Operation and maintenance methods, devices, equipment and storage media of commodity storage warehouses

Abstract

This application provides a method, apparatus, equipment, and storage medium for the operation and maintenance of a commodity storage warehouse. The method includes: acquiring order volume indicators, transportation costs, and electronic discount voucher usage for multiple commodity storage warehouses within a preset time period; statistically analyzing these indicators to determine a first correspondence between order volume indicators and transportation costs, and a second correspondence between order volume indicators and electronic discount voucher usage; determining indicator improvement strategies for each of the multiple order volume indicator intervals based on the first and second correspondences; for target commodity storage warehouses belonging to a target order volume indicator interval, generating execution suggestion information corresponding to the target order volume indicator interval based on the indicator improvement strategy for that interval; and sending the execution suggestion information to the management terminal corresponding to the target commodity storage warehouse. This method aims to reduce the operation and maintenance costs of commodity storage warehouses and is simple to implement.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, equipment, and storage medium for the operation and maintenance of a commodity storage warehouse. Background Technology

[0002] Community group buying is an online and offline shopping activity among residents within a residential community. It is a regionalized, niche, localized, and networked form of group buying based on a real community. In short, it is a new retail model that leverages the social relationships of the community and the group leader to facilitate the distribution of fresh produce.

[0003] Currently, the fulfillment costs of community group buying are relatively high, and many community group buying companies are even operating at a loss. In the long run, this is very detrimental to providing consumers with continuous community group buying services. Effectively reducing the fulfillment costs of community group buying is particularly important for its long-term sustainable development.

[0004] The fulfillment costs of community group buying mainly consist of: central warehouse costs, trunk line costs, and commodity storage warehouse costs. Analyzing the above composition of fulfillment costs, it is not difficult to find that commodity storage warehouse costs account for more than 50% of the fulfillment costs. Therefore, reducing commodity storage warehouse costs to ultimately reduce fulfillment costs is a feasible and cost-effective solution. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for the operation and maintenance of a commodity storage warehouse. By analyzing the order volume indicators, transportation costs, and electronic discount voucher usage corresponding to the commodity storage warehouse, the operation and maintenance costs of the commodity storage warehouse can be reduced.

[0006] In a first aspect, embodiments of the present invention provide an operation and maintenance method for a commodity storage warehouse, the method comprising:

[0007] Get the order volume, transportation costs, and electronic voucher usage for multiple product storage warehouses within a preset time period;

[0008] Statistical analysis is performed on the order volume indicators, transportation costs, and electronic discount voucher usage corresponding to the multiple commodity storage warehouses to determine the first correspondence between the order volume indicators and the transportation costs, and the second correspondence between the order volume indicators and the electronic discount voucher usage.

[0009] Based on the first correspondence and the second correspondence, determine the indicator improvement strategies corresponding to each of the multiple single-volume indicator ranges;

[0010] For a target product storage warehouse that falls within the target order volume indicator range, execution suggestion information corresponding to the target order volume indicator range is generated based on the indicator improvement strategy corresponding to the target order volume indicator range. The target order volume indicator range is any one of multiple order volume indicator ranges.

[0011] The execution suggestion information is sent to the management terminal corresponding to the target product storage warehouse.

[0012] Secondly, embodiments of the present invention provide an operation and maintenance device for a commodity storage warehouse, the device comprising:

[0013] The acquisition module is used to acquire the order volume indicators, transportation costs, and electronic discount voucher usage for multiple product storage warehouses within a preset time period.

[0014] The statistical analysis module is used to statistically analyze the order volume indicators, transportation costs, and electronic discount voucher usage corresponding to the multiple commodity storage warehouses, so as to determine the first correspondence between the order volume indicators and the transportation costs, and the second correspondence between the order volume indicators and the electronic discount voucher usage.

[0015] The determination module is used to determine the indicator improvement strategy corresponding to each of the multiple single-volume indicator intervals based on the first correspondence relationship and the second correspondence relationship;

[0016] The generation module is used to generate execution suggestion information corresponding to the target order volume indicator range for target product storage warehouses that belong to the target order volume indicator range, based on the indicator improvement strategy corresponding to the target order volume indicator range, wherein the target order volume indicator range is any one of multiple order volume indicator ranges; and send the execution suggestion information to the management terminal corresponding to the target product storage warehouse.

[0017] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a communication interface; wherein, the memory stores executable code, and when the executable code is executed by the processor, the processor can at least implement the operation and maintenance method of the commodity storage warehouse as described in the first aspect.

[0018] Fourthly, embodiments of the present invention provide a non-transitory machine-readable storage medium storing executable code, wherein when the executable code is executed by a processor of an electronic device, the processor is able to at least implement the operation and maintenance method of the commodity storage warehouse as described in the first aspect.

[0019] In this embodiment of the invention, after obtaining the order volume indicators, transportation costs, and electronic voucher usage for multiple product storage warehouses within a preset time period; by statistically analyzing the order volume indicators, transportation costs, and electronic voucher usage for multiple product storage warehouses, a first correspondence between the order volume indicators and transportation costs, and a second correspondence between the order volume indicators and electronic voucher usage are determined; based on the first and second correspondences, indicator improvement strategies corresponding to each of the multiple order volume indicator intervals are determined; for target product storage warehouses belonging to the target order volume indicator interval, execution suggestion information corresponding to the target order volume indicator interval is generated based on the indicator improvement strategy corresponding to the target order volume indicator interval; and the execution suggestion information is sent to the management terminal corresponding to the target product storage warehouse.

[0020] In the above solution, by analyzing the order volume indicators, transportation costs, and electronic voucher usage corresponding to multiple commodity storage warehouses, a first correspondence between order volume indicators and transportation costs, and a second correspondence between order volume indicators and electronic voucher usage, are determined. Based on the first and second correspondences, indicator improvement strategies corresponding to the order volume indicator range can be provided. For example, the amount of transportation costs and electronic voucher usage corresponding to the order volume indicator range. Based on the indicator improvement strategies corresponding to the target order volume indicator range, execution suggestion information corresponding to the target order volume indicator range is generated. This allows the management end of the target commodity storage warehouse to better manage the operation and maintenance of the target commodity storage warehouse based on the execution suggestion information, thereby reducing the operation and maintenance costs of the commodity storage warehouse. Finally, by reducing the commodity storage warehouse costs, the fulfillment costs of community group buying are reduced, achieving a simple and low-cost solution. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating an operation and maintenance method for a commodity storage warehouse, provided as an embodiment of the present invention;

[0023] Figure 2 A scatter plot for displaying a first correspondence relationship is provided in an embodiment of the present invention;

[0024] Figure 3 A scatter plot for displaying a second correspondence relationship is provided in an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of an optional plurality of first fitting function curves provided for an embodiment of the present invention;

[0026] Figure 5 A schematic diagram of an optional plurality of second fitting function curves provided for an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of the operation and maintenance device for a commodity storage warehouse provided in an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Where there is no conflict between the embodiments, the following embodiments and features can be combined with each other. Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0031] First, the terms or concepts involved in the embodiments of this invention will be explained:

[0032] Goods storage warehouse: refers to the actual warehousing resource point where goods are distributed in the community group buying operation model, such as a goods storage warehouse.

[0033] Goods storage warehouse: This is an important part of the community group buying operation model. It carries the core function of the circulation and delivery of goods from the group buying platform's main warehouse to offline service stores (group leader's end, group buying point). To a large extent, the quality of goods and whether they can be delivered to consumers in a timely manner depend on the operational capabilities of the goods storage warehouse.

[0034] Costs of goods storage warehouse service providers: The total cost of all expenses incurred in operating a goods storage warehouse, including rent, labor costs, management personnel costs, transportation and distribution costs, and other expenses.

[0035] Group buying efficiency: The number of orders fulfilled (purchased by users) by each group buying point per day, that is, the number of orders that the group buying point actually completes each day.

[0036] Product storage warehouse group efficiency: The number of products fulfilled (purchased by users) in the product storage warehouse per day divided by the number of target group buying points, where the number of target group buying points is the number of group buying points with orders within 24 hours of that day.

[0037] The operation and maintenance method of the commodity storage warehouse provided in this embodiment of the invention can be executed by an electronic device. In practical applications, the electronic device can be an electronic device, a server, or a user terminal such as a PC. The server can be a physical server or a virtual server (virtual machine) in the cloud.

[0038] Figure 1 A flowchart illustrating an operation and maintenance method for a commodity storage warehouse provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes the following steps:

[0039] 101. Obtain the order volume indicators, transportation costs, and electronic discount voucher usage for multiple product storage warehouses within a preset time period.

[0040] 102. Statistically analyze the order volume indicators, transportation costs, and electronic discount voucher usage corresponding to multiple commodity storage warehouses to determine the first correspondence between order volume indicators and transportation costs, and the second correspondence between order volume indicators and electronic discount voucher usage.

[0041] 103. Based on the first and second correspondences, determine the indicator improvement strategies corresponding to each of the multiple single-volume indicator ranges.

[0042] 104. For target commodity storage warehouses that fall within the target order volume indicator range, generate execution suggestion information corresponding to the target order volume indicator range based on the indicator improvement strategy corresponding to the target order volume indicator range. The target order volume indicator range can be any one of multiple order volume indicator ranges.

[0043] 105. Send the execution suggestion information to the management terminal corresponding to the target product storage warehouse.

[0044] In many practical applications, warehouses are used to store goods. A warehouse is a storage resource point for goods; for example, it's a storage resource point used by e-commerce platforms, community group-buying platforms, express delivery platforms, and logistics platforms to actually sort and pick goods. It can also be called a grid warehouse.

[0045] Community group buying is an online and offline shopping activity among residents within a real residential community. It is a regionalized, niche, localized, and networked form of group buying based on a real community. In short, it is a new retail model that leverages the social relationships of the community and the group leader to facilitate the distribution of fresh produce.

[0046] Specifically, community group-buying platforms can set up a central warehouse and multiple shared warehouses in each province and city. Most suppliers will build supply warehouses near the central warehouse to facilitate supplying goods to the central warehouse. Some goods, such as vegetables and fruits, can be supplied to shared warehouses for processing and packaging before being transported to the central warehouse by the platform's own transportation capacity. After the daily or batch order cutoff time, the central warehouse will begin to sort the goods according to the user's location and distribute them via freight to multiple goods storage warehouses in the city, i.e., grid warehouses. Grid warehouses are generally franchised and are responsible for orders from all stores within a grid on the map. Goods will be sorted and packaged at the order level here, and then delivered to the responsible stores by the franchisees themselves. The platform then pays the franchisees a fulfillment fee for each order.

[0047] This shopping method combines community group buying with the new retail model. Based on real offline communities, it uses community residents or managers of nearby shops as distribution nodes and utilizes online group chats, mini-programs, and mobile apps as group buying platforms to conduct pre-sales. It collects orders from community users, completes online order payments, and then sends the goods to self-pickup points for community members to pick up.

[0048] The community group buying operation model involves unified ordering by group buying points and then distribution to individual points. During the delivery process, only large orders are distributed. Centralized delivery reduces the logistics costs of sales. Therefore, the community group buying operation model involves multiple group buying points. Multiple group buying points in the same geographical area or planned area can occupy the same goods storage warehouse, which can eliminate intermediaries and allow users to enjoy the same quality of goods at a more favorable price through a simple ordering method.

[0049] Optionally, in this embodiment of the invention, the preset time period can be one day, i.e., within 24 hours, or one week, i.e. within seven consecutive days, or other time periods.

[0050] Optionally, the order volume indicators corresponding to the above-mentioned multiple product storage warehouses specifically refer to the group efficiency of the product storage warehouses. Taking the above-mentioned preset time period as one day as an example, the order volume indicator is the number of orders fulfilled (purchased by users) in the product storage warehouses per day divided by the number of target group buying points. The number of target group buying points can be the number of group buying points with orders within that day.

[0051] Alternatively, transportation refers to the act of moving the transported entity (person or goods) from place A to place B using a means of transport (or vehicle) to achieve a certain economic purpose. In other words, it can be understood as the spatial displacement of an object within different spatial ranges with the aim of changing the spatial location of the goods.

[0052] It is easy to understand that transportation cost refers to the transportation expenses allocated to each unit of transportation vehicle, simply referred to as transportation cost. Transportation cost includes: fixed facility cost, mobile equipment cost, and operating cost. The investment in fixed facility cost is considered a sunk cost, and in this embodiment of the invention, it can be temporarily ignored. Mobile equipment cost mainly consists of the depreciation transfer cost of the transportation vehicle, which is a variable cost. Among them, the depreciation transfer cost is not directly related to the transportation volume provided and is a fixed cost per year or month. In this embodiment of the invention, the depreciation transfer cost can be temporarily ignored.

[0053] Operating costs are variable costs directly related to the volume of transportation. Operating costs include the wages of direct operating personnel and the fuel costs of transportation vehicles. The larger the transportation volume, the greater the amount of direct operating costs. In addition to the variable costs directly related to the transportation volume, auxiliary personnel and management personnel are also required. The wages and operating expenses of auxiliary personnel and management personnel are all indirect operating costs. Therefore, the transportation costs mainly considered in this embodiment of the invention refer to the operating costs incurred in transportation.

[0054] Optionally, among the promotional methods frequently used and meticulously planned by businesses, electronic vouchers are one such method. Electronic vouchers are discount coupons that consumers can use to enjoy certain benefits when purchasing goods. These discounts can be direct price reductions or pre-defined price cuts. Using electronic vouchers to enjoy discounts is a form of price reduction promotion, simply adding a medium for price reduction. The aim is to relatively avoid the negative impacts of direct discount promotions, but still, it satisfies consumers' needs in terms of price.

[0055] In this embodiment of the invention, the usage of electronic vouchers refers to the number of electronic vouchers used by a user when purchasing goods, such as the number of electronic vouchers used at the time of payment in one or more orders within a predetermined time period.

[0056] Using scatter plot analysis to determine the relationship between order volume and transportation costs, the first correlation between the two can be obtained, such as... Figure 2 The scatter plot illustrating the first correlation shows a negative correlation between order volume and transportation cost; that is, the higher the order volume, the lower the transportation cost. However, when the order volume reaches a certain level, the transportation cost tends to reach a minimum range, for example... Figure 2 When the order volume index reaches 24, the corresponding transportation cost is 0.34.

[0057] Using scatter plot analysis to examine the impact of electronic coupon usage on the unit quantity index, a second correlation between the unit quantity index and electronic coupon usage can be obtained, such as... Figure 3As shown in the scatter plot of the second correspondence, there is a positive correlation between the order volume index and the usage of electronic coupons, that is, the more electronic coupons a user uses in each order, the higher the order volume index will be.

[0058] After obtaining the first and second correspondences mentioned above, the single quantity index can be divided into multiple single quantity index intervals as follows, but not limited to: single quantity index ≤ 15, 15 < single quantity index ≤ 20, 20 < single quantity index ≤ 25, 25 < single quantity index ≤ 30, 30 < single quantity index ≤ 35, 35 < single quantity index.

[0059] After dividing the single-quantity indicator into multiple single-quantity indicator intervals, based on the first and second correspondence relationships determined above, a hierarchical analysis method is used to determine the indicator improvement strategies corresponding to each interval. For example, when the single-quantity indicator interval is: single-quantity indicator ≤ 15, the corresponding indicator improvement strategy could be to improve the single-quantity indicator to 15 < single-quantity indicator ≤ 20; for example, when the single-quantity indicator interval is: 15 < single-quantity indicator ≤ 20, the corresponding indicator improvement strategy could be to improve the single-quantity indicator to 20 < single-quantity indicator ≤ 25; and for another example, when the single-quantity indicator interval is: 20 < single-quantity indicator ≤ 25, the corresponding indicator improvement strategy could be to improve the single-quantity indicator to 25 < single-quantity indicator ≤ 30, and so on.

[0060] After obtaining the indicator improvement strategies corresponding to each of the multiple order volume indicator ranges, for the target product storage warehouse that belongs to the target order volume indicator range, the execution suggestion information corresponding to the target order volume indicator range is generated according to the indicator improvement strategy corresponding to the target order volume indicator range, and the execution suggestion information is sent to the management terminal corresponding to the target product storage warehouse.

[0061] The target single-volume index range mentioned above can be any one of multiple single-volume index ranges. For example, it can be 15 < single-volume index ≤ 20.

[0062] Optionally, taking community group buying as an example, since there is a negative correlation between increasing order volume and transportation costs, and a positive correlation between increasing order volume and the usage of electronic coupons, generating execution suggestions based on the target order volume range's improvement strategy can determine how to increase the usage of electronic coupons to correspondingly increase the order volume within the target range. Improving the order volume within the target range can, to some extent, reduce transportation costs. For example, if the current average cost is 0.4 yuan, increasing the order volume to 24 orders could theoretically reduce fulfillment costs by 600,000 yuan for transporting 10 million items.

[0063] Community group buying platforms use marketing methods such as electronic discount vouchers to help target product storage warehouses reduce transportation costs, thereby reducing the fulfillment costs of product storage warehouse service providers. The marketing costs of community group buying platforms are less than the cost reductions for target product storage warehouse service providers, and they can also extract a certain service fee from the reduced fulfillment costs depending on the specific circumstances. Therefore, overall, reducing costs can effectively increase the economic benefits of product storage warehouse service providers.

[0064] In this embodiment of the invention, the use of electronic vouchers to increase order volume is highly feasible and effectively reduces the cost of goods storage. Electronic vouchers can also incentivize non-target consumer groups to try the products, enhance the consumer loyalty of the target consumer group, attract a fixed consumer group, and allow for targeted promotional activities. The promotional effect is even better for target consumer groups with consumption needs.

[0065] This invention, through analysis of order volume indicators, transportation costs, and electronic voucher usage across multiple product storage warehouses, establishes a first correspondence between order volume indicators and transportation costs, and a second correspondence between order volume indicators and electronic voucher usage. Based on these first and second correspondences, an indicator improvement strategy can be provided for each order volume indicator range. For example, considering the transportation costs and electronic voucher usage within each range, execution suggestions are generated based on the target order volume indicator range improvement strategy. This allows the management of the target product storage warehouse to better manage its operation and maintenance, thereby reducing its operation and maintenance costs. By reducing the cost of product storage warehouses, the fulfillment costs of community group buying are reduced, resulting in a simple and low-cost solution.

[0066] In practical applications, taking community group buying as an example, the cost of goods storage warehouses includes the platform's goods storage warehouse costs and the service provider's goods storage warehouse costs. The platform's goods storage warehouse costs include the average price per item and various platform subsidies; the service provider's goods storage warehouse costs include: rent, labor costs, management personnel salaries, delivery costs, and other costs.

[0067] Significantly reducing the cost of goods storage on the platform could lead to a large number of partner franchisees terminating their contracts. Conversely, reducing the cost of goods storage for service providers would allow more service providers to increase their economic benefits or reduce their losses, while also providing an opportunity to sign new contracts with service providers, indirectly reducing the platform's costs and achieving a virtuous cycle.

[0068] Analyzing service providers' warehouse costs reveals that transportation costs are the most significant factor influencing these costs, and are the key driver of changes in overall warehouse costs. Further analysis of transportation costs shows that the volume of goods stored in the warehouse within a given time period is a crucial factor affecting transportation costs.

[0069] In an optional embodiment of the present invention, multiple product distribution stations are associated with the target product storage warehouse, wherein the target product storage warehouse is any one of the multiple product storage warehouses; the preset time period includes at least one unit time period; the order volume index corresponding to the target product storage warehouse within the preset time period can be obtained in the following optional manner:

[0070] Obtain the number of delivery stations associated with the target product storage warehouse, and the number of orders at each delivery station within a unit time period; based on the number of delivery stations and the number of orders at each delivery station within a unit time period, determine the order volume index of the target product storage warehouse within a unit time period.

[0071] In practical applications, both express delivery and community group buying adopt centralized distribution methods, which involve using dedicated delivery stations to uniformly deliver goods to multiple express delivery stations or group buying points. This not only reduces the total inventory in the target goods storage warehouse and lowers the capital tied up in inventory, but also reduces transportation costs from the target goods storage warehouse to the express delivery station or group buying point, and improves the utilization efficiency of delivery vehicles by optimizing centralized delivery routes and delivery volume.

[0072] Optionally, the preset time period includes at least one unit time period. For example, if the preset time period is one day, then at least one unit time period can be one hour within one day. Or, if the preset time period is one month, then at least one unit time period can be one day within one month.

[0073] There can be multiple delivery stations associated with a target goods storage warehouse. These delivery stations can be understood as express delivery stations or group-buying points. By obtaining the number of delivery stations associated with the target goods storage warehouse, we can determine the number of delivery stations currently capable of delivering goods to that warehouse. Then, we obtain the order volume of each delivery station within a unit of time. For example, if the preset time period is one month, we obtain the order volume of each delivery station within one day. By dividing the number of delivery stations by the order volume of each station within the unit of time, we can determine the order volume index of the target goods storage warehouse within that unit of time.

[0074] In another optional embodiment of the present invention, multiple distribution stations are associated with the target goods storage warehouse, wherein the target goods storage warehouse is any one of the multiple goods storage warehouses; the preset time period includes at least one unit time period; the transportation cost corresponding to the target goods storage warehouse within the preset time period can be obtained in the following optional manner:

[0075] Obtain the number of distribution stations associated with the target goods storage warehouse, the distance between different distribution stations, and the number of orders at each distribution station within a unit time period; based on the number of distribution stations, the distance between stations, and the number of orders at each distribution station within a unit time period, determine the transportation cost of the target goods storage warehouse within a unit time period.

[0076] As mentioned above, there can be one or more commodity distribution stations associated with the target commodity storage warehouse. In this embodiment of the invention, since the distance between multiple different commodity distribution stations will affect the amount of transportation costs to a certain extent, the distance between multiple different commodity distribution stations will also be obtained. Then, based on the number of commodity distribution stations, the distance between stations, and the number of orders at each commodity distribution station within a unit time period, the transportation cost of the target commodity storage warehouse within a unit time period is determined.

[0077] As an optional implementation, statistical analysis of order volume indicators, transportation costs, and electronic voucher usage across multiple commodity storage warehouses is used to determine a first correspondence between order volume indicators and transportation costs, and a second correspondence between order volume indicators and electronic voucher usage. This can be achieved in the following optional manner:

[0078] The order volume indicators, transportation costs, and electronic voucher usage corresponding to multiple commodity storage warehouses are divided into different groups to obtain multiple order volume indicator ranges, multiple transportation cost ranges, and multiple electronic voucher usage ranges.

[0079] Statistical analysis is performed on multiple order volume ranges, multiple transportation cost ranges, and multiple electronic voucher usage ranges to determine the first correspondence between an order volume range and a corresponding transportation cost range, and the second correspondence between an order volume range and a corresponding set of electronic voucher usage ranges.

[0080] For example, but not limited to, the single quantity index can be divided into multiple single quantity index ranges as follows: single quantity index ≤ 15, 15 < single quantity index ≤ 20, 20 < single quantity index ≤ 25, 25 < single quantity index ≤ 30, 30 < single quantity index ≤ 35, 35 < single quantity index.

[0081] For example, but not limited to, transportation costs can be divided into several ranges as follows: 0 < transportation cost ≤ 0.1, 0.1 < transportation cost ≤ 0.2, 0.2 < transportation cost ≤ 0.3, 0.3 < transportation cost ≤ 0.4, 0.4 < transportation cost ≤ 0.5, 0.5 < transportation cost ≤ 0.6, 0.6 < transportation cost ≤ 0.7, 0.7 < transportation cost ≤ 0.8, 0.8 < transportation cost ≤ 0.9, and 0.9 < transportation cost ≤ 1.

[0082] For example, but not limited to, the usage of electronic vouchers can be divided into several usage ranges as follows: electronic voucher usage ≤ 500, 501 < electronic voucher usage ≤ 1000, 1001 < electronic voucher usage ≤ 1500, 1501 < electronic voucher usage ≤ 2000, 2001 < electronic voucher usage ≤ 2500, 2501 < electronic voucher usage ≤ 3000.

[0083] Statistical analysis of multiple order volume indicators and transportation costs revealed that a daily transportation cost reduction of 600,000 units could be achieved when the order volume indicator reaches 24. However, a result of an ideal order volume indicator of 24 is rarely attainable. Therefore, in this embodiment of the invention, the order volume indicator is divided into multiple intervals using a stratified approach: order volume indicator ≤ 15, 15 < order volume indicator ≤ 20, 20 < order volume indicator ≤ 25, 25 < order volume indicator ≤ 30, 30 < order volume indicator ≤ 35, 35 < order volume indicator, etc. Figure 4 As shown in (a)-(f), the strength of the negative correlation between the above-mentioned multiple order quantity index intervals and transportation costs is analyzed, and then the sensitive order quantity index intervals that affect transportation costs are identified, that is, the order quantity index intervals with a high degree of influence on transportation costs are analyzed.

[0084] In practical applications, determining the first correspondence between a single-volume index range and a corresponding transportation cost range can be achieved using the following steps: A univariate linear regression function is used to fit the single-volume index range and the corresponding transportation cost range to obtain a first fitted function curve; the first correspondence between the single-volume index range and the corresponding transportation cost range is determined based on the first fitted function curve.

[0085] like Figure 4 As shown in Figure (a), the x-axis represents the unit quantity index, and the y-axis represents the transportation cost. The current unit quantity index range is: unit quantity index ≤ 15, and the corresponding current transportation cost range is: 0 < transportation cost ≤ 1. A univariate linear regression function is used to fit this unit quantity index range and the corresponding transportation cost range to obtain the first fitted function curve: y = 0.003x 2-0.0968x+1.2081.

[0086] Similarly, such as Figure 4 As shown in (b), the x-axis represents the unit quantity index, and the y-axis represents the transportation cost. The current unit quantity index range is 15 < unit quantity index ≤ 20, and the corresponding transportation cost range is 0 < transportation cost ≤ 0.4. A univariate linear regression function is used to fit this unit quantity index range and the corresponding transportation cost range to obtain the first fitted function curve: y = 0.0007x 2 -0.034x+0.7906.

[0087] like Figure 4 As shown in (c), the x-axis represents the unit quantity index, and the y-axis represents the transportation cost. The current unit quantity index range is 20 < unit quantity index ≤ 25, and the corresponding transportation cost range is 0 < transportation cost ≤ 0.375. A univariate linear regression function is used to fit this unit quantity index range and the corresponding transportation cost range to obtain the first fitted function curve: y = 0.0003x 2 -0.0202x+0.6542.

[0088] like Figure 4 As shown in Figure (d), the x-axis represents the unit quantity index, and the y-axis represents the transportation cost. The current unit quantity index range is 25 < unit quantity index ≤ 30, and the corresponding transportation cost range is 0 < transportation cost ≤ 0.34. A univariate linear regression function is used to fit this unit quantity index range and the corresponding transportation cost range to obtain the first fitted function curve: y = 0.0002x 2 -0.0137x+0.5739.

[0089] like Figure 4 As shown in Figure (e), the x-axis represents the unit quantity index, and the y-axis represents the transportation cost. The current unit quantity index range is 30 < unit quantity index ≤ 35, and the corresponding transportation cost range is 0 < transportation cost ≤ 0.34. A univariate linear regression function is used to fit this unit quantity index range and the corresponding transportation cost range to obtain the first fitted function curve: y = 0.0001x 2 -0.0099x+0.5173.

[0090] like Figure 4 As shown in Figure (f), the x-axis represents the unit quantity index, and the y-axis represents the transportation cost. The current unit quantity index range is 35 < unit quantity index, and the corresponding transportation cost range is 0 < transportation cost ≤ 0.295. A univariate linear regression function is used to fit this unit quantity index range and the corresponding transportation cost range to obtain the first fitted function curve: y = 0.0005x 2-0.0066x+0.4594.

[0091] like Figure 4 As shown in (a)-(f), the greater the slope of the first fitted function curve obtained through fitting operations, the greater the impact of the unit quantity index within that unit quantity index range on transportation costs. Finally, based on the first fitted function curve, the first correspondence between a unit quantity index range and a corresponding transportation cost range is determined, leading to the conclusion that the order of sensitive unit quantity index ranges affecting transportation costs is: [unit quantity index ≤ 15] > [15 < unit quantity index ≤ 20] > [20 < unit quantity index ≤ 25] > [25 < unit quantity index ≤ 30] > [30 < unit quantity index ≤ 35] > [35 < unit quantity index]. That is, the higher the unit quantity index, the lower the transportation cost per unit. However, there is a minimum threshold for the transportation cost per unit; increasing the unit quantity index will not ultimately result in zero transportation cost per unit.

[0092] In practical applications, determining the second correspondence between a single-quantity index interval and a corresponding set of electronic discount voucher usage intervals can be achieved using the following steps: A univariate linear regression function is used to fit the single-quantity index interval and the corresponding set of electronic discount voucher usage intervals to obtain a second fitted function curve; based on the second fitted function curve, the second correspondence between the single-quantity index interval and the corresponding set of electronic discount voucher usage intervals is determined.

[0093] As an alternative embodiment, a second correspondence is determined between a single-unit indicator range and a corresponding set of electronic voucher usage ranges. This primarily involves analyzing the positive correlation between user electronic voucher usage and the increase in the single-unit indicator. First, electronic voucher usage is divided into several different usage ranges: ≤500, 501 < ≤1000, 1001 < ≤1500, 1501 < ≤2000, 2001 < ≤2500, and 2501 < ≤3000. Then, the correlation strength between electronic voucher usage and the increase in the single-unit indicator is analyzed using a stratified approach. Specifically, a univariate linear regression function is used to fit a single-unit indicator range to a corresponding set of electronic voucher usage ranges to obtain a second fitting function curve, for example, y = 0.0323x, y = 0.0119x, etc.

[0094] like Figure 5 As shown in (a), the x-axis represents the usage of electronic vouchers, and the y-axis represents the single-item quantity. The current range of electronic voucher usage is: electronic voucher usage ≤ 500, and the corresponding range of single-item quantity is: 0 < transportation cost ≤ 60.

[0095] like Figure 5 As shown in (b), the x-axis represents the usage of electronic vouchers, and the y-axis represents the single-item quantity indicator. The current range of electronic voucher usage is: 501 < electronic voucher usage ≤ 1000. The corresponding range of single-item quantity indicator is: 0 < transportation cost ≤ 60.

[0096] like Figure 5 As shown in (c), the x-axis represents the usage of electronic vouchers, and the y-axis represents the single-item quantity indicator. The current range of electronic voucher usage is: 1001 < electronic voucher usage ≤ 1500. The corresponding range of single-item quantity indicator is: 0 < transportation cost ≤ 60.

[0097] like Figure 5 As shown in (d), the x-axis represents the usage of electronic vouchers, and the y-axis represents the single-item quantity index. The current range of electronic voucher usage is: 1501 < electronic voucher usage ≤ 1500. The corresponding range of single-item quantity index is: 0 < transportation cost ≤ 60.

[0098] like Figure 5 As shown in (e), the x-axis represents the usage of electronic vouchers, and the y-axis represents the single-item quantity indicator. The current range of electronic voucher usage is: 2001 < electronic voucher usage ≤ 2500. The corresponding range of single-item quantity indicator is: 0 < transportation cost ≤ 60.

[0099] like Figure 5 As shown in (f), the x-axis represents the usage of electronic vouchers, and the y-axis represents the single-item quantity. The current range of electronic voucher usage is: 2501 < electronic voucher usage ≤ 3000. The corresponding range of single-item quantity is: 0 < transportation cost ≤ 60.

[0100] like Figure 5 As shown in (a)-(f), the greater the slope of the second fitting function curve obtained through fitting operation, the greater the impact of the electronic discount voucher usage in the electronic discount voucher usage range on the single-volume index. Finally, based on the second fitting function curve, a second correspondence between a single-volume index range and a corresponding set of electronic discount voucher usage ranges is determined, and the conclusion is that the electronic discount voucher usage ranges affecting the single-volume index are ordered from high to low as follows: [electronic discount voucher usage ≤ 500] > [501 < electronic discount voucher usage ≤ 1000] > [1001 < electronic discount voucher usage ≤ 1500] > [1501 < electronic discount voucher usage ≤ 2000] > [2001 < electronic discount voucher usage ≤ 2500] > [2501 < electronic discount voucher usage ≤ 3000].

[0101] Subsequently, based on the established first and second correspondences, we can determine the indicator improvement strategy for optimizing the order volume indicator using electronic coupons. That is, how to provide the best effect for optimizing the order volume indicator using electronic coupons, so that the optimized order volume indicator leads to a relatively better reduction in transportation costs. We can conclude through the above hierarchical analysis that the indicator improvement strategy corresponding to the order volume indicator space of 15 < order volume indicator ≤ 20 provides a relatively better effect for reducing transportation costs.

[0102] To facilitate understanding, let's illustrate this with a real-world application scenario. Taking community group buying as an example, after a consumer places an order and pays the corresponding amount, a fulfillment collaboration algorithm generates a fulfillment order and a logistics order for the paid order. These are calculated in real-time by the sales department. The sales department then performs further calculations based on the fulfillment order and logistics order.

[0103] For example, calculate the order volume index of the goods storage warehouse for the past 30 days every day, search for target goods storage warehouses with an average order volume index of 15 < order volume index ≤ 20, observe the order volume index of the target goods storage warehouses at 6 pm every day (not fixed and can be changed according to specific circumstances), and coordinate corresponding marketing strategies with relevant personnel in a timely manner. For example, if the distribution of electronic discount vouchers given by relevant personnel is biased, then intervene; if the target goods storage warehouse reaches the order volume index, then stop distributing electronic discount vouchers.

[0104] For example, calculate the order volume index of the goods storage warehouse for the past 30 days every day, search for target goods storage warehouses with an average order volume index in the range of 20 < order volume index ≤ 25, observe the order volume index of the target goods storage warehouses at 6 pm every day (not fixed and can be changed according to specific circumstances), and coordinate corresponding marketing strategies with relevant personnel in a timely manner. For example, if the distribution of electronic discount vouchers given by relevant personnel is biased, then intervene; if the target goods storage warehouse reaches the order volume index, then stop distributing electronic discount vouchers.

[0105] Another optional implementation is that if the order quantity index is detected to be <10, the network age is calculated to be less than 90 days per day, the order quantity of the goods distribution station within the unit time period (month, day) is less than 2000, the order quantity index is <10, and the inflow of economic benefits is less than the expenditure of the target goods storage warehouse, and relevant personnel are consulted to shut down the target goods storage warehouse.

[0106] The following will describe in detail one or more embodiments of the operation and maintenance apparatus for a commodity storage warehouse according to the present invention. Those skilled in the art will understand that these apparatuses can be configured using commercially available hardware components through the steps taught in this solution.

[0107] Figure 6This is a schematic diagram of the operation and maintenance device for a commodity storage warehouse provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the device includes: an acquisition module 11, a statistical analysis module 12, a determination module 13, and a generation module 14.

[0108] The acquisition module 11 is used to acquire the order volume indicators, transportation costs, and electronic discount voucher usage for multiple product storage warehouses within a preset time period.

[0109] The statistical analysis module 12 is used to statistically analyze the order quantity indicators, transportation costs, and electronic discount voucher usage corresponding to the multiple commodity storage warehouses, so as to determine the first correspondence between the order quantity indicators and the transportation costs, and the second correspondence between the order quantity indicators and the electronic discount voucher usage.

[0110] The determination module 13 is used to determine the indicator improvement strategy corresponding to each of the multiple single-volume indicator intervals based on the first correspondence and the second correspondence.

[0111] The generation module 14 is used to generate execution suggestion information corresponding to the target order volume indicator range for the target product storage warehouse that belongs to the target order volume indicator range, according to the indicator improvement strategy corresponding to the target order volume indicator range, wherein the target order volume indicator range is any one of multiple order volume indicator ranges; and send the execution suggestion information to the management terminal corresponding to the target product storage warehouse.

[0112] Optionally, the target product storage warehouse is associated with multiple product distribution stations, wherein the target product storage warehouse is any one of the multiple product storage warehouses; the preset time period includes at least one unit time period; the acquisition module includes: a first acquisition unit, used to acquire the number of product distribution stations associated with the target product storage warehouse, and the order volume of each product distribution station within the unit time period; and a first determination unit, used to determine the order volume index of the target product storage warehouse within the unit time period based on the number of product distribution stations and the order volume of each product distribution station within the unit time period.

[0113] Optionally, the target goods storage warehouse is associated with multiple goods distribution stations, wherein the target goods storage warehouse is any one of the multiple goods storage warehouses; the preset time period includes at least one unit time period; the acquisition module further includes: a second acquisition unit, used to acquire the number of goods distribution stations associated with the target goods storage warehouse, the distance between different goods distribution stations, and the number of orders for each goods distribution station within the unit time period; and a second determination unit, used to determine the transportation cost of the target goods storage warehouse within the unit time period based on the number of goods distribution stations, the distance between stations, and the number of orders for each goods distribution station within the unit time period.

[0114] Optionally, the statistical analysis module further includes: a grouping unit, used to divide the order quantity indicators, transportation costs, and electronic voucher usage corresponding to the multiple commodity storage warehouses into different groups to obtain multiple order quantity indicator intervals, multiple transportation cost intervals, and multiple electronic voucher usage intervals; and a statistical analysis unit, used to statistically analyze the multiple order quantity indicator intervals, multiple transportation cost intervals, and multiple electronic voucher usage intervals to determine a first correspondence between an order quantity indicator interval and a corresponding transportation cost interval, and a second correspondence between an order quantity indicator interval and a corresponding set of electronic voucher usage intervals.

[0115] Optionally, the statistical analysis unit is specifically used to: perform a fitting operation on a single quantity index interval and a corresponding transportation cost interval using a univariate linear regression function to obtain a first fitting function curve; and determine the first correspondence between the single quantity index interval and the corresponding transportation cost interval based on the first fitting function curve.

[0116] Optionally, the statistical analysis unit is further configured to: perform a fitting operation using a univariate linear regression function on a single quantity index interval and a corresponding set of electronic discount voucher usage intervals to obtain a second fitting function curve; and determine a second correspondence between the single quantity index interval and the corresponding set of electronic discount voucher usage intervals based on the second fitting function curve.

[0117] In one possible design, the above Figure 6 The structure of the maintenance device for the commodity storage warehouse shown can be implemented as an electronic device. For example... Figure 7 As shown, the electronic device may include: a processor 21, a memory 22, and a communication interface 23. The memory 22 stores executable code, which, when executed by the processor 21, enables the processor 21 to at least implement the operation and maintenance method for the commodity storage warehouse provided in the foregoing embodiments.

[0118] In addition, embodiments of the present invention provide a non-transitory machine-readable storage medium storing executable code, which, when executed by a processor of an electronic device, enables the processor to at least implement the operation and maintenance method of the commodity storage warehouse provided in the foregoing embodiments.

[0119] The device embodiments described above are merely illustrative. The network elements described as separate components may or may not be physically separate. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of a necessary general-purpose hardware platform, or by a combination of hardware and software. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a computer product. The present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for operating and maintaining a commodity storage warehouse, characterized in that, include: Within a preset time period, obtain the order volume indicators, transportation costs, and electronic discount voucher usage for multiple product storage warehouses. The order volume indicator refers to the number of fulfilled orders in the product storage warehouse within the preset time period divided by the number of target group-buying points. The number of target group-buying points is the number of group-buying points with orders within the preset time period. Statistical analysis is performed on the order volume indicators, transportation costs, and electronic discount voucher usage corresponding to the multiple commodity storage warehouses to determine the first correspondence between the order volume indicators and the transportation costs, and the second correspondence between the order volume indicators and the electronic discount voucher usage. Based on the first correspondence and the second correspondence, determine the indicator improvement strategies corresponding to each of the multiple single-volume indicator ranges; For a target product storage warehouse that falls within the target order volume indicator range, execution suggestion information corresponding to the target order volume indicator range is generated based on the indicator improvement strategy corresponding to the target order volume indicator range. The target order volume indicator range is any one of multiple order volume indicator ranges. The execution suggestion information is sent to the management terminal corresponding to the target product storage warehouse.

2. The method according to claim 1, characterized in that, The target product storage warehouse is associated with multiple product distribution stations, wherein the target product storage warehouse is any one of the multiple product storage warehouses; the preset time period includes at least one unit time period; obtaining the order volume indicator corresponding to the target product storage warehouse within the preset time period includes: Obtain the number of the product distribution stations associated with the target product storage warehouse, and the number of orders for each product distribution station within the unit time period; Based on the number of the commodity distribution stations and the number of orders at each of the commodity distribution stations within the unit time period, the order volume index of the target commodity storage warehouse within the unit time period is determined.

3. The method according to claim 1, characterized in that, The target product storage warehouse is associated with multiple product distribution stations, wherein the target product storage warehouse is any one of the multiple product storage warehouses; the preset time period includes at least one unit time period; obtaining the transportation cost corresponding to the target product storage warehouse within the preset time period includes: The number of the commodity distribution stations associated with the target commodity storage warehouse, the distance between different commodity distribution stations, and the number of orders for each commodity distribution station within the unit time period are obtained. Based on the number of the commodity distribution stations, the distance between the stations, and the number of orders at each of the commodity distribution stations within the unit time period, the transportation cost of the target commodity storage warehouse within the unit time period is determined.

4. The method according to claim 1, characterized in that, The statistical analysis of the order volume indicators, transportation costs, and electronic voucher usage corresponding to the multiple commodity storage warehouses, to determine the first correspondence between the order volume indicators and the transportation costs, and the second correspondence between the order volume indicators and the electronic voucher usage, includes: The order volume indicators, transportation costs, and electronic voucher usage corresponding to the multiple commodity storage warehouses are each divided into different groups to obtain multiple order volume indicator ranges, multiple transportation cost ranges, and multiple electronic voucher usage ranges. Statistical analysis is performed on the multiple order volume index intervals, multiple transportation cost intervals, and multiple electronic discount voucher usage intervals to determine a first correspondence between an order volume index interval and a corresponding transportation cost interval, and a second correspondence between an order volume index interval and a corresponding set of electronic discount voucher usage intervals.

5. The method according to claim 4, characterized in that, Determining the first correspondence between a single-volume index range and a corresponding transportation cost range includes: A univariate linear regression function is used to fit a single quantity index range to a corresponding transportation cost range to obtain the first fitted function curve. The first correspondence between a single-volume index range and a corresponding transportation cost range is determined based on the first fitted function curve.

6. The method according to claim 4, characterized in that, The determination of the second correspondence between the single-volume index range and the corresponding set of electronic discount voucher usage ranges includes: A univariate linear regression function is used to fit a single quantity index interval and a corresponding set of electronic discount voucher usage intervals to obtain a second fitting function curve; The second correspondence between a single quantity index interval and a corresponding set of electronic discount voucher usage intervals is determined based on the second fitting function curve.

7. An operation and maintenance device for a commodity storage warehouse, characterized in that, include: The acquisition module is used to acquire the order volume indicators, transportation costs and electronic discount voucher usage of multiple product storage warehouses within a preset time period. The order volume indicator refers to the number of fulfilled orders in the product storage warehouse within the preset time period divided by the number of target group buying points. The number of target group buying points is the number of group buying points with orders within the preset time period. The statistical analysis module is used to statistically analyze the order volume indicators, transportation costs, and electronic discount voucher usage corresponding to the multiple commodity storage warehouses, so as to determine the first correspondence between the order volume indicators and the transportation costs, and the second correspondence between the order volume indicators and the electronic discount voucher usage. The determination module is used to determine the indicator improvement strategy corresponding to each of the multiple single-volume indicator intervals based on the first correspondence relationship and the second correspondence relationship; The generation module is used to generate execution suggestion information corresponding to the target order volume indicator range for target product storage warehouses that belong to the target order volume indicator range, based on the indicator improvement strategy corresponding to the target order volume indicator range, wherein the target order volume indicator range is any one of multiple order volume indicator ranges; and send the execution suggestion information to the management terminal corresponding to the target product storage warehouse.

8. The apparatus according to claim 7, characterized in that, The target product storage warehouse is associated with multiple product distribution stations, wherein the target product storage warehouse is any one of the multiple product storage warehouses; the preset time period includes at least one unit time period; the acquisition module includes: The first acquisition unit is used to acquire the number of the commodity delivery stations associated with the target commodity storage warehouse, and the number of orders for each commodity delivery station within the unit time period; The first determining unit is used to determine the order volume index of the target commodity storage warehouse within the unit time period based on the number of commodity distribution stations and the order volume of each commodity distribution station within the unit time period.

9. The apparatus according to claim 7, characterized in that, The target product storage warehouse is associated with multiple product distribution stations, wherein the target product storage warehouse is any one of the multiple product storage warehouses; the preset time period includes at least one unit time period; the acquisition module further includes: The second acquisition unit is used to acquire the number of commodity delivery stations associated with the target commodity storage warehouse, the distance between different commodity delivery stations, and the number of orders for each commodity delivery station within the unit time period; The second determining unit is used to determine the transportation cost of the target commodity storage warehouse within the unit time period based on the number of commodity distribution stations, the distance between the stations, and the number of orders at each commodity distribution station within the unit time period.

10. An electronic device, characterized in that, include: The device includes a memory, a processor, and a communication interface; wherein the memory stores executable code, and when the executable code is executed by the processor, the processor performs the operation and maintenance method of the commodity storage warehouse as described in any one of claims 1 to 6.

11. A non-transitory machine-readable storage medium, characterized in that, The non-transitory machine-readable storage medium stores executable code, which, when executed by a processor of an electronic device, causes the processor to perform the operation and maintenance method of the commodity storage warehouse as described in any one of claims 1 to 6.

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