data; constructing the sales data warehouse of the enterprise based on the pre-processed comprehensive sales data; constructing a multidimensional decision system based on the analysis needs of the enterprise; through the multidimensional decision system and the online analysis processing OLAP tool, the data in the sales data warehouse is analyzed and processed to obtain multidimensional decision information; and a pre-decision plan is generated based on the multidimensional decision information.

[0004] With respect to the above scheme, the applicant of the present invention has found that the above technology has at least the following technical problems: 1. Traditional warehouse management may not be able to fully utilize existing storage space and resources, resulting in inventory backlogs or insufficient space. It is necessary to invest in new construction or lease independent storage facilities, resulting in increased capital expenditure and operating costs. The maintenance and management costs of independent warehouses may be very high, especially when demand fluctuates.

[0005] Under the traditional warehousing model, there may be idle space in the warehouse, resulting in waste of resources. The lack of distributed inventory management may lead to excess or shortage of inventory, affecting overall operational efficiency. The warehousing facilities of large enterprises are often concentrated in specific areas and cannot respond quickly to changes in demand within the community, which may lead to delivery delays. When faced with market changes, there is a lack of flexible warehousing solutions, making it difficult to quickly adjust inventory and distribution strategies.

[0006] It is difficult for companies to cooperate with local merchants, which may lead to alienation between the company and the community, affecting brand image and customer loyalty. The lack of in-depth understanding of local market demand may result in products and services that fail to meet the actual needs of community consumers. In the absence of community warehousing cooperation, each warehousing facility may operate independently, making it difficult to integrate data, leading to information asymmetry. Without flexible warehousing solutions, the return on investment in advanced warehousing management technologies (such as automation and intelligent systems) may be reduced. Under the traditional model, companies may lack the motivation to innovate and cannot adapt to the rapidly changing market environment. Summary of the invention

[0007] In view of the above existing problems, the present invention is proposed.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions including: collecting storage reputation influencing parameters and storage capacity parameters of each community warehouse, calculating the storage reputation influencing factor, and calculating the storage capacity evaluation coefficient; comparing the storage capacity evaluation coefficient of each community warehouse with the standard storage capacity evaluation coefficient, and screening out warehouses that meet the standards of potential cooperative community warehouses; analyzing the storage cost, transportation cost and transportation time of potential cooperative community warehouses, and calculating the decision factor evaluation coefficient; sorting the potential cooperative community warehouses according to the decision factor evaluation coefficient, and selecting the warehouse with the highest evaluation coefficient as the best cooperative community warehouse; analyzing the sales data evaluation coefficient within the target market of the enterprise, and comparing it with the sales data evaluation coefficient corresponding to each rental area in the database, to determine the most suitable rental area for the best cooperative community warehouse.

[0009] As a preferred solution of the intelligent warehousing management method described in the present invention, wherein: the warehouse reputation influencing parameters include the number of successful cooperation cases, customer retention rate and the corresponding praise rate of each evaluation platform, and the warehouse capacity parameters include the actual available area, the corresponding number of each type of available shelves and the corresponding number of processed orders in each unit time.

[0010] As a preferred solution of the intelligent warehouse management method described in the present invention, the calculation of the warehouse reputation influencing factor includes recording the number of successful cooperation cases, customer retention rate and praise rate of each evaluation platform corresponding to each sales season in each community warehouse as and

[0011] Among them, f represents the number corresponding to each community warehouse, f=1,2......u, u is any integer greater than 2, g represents the number corresponding to each sales season, g=1,2......n, n is any integer greater than 2, h represents the number corresponding to each evaluation platform, h=1,2......m, m is any integer greater than 2, substitute into the calculation formula:

[0012]

[0013] Among them, α f represents the warehouse reputation influencing factors corresponding to each community warehouse, A′, B′, and C′ are respectively the standard number of successful cooperation cases, standard customer retention rate, and standard praise rate corresponding to the evaluation platform corresponding to the set community warehouse sales season, μ1, μ2, and μ3 are respectively the weight factors corresponding to the number of successful cooperation cases, customer retention rate, and praise rate of the evaluation platform corresponding to the set community warehouse sales season.

[0014] As a preferred solution of the intelligent warehouse management method described in the present invention, the calculation of the warehouse capacity evaluation coefficient includes recording the actual available area corresponding to each sales season in each community warehouse, the number of available shelves of each type, and the number of processed orders corresponding to each unit time as and

[0015] Among them, y represents the number corresponding to each type of available shelf, y=1,2......z, z is any integer greater than 2, k represents the number corresponding to each unit time, k=1,2......x, x is any integer greater than 2, substitute into the formula:

[0016]

[0017] Among them, β f are the storage capacity assessment coefficients corresponding to each community warehouse, D′, E′, and F′ are the standard actual available area corresponding to the set community warehouse sales season, the standard number of available shelves of different types, and the standard number of processed orders per unit time, respectively; π1, π2, and π3 are the weight factors corresponding to the actual available area of ​​the set community warehouse sales season, the weight factors corresponding to the number of available shelves of different types, and the weight factors corresponding to the number of processed orders per unit time, respectively.

[0018] As a preferred embodiment of the intelligent warehousing management method described in the present invention, the method further comprises: the method of screening out warehouses that meet the standards of potential cooperative community warehouses comprises comparing the warehousing capacity assessment coefficient corresponding to each community warehouse with the warehousing capacity assessment coefficient corresponding to the set standard community warehouse; when the warehousing capacity assessment coefficient corresponding to the community warehouse is less than the warehousing capacity assessment coefficient corresponding to the set standard community warehouse, it is evaluated that the current community warehouse does not meet the standards of potential cooperative community warehouses; when the warehousing capacity assessment coefficient corresponding to the community warehouse is greater than or equal to the warehousing capacity assessment coefficient corresponding to the set standard community warehouse, it is evaluated that the current community warehouse meets the standards of potential cooperative community warehouses; and each community warehouse is evaluated whether it meets the standards of potential cooperative community warehouses to obtain a collection of potential cooperative community warehouses within the target market of the enterprise.

[0019] As a preferred solution of the intelligent warehousing management method described in the present invention, wherein: the calculation of the decision factor evaluation coefficient includes recording the storage cost corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, the transportation cost and transportation time corresponding to each distributor as G t , and

[0020] Where t represents the number corresponding to each potential cooperative community warehouse, t = 1, 2...q, q is any integer greater than 2, r represents the number corresponding to each distributor, r = 1, 2...w, w is any integer greater than 2, substitute into the calculation formula:

[0021]

[0022] Among them, γ t represents the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, G′, H′, R′ are respectively the standard storage cost corresponding to the set potential cooperative community warehouse, the standard transportation cost corresponding to the distributor, and the standard transportation time, η1, η2, η3 are respectively the weight factor corresponding to the storage cost of the set potential cooperative community warehouse, the weight factor corresponding to the transportation cost of the distributor, and the weight factor corresponding to the transportation time;

[0023] Arrange the decision factor evaluation coefficients corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection in order from large to small, and select the potential cooperative community warehouse corresponding to the largest decision factor evaluation coefficient in the cooperative community warehouse collection as the corresponding best cooperative community warehouse within the enterprise's target market.

[0024] As a preferred solution of the intelligent warehouse management method described in the present invention, the method includes: determining the most suitable rental area for the best cooperative community warehouse includes recording the sales volume of each distributor within the target market of the enterprise and the inventory turnover rate of each category of products as Q r and

[0025] Where r represents the number corresponding to each distributor, r=1,2...w, w is any integer greater than 2, v represents the number corresponding to each category of products, v=1,2...s, s is any integer greater than 2, substitute into the calculation formula:

[0026]

[0027] Among them, the sales data evaluation coefficient λ corresponding to the enterprise target market is obtained, where Q′ and T′ are the standard sales volume corresponding to the distributor within the set enterprise target market and the standard inventory turnover rate corresponding to the category product, respectively, and ω1 and ω2 are the weight factors corresponding to the sales volume of the distributor within the set enterprise target market and the weight factors corresponding to the inventory turnover rate of the category product, respectively;

[0028] Compare the sales data evaluation coefficients corresponding to the enterprise's target market with the sales data evaluation coefficients corresponding to each leased area in the database. When the sales data evaluation coefficients corresponding to the enterprise's target market are the same as the sales data evaluation coefficients corresponding to the leased area in the database, the current leased area in the database will be used as the most suitable leased area corresponding to the best cooperative community warehouse within the enterprise's target market.

[0029] The database is used to store the sales data evaluation coefficient corresponding to each leased area.

[0030] Another object of the present invention is to provide an intelligent warehouse management system.

[0031] As a preferred solution of the intelligent warehouse management system described in the present invention, it includes:

[0032] It includes a storage capacity evaluation coefficient acquisition module, a potential cooperative community storage analysis module, a decision factor evaluation coefficient acquisition module, an optimal cooperative community storage selection module, and a sales data evaluation coefficient acquisition module;

[0033] The storage capacity evaluation coefficient acquisition module is used to obtain storage reputation influence parameters and storage capacity parameters corresponding to each sales season in each community storage within the target market of the enterprise, and analyze and obtain the storage reputation influence factors and storage capacity evaluation coefficients corresponding to each community storage;

[0034] The potential cooperative community warehouse analysis module is used to evaluate whether each community warehouse meets the standards of potential cooperative community warehouses according to the corresponding warehousing capacity evaluation coefficient of each community warehouse, and then obtain a collection of potential cooperative community warehouses within the target market of the enterprise;

[0035] The decision factor evaluation coefficient acquisition module is used to obtain the decision factor parameters corresponding to each potential cooperative community warehouse in the collection of potential cooperative community warehouses within the target market of the enterprise, and the decision factor parameters include storage cost, transportation cost and transportation time corresponding to each distributor, and then analyze and obtain the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the collection of cooperative community warehouses;

[0036] The optimal cooperative community warehouse selection module is used to select the optimal cooperative community warehouse corresponding to the enterprise's target market according to the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection;

[0037] The sales data evaluation coefficient acquisition module is used to obtain the sales data corresponding to each distributor within the enterprise's target market. The sales data includes sales volume and inventory turnover rate corresponding to each category of products, and then analyze to obtain the sales data evaluation coefficient corresponding to the enterprise's target market, and evaluate the most suitable rental area corresponding to the best cooperative community warehouse within the enterprise's target market.

[0038] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor implements the steps of an intelligent warehouse management method when executing the computer program.

[0039] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of an intelligent warehouse management method are implemented.

[0040] Beneficial effects of the invention: The invention scientifically evaluates the reputation and capabilities of warehouses, optimizes resource allocation, reduces logistics costs, and improves sales data analysis capabilities, thereby enhancing the market response speed and decision-making intelligence of enterprises. It helps enterprises quickly identify community warehouses that meet cooperation standards and optimize resource allocation. By analyzing the sales data and inventory turnover rate of each community, the system can achieve reasonable resource allocation and maximize the use of existing warehouse space to select warehouses closer to customers, reduce transportation time and costs, and improve transportation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0042] Figure 1 A schematic flow chart of an intelligent warehouse management method provided for one embodiment of the present invention.

[0043] Figure 2 A schematic diagram of working modules of an intelligent warehouse management system provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0048] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides an intelligent warehouse management method, including:

[0051] S1: Collect the storage reputation influencing parameters and storage capacity parameters of each community warehouse, calculate the storage reputation influencing factor, and calculate the storage capacity evaluation coefficient.

[0052] Furthermore, the warehouse reputation influencing parameters include the number of successful cooperation cases, customer retention rate and the corresponding praise rate of each evaluation platform, and the warehouse capacity parameters include the actual available area, the corresponding number of each type of available shelves and the corresponding number of processed orders in each unit time.

[0053] It should be noted that the calculation of the warehouse reputation influencing factor includes recording the number of successful cooperation cases, customer retention rate and the praise rate of each evaluation platform corresponding to each sales season in each community warehouse as and

[0054] Among them, f represents the number corresponding to each community warehouse, f=1,2......u, u is any integer greater than 2, g represents the number corresponding to each sales season, g=1,2......n, n is any integer greater than 2, h represents the number corresponding to each evaluation platform, h=1,2......m, m is any integer greater than 2, substitute into the calculation formula:

[0055]

[0056] Among them, α frepresents the warehouse reputation influencing factors corresponding to each community warehouse, A′, B′, and C′ are respectively the standard number of successful cooperation cases, standard customer retention rate, and standard praise rate corresponding to the evaluation platform corresponding to the set community warehouse sales season, μ1, μ2, and μ3 are respectively the weight factors corresponding to the number of successful cooperation cases, customer retention rate, and praise rate of the evaluation platform corresponding to the set community warehouse sales season.

[0057] S2: Compare the storage capacity assessment coefficient of each community warehouse with the standard storage capacity assessment coefficient to screen out warehouses that meet the potential cooperative community storage standards.

[0058] Furthermore, the calculation of the storage capacity evaluation coefficient includes recording the actual available area corresponding to each sales season in each community warehouse, the number of available shelves of each type, and the number of processed orders per unit time as and

[0059] Among them, y represents the number corresponding to each type of available shelf, y=1,2......z, z is any integer greater than 2, k represents the number corresponding to each unit time, k=1,2......x, x is any integer greater than 2, substitute into the formula:

[0060]

[0061] Among them, β f are the storage capacity assessment coefficients corresponding to each community warehouse, D′, E′, and F′ are the standard actual available area corresponding to the set community warehouse sales season, the standard number of available shelves of different types, and the standard number of processed orders per unit time, respectively; π1, π2, and π3 are the weight factors corresponding to the actual available area of ​​the set community warehouse sales season, the weight factors corresponding to the number of available shelves of different types, and the weight factors corresponding to the number of processed orders per unit time, respectively.

[0062] S3: Analyze the storage cost, transportation cost and transportation time of potential cooperative community warehousing, and calculate the decision factor evaluation coefficient.

[0063] Furthermore, the screening out of warehouses that meet the standards of potential cooperative community warehouses includes comparing the storage capacity assessment coefficient corresponding to each community warehouse with the storage capacity assessment coefficient corresponding to the set standard community warehouse. When the storage capacity assessment coefficient corresponding to the community warehouse is less than the storage capacity assessment coefficient corresponding to the set standard community warehouse, it is evaluated that the current community warehouse does not meet the standards of potential cooperative community warehouses. When the storage capacity assessment coefficient corresponding to the community warehouse is greater than or equal to the storage capacity assessment coefficient corresponding to the set standard community warehouse, it is evaluated that the current community warehouse meets the standards of potential cooperative community warehouses. It is evaluated whether each community warehouse meets the standards of potential cooperative community warehouses to obtain a collection of potential cooperative community warehouses within the enterprise's target market.

[0064] S4: Sort the potential cooperative community warehouses according to the decision factor evaluation coefficients, and select the warehouse with the highest evaluation coefficient as the best cooperative community warehouse.

[0065] Furthermore, the calculation of the decision factor evaluation coefficient includes recording the storage cost corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, the transportation cost and transportation time corresponding to each distributor as G t , and

[0066] Where t represents the number corresponding to each potential cooperative community warehouse, t = 1, 2...q, q is any integer greater than 2, r represents the number corresponding to each distributor, r = 1, 2...w, w is any integer greater than 2, substitute into the calculation formula:

[0067]

[0068] Among them, γ t represents the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, G′, H′, R′ are respectively the standard storage cost corresponding to the set potential cooperative community warehouse, the standard transportation cost corresponding to the distributor, and the standard transportation time, η1, η2, η3 are respectively the weight factor corresponding to the storage cost of the set potential cooperative community warehouse, the weight factor corresponding to the transportation cost of the distributor, and the weight factor corresponding to the transportation time;

[0069] Arrange the decision factor evaluation coefficients corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection in order from large to small, and select the potential cooperative community warehouse corresponding to the largest decision factor evaluation coefficient in the cooperative community warehouse collection as the corresponding best cooperative community warehouse within the enterprise's target market.

[0070] S5: Analyze the sales data evaluation coefficient within the enterprise's target market, compare it with the sales data evaluation coefficient corresponding to each rental area in the database, and determine the most suitable rental area for the best cooperative community warehouse.

[0071] Furthermore, the method of determining the most suitable rental area for the best cooperative community warehouse includes recording the sales volume of each distributor within the target market of the enterprise and the inventory turnover rate of each category of products as Q r and

[0072] Where r represents the number corresponding to each distributor, r=1,2...w, w is any integer greater than 2, v represents the number corresponding to each category of products, v=1,2...s, s is any integer greater than 2, substitute into the calculation formula:

[0073]

[0074] Among them, the sales data evaluation coefficient λ corresponding to the enterprise target market is obtained, where Q′ and T′ are the standard sales volume corresponding to the distributor within the set enterprise target market and the standard inventory turnover rate corresponding to the category product, respectively, and ω1 and ω2 are the weight factors corresponding to the sales volume of the distributor within the set enterprise target market and the weight factors corresponding to the inventory turnover rate of the category product, respectively;

[0075] Compare the sales data evaluation coefficients corresponding to the enterprise's target market with the sales data evaluation coefficients corresponding to each leased area in the database. When the sales data evaluation coefficients corresponding to the enterprise's target market are the same as the sales data evaluation coefficients corresponding to the leased area in the database, the current leased area in the database will be used as the most suitable leased area corresponding to the best cooperative community warehouse within the enterprise's target market.

[0076] The database is used to store the sales data evaluation coefficient corresponding to each leased area.

[0077] Embodiment 2, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0078] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0079] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0080] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] Example 3, reference Figure 2 , is an embodiment of the present invention, and provides an intelligent warehouse management system, characterized by: comprising a warehouse capacity evaluation coefficient acquisition module, a potential cooperative community warehouse analysis module, a decision factor evaluation coefficient acquisition module, an optimal cooperative community warehouse selection module, and a sales data evaluation coefficient acquisition module;

[0082] The storage capacity evaluation coefficient acquisition module is used to obtain the storage reputation influencing parameters and storage capacity parameters corresponding to each sales season in each community storage within the target market of the enterprise, and analyze and obtain the storage reputation influencing factors and storage capacity evaluation coefficients corresponding to each community storage;

[0083] The potential cooperative community warehouse analysis module is used to evaluate whether each community warehouse meets the standards of potential cooperative community warehouses according to the corresponding warehousing capacity evaluation coefficient of each community warehouse, and then obtain the collection of potential cooperative community warehouses within the target market of the enterprise;

[0084] The decision factor evaluation coefficient acquisition module is used to obtain the decision factor parameters corresponding to each potential cooperative community warehouse in the collection of potential cooperative community warehouses within the target market of the enterprise. The decision factor parameters include storage cost, transportation cost and transportation time corresponding to each distributor, and then analyze and obtain the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the collection of cooperative community warehouses;

[0085] The best cooperative community warehouse selection module is used to select the best cooperative community warehouse within the target market of the enterprise according to the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection;

[0086] The sales data evaluation coefficient acquisition module is used to obtain the sales data corresponding to each distributor within the company's target market. The sales data includes sales volume and inventory turnover rate corresponding to each category of products, and then analyzes the sales data evaluation coefficient corresponding to the company's target market, and evaluates the most suitable rental area corresponding to the best cooperative community warehouse within the company's target market.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent warehouse management method, characterized in that: include, Collect the storage reputation influencing parameters and storage capacity parameters of each community storage, calculate the storage reputation influencing factor, and calculate the storage capacity evaluation coefficient; Compare the storage capacity assessment coefficients of each community warehouse with the standard storage capacity assessment coefficient to screen out warehouses that meet the potential cooperative community storage standards; Analyze the storage cost, transportation cost and transportation time of potential cooperative community warehousing, and calculate the evaluation coefficient of decision factors; According to the evaluation coefficient of the decision-making factors, the potential cooperative community warehouses are ranked, and the warehouse with the highest evaluation coefficient is selected as the best cooperative community warehouse; Analyze the sales data evaluation coefficient within the company's target market, compare it with the sales data evaluation coefficient corresponding to each rental area in the database, and determine the most suitable rental area for the best cooperative community warehouse.

2. An intelligent warehouse management method as claimed in claim 1, characterized in that: The warehouse reputation influencing parameters include the number of successful cooperation cases, customer retention rate and the corresponding praise rate of each evaluation platform, and the warehouse capacity parameters include the actual available area, the corresponding number of available shelves of each type and the corresponding number of processed orders in each unit time.

3. An intelligent warehouse management method as claimed in claim 2, characterized in that: The calculation of the warehouse reputation influencing factor includes recording the number of successful cooperation cases, customer retention rate and praise rate of each evaluation platform corresponding to each sales season in each community warehouse as and Among them, f represents the number corresponding to each community warehouse, f=1,2......u, u is any integer greater than 2, g represents the number corresponding to each sales season, g=1,2......n, n is any integer greater than 2, h represents the number corresponding to each evaluation platform, h=1,2......m, m is any integer greater than 2, substitute into the calculation formula: Among them, α f represents the warehouse reputation influencing factors corresponding to each community warehouse, A′, B′, and C′ are respectively the standard number of successful cooperation cases, standard customer retention rate, and standard praise rate corresponding to the evaluation platform corresponding to the set community warehouse sales season, μ1, μ2, and μ3 are respectively the weight factors corresponding to the number of successful cooperation cases, customer retention rate, and praise rate of the evaluation platform corresponding to the set community warehouse sales season.

4. An intelligent warehouse management method as claimed in claim 3, characterized in that: The calculation of the storage capacity evaluation coefficient includes recording the actual available area corresponding to each sales season in each community warehouse, the number of available shelves of each type, and the number of processed orders corresponding to each unit time as and Among them, y represents the number corresponding to each type of available shelf, y=1,2......z, z is any integer greater than 2, k represents the number corresponding to each unit time, k=1,2......x, x is any integer greater than 2, substitute into the formula: Among them, β f are the storage capacity assessment coefficients corresponding to each community warehouse, D′, E′, and F′ are the standard actual available area corresponding to the set community warehouse sales season, the standard number of available shelves of different types, and the standard number of processed orders per unit time, respectively; π1, π2, and π3 are the weight factors corresponding to the actual available area of ​​the set community warehouse sales season, the weight factors corresponding to the number of available shelves of different types, and the weight factors corresponding to the number of processed orders per unit time, respectively.

5. An intelligent warehouse management method as claimed in claim 4, characterized in that: The screening out of warehouses that meet the standards of potential cooperative community warehouses includes comparing the storage capacity assessment coefficient corresponding to each community warehouse with the storage capacity assessment coefficient corresponding to the set standard community warehouse. When the storage capacity assessment coefficient corresponding to the community warehouse is less than the storage capacity assessment coefficient corresponding to the set standard community warehouse, it is evaluated that the current community warehouse does not meet the standards of potential cooperative community warehouses. When the storage capacity assessment coefficient corresponding to the community warehouse is greater than or equal to the storage capacity assessment coefficient corresponding to the set standard community warehouse, it is evaluated that the current community warehouse meets the standards of potential cooperative community warehouses. It is evaluated whether each community warehouse meets the standards of potential cooperative community warehouses to obtain a collection of potential cooperative community warehouses within the enterprise's target market.

6. An intelligent warehouse management method as claimed in claim 5, characterized in that: The calculation of the decision factor evaluation coefficient includes recording the storage cost corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, the transportation cost and transportation time corresponding to each distributor as G t , and Where t represents the number corresponding to each potential cooperative community warehouse, t = 1, 2...q, q is any integer greater than 2, r represents the number corresponding to each distributor, r = 1, 2...w, w is any integer greater than 2, substitute into the calculation formula: Among them, γ t represents the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection, G′, H′, R′ are respectively the standard storage cost corresponding to the set potential cooperative community warehouse, the standard transportation cost corresponding to the distributor, and the standard transportation time, η1, η2, η3 are respectively the weight factor corresponding to the storage cost of the set potential cooperative community warehouse, the weight factor corresponding to the transportation cost of the distributor, and the weight factor corresponding to the transportation time; Arrange the decision factor evaluation coefficients corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection in order from large to small, and select the potential cooperative community warehouse corresponding to the largest decision factor evaluation coefficient in the cooperative community warehouse collection as the corresponding best cooperative community warehouse within the enterprise's target market.

7. An intelligent warehouse management method according to claim 6, characterized in that: Determining the most suitable rental area for the best cooperative community warehouse includes recording the sales volume of each distributor within the target market of the enterprise and the inventory turnover rate of each category of products as Q r and Where r represents the number corresponding to each distributor, r=1,2...w, w is any integer greater than 2, v represents the number corresponding to each category of products, v=1,2...s, s is any integer greater than 2, substitute into the calculation formula: Among them, the sales data evaluation coefficient λ corresponding to the enterprise target market is obtained, where Q′ and T′ are the standard sales volume corresponding to the distributor within the set enterprise target market and the standard inventory turnover rate corresponding to the category product, respectively, and ω1 and ω2 are the weight factors corresponding to the sales volume of the distributor within the set enterprise target market and the weight factors corresponding to the inventory turnover rate of the category product, respectively; Compare the sales data evaluation coefficients corresponding to the enterprise's target market with the sales data evaluation coefficients corresponding to each leased area in the database. When the sales data evaluation coefficients corresponding to the enterprise's target market are the same as the sales data evaluation coefficients corresponding to the leased area in the database, the current leased area in the database will be used as the most suitable leased area corresponding to the best cooperative community warehouse within the enterprise's target market. The database is used to store the sales data evaluation coefficient corresponding to each leased area.

8. A system using an intelligent warehouse management method as claimed in any one of claims 1 to 7, characterized in that: It includes a storage capacity evaluation coefficient acquisition module, a potential cooperative community storage analysis module, a decision factor evaluation coefficient acquisition module, an optimal cooperative community storage selection module, and a sales data evaluation coefficient acquisition module; The storage capacity evaluation coefficient acquisition module is used to obtain storage reputation influence parameters and storage capacity parameters corresponding to each sales season in each community storage within the target market of the enterprise, and analyze and obtain the storage reputation influence factors and storage capacity evaluation coefficients corresponding to each community storage; The potential cooperative community warehouse analysis module is used to evaluate whether each community warehouse meets the standards of potential cooperative community warehouses according to the corresponding warehousing capacity evaluation coefficient of each community warehouse, and then obtain a collection of potential cooperative community warehouses within the target market of the enterprise; The decision factor evaluation coefficient acquisition module is used to obtain the decision factor parameters corresponding to each potential cooperative community warehouse in the collection of potential cooperative community warehouses within the target market of the enterprise, and the decision factor parameters include storage cost, transportation cost and transportation time corresponding to each distributor, and then analyze and obtain the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the collection of cooperative community warehouses; The optimal cooperative community warehouse selection module is used to select the optimal cooperative community warehouse corresponding to the enterprise's target market according to the decision factor evaluation coefficient corresponding to each potential cooperative community warehouse in the cooperative community warehouse collection; The sales data evaluation coefficient acquisition module is used to obtain the sales data corresponding to each distributor within the enterprise's target market. The sales data includes sales volume and inventory turnover rate corresponding to each category of products, and then analyze to obtain the sales data evaluation coefficient corresponding to the enterprise's target market, and evaluate the most suitable rental area corresponding to the best cooperative community warehouse within the enterprise's target market.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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