Article circulation method, device and terminal equipment

Abstract

The embodiments of the present disclosure disclose an item circulation method, apparatus, and terminal device. A specific implementation of the method includes: detecting the inventory of the target warehouse and the characteristics of the items stored in the target warehouse to obtain detection information; in response to the detection information meeting a predetermined condition, obtaining an item information set set; generating a set of first candidate item information sets and a set of second candidate item information sets; generating a first optimization information set and a second optimization information set; generating a target item attribute adjustment result; receiving item attribute adjustment start information input by the user; and controlling the vehicle corresponding to the item attribute adjustment start information to complete item circulation scheduling-related operations. This implementation generates the first optimization information set and the second optimization information set according to the corresponding rules, can quickly adjust item attributes, has strong scalability, and high accuracy, thereby improving the operational level of item circulation to avoid item loss caused by untimely circulation.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and more particularly to an article circulation method, apparatus, and terminal device. Background Art

[0002] In today's era of online platforms, ensuring scientific and reasonable warehousing inventory and equipping corresponding item circulation processes have become indispensable links. In previous system designs, after the user determines a series of items, the corresponding logistics process is initiated based on the storage status of the items in the warehouse to ensure the timely circulation of items within the shelf life, reduce item waste and lower storage costs. In order to promptly initiate the circulation of items, the value transfer process of items is generally carried out through manually set value transfer sequence rules to obtain the global optimal value transfer result. That is, the optimal value transfer result is calculated using linear programming methods, thereby initiating the optimal item circulation service to ensure the timely circulation of items.

[0003] However, when controlling the flow of goods by determining the results of value transfer, the following technical problems often arise:

[0004] Existing technologies use exhaustive and greedy algorithms based on artificially set rules to determine the optimal value transfer results. This has high computational complexity and is time-consuming, and cannot meet the real-time requirements of item circulation. As a result, items in storage may exceed their shelf life, rot, or become damaged, resulting in a waste of resources and enormous pressure on warehousing. Summary of the Invention

[0005] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] Some embodiments of the present disclosure propose an item circulation method, apparatus, and terminal device to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide an item circulation method, the method comprising: detecting the inventory of a target warehouse and the characteristics of items stored in the target warehouse to obtain detection information; in response to the detection information meeting a predetermined condition, obtaining an item information set set; generating a set of first candidate item information set sets and a set of second candidate item information set sets; generating a first optimization information set and a second optimization information set; generating a target item attribute adjustment result; receiving item attribute adjustment start information input by a user; and according to the target item attribute adjustment result and the item attribute adjustment start information, controlling a vehicle corresponding to the item attribute adjustment start information to complete item circulation scheduling related operations.

[0008] In a second aspect, some embodiments of the present disclosure provide an item circulation device, which includes: a detection unit configured to detect the inventory of a target warehouse and the characteristics of items stored in the target warehouse to obtain detection information; an acquisition unit configured to acquire an item information set set in response to the detection information meeting a predetermined condition, wherein the item information set set includes a first number of item information sets, the item information set includes an item type feature set, an item quantity, and a rule set, and the rules in the rule set are rules that constrain the item circulation combination mode; a first generation unit configured to generate a set of first candidate item information set sets and a set of second candidate item information set sets based on the item information set set; a second generation unit configured to generate a set of first candidate item information set sets and a set of second candidate item information set sets based on the first candidate item information set set and the second candidate item information set set; A set of item information sets is selected to generate a first optimization information set and a second optimization information set, wherein the first optimization information in the first optimization information set is the optimization information of the item combination method, and the second optimization information in the second optimization information set is the optimization information of the item combination method; a third generating unit is configured to generate a target item attribute adjustment result based on the first optimization information set and the second optimization information set, wherein the target item attribute adjustment result is the item attribute adjustment result obtained after the item is optimized combined; a receiving unit is configured to receive item attribute adjustment start information input by a user; a control unit is configured to control a vehicle corresponding to the item attribute adjustment start information to complete item flow scheduling related operations according to the target item attribute adjustment result and the item attribute adjustment start information.

[0009] In a third aspect, some embodiments of the present disclosure provide a terminal device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement any method as described in the first aspect.

[0010] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the item circulation method of some embodiments of the present disclosure, a set of first candidate item information sets and a set of second candidate item information sets can be determined based on an item information set set, and a first optimized information set and a second optimized information set can be generated according to corresponding rules, respectively. The method has strong scalability, a fast response speed for controlling item attribute adjustment, a high accuracy rate of target item attribute adjustment results obtained after the item optimization combination, and a value transfer result is determined based on the target item attribute adjustment results, and a vehicle is activated in time to transport the corresponding items, thereby improving the operational level of item circulation, and timely activating the vehicle to realize item circulation to avoid item loss caused by untimely circulation. Specifically, the inventors found that the reason for the current low level of item circulation is that the existing technology uses exhaustive and greedy algorithms based on artificially set rules to determine the optimal value transfer result, which has high computational complexity, is time-consuming, and cannot meet the real-time requirements of item circulation, resulting in problems such as items in the warehouse exceeding their shelf life, rotting and being damaged, causing resource waste and placing enormous pressure on the warehouse. Based on this, some embodiments of the present disclosure first obtain a set of item information sets, wherein the set of item information sets includes a first number of item information sets, each including an item type feature set, an item quantity, and a rule set, wherein the rules in the rule set are rules that constrain the item flow combination method. Secondly, based on the set of item information sets, a set of first candidate item information sets and a set of second candidate item information sets are generated. Thirdly, based on the set of first candidate item information sets and the set of second candidate item information sets, a first optimization information set and a second optimization information set are generated. The first optimization information in the first optimization information set is generated based on the item type feature, while the second optimization information in the second optimization information set is not generated based on the item type feature. Then, based on the first optimization information set and the second optimization information set, a target item attribute adjustment result is generated. The target item attribute adjustment result is the item attribute adjustment result obtained after the item is optimized. Finally, item attribute adjustment initiation information input by a user is received, and based on the target item attribute adjustment result and the item attribute adjustment initiation information, operations related to item attribute adjustment are performed, and the vehicle corresponding to the item attribute adjustment initiation information is controlled to perform operations related to item flow scheduling. This method generates a first and second optimized information set based on whether item type characteristics affect information. Then, based on the first and second optimized information sets, it generates a target item attribute adjustment result controlled by the optimized combination of items. This eliminates the need for exhaustive or greedy algorithms based on various rules. This method is fast, time-efficient, and has low computational complexity, meeting the responsiveness requirements of large-scale item flows. By completing the item attribute adjustment operation, vehicles are promptly activated to complete the delivery of the corresponding items, avoiding damage to items in storage and reducing resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0012] Figure 1 is an architectural diagram of an exemplary system in which some embodiments of the present disclosure may be applied;

[0013] Figure 2 is a flow chart of some embodiments of the item circulation method according to the present disclosure;

[0014] Figure 3 This is an example authorization prompt box;

[0015] Figure 4 is a flow chart of some embodiments of the article circulation device according to the present disclosure;

[0016] Figure 5 It is a structural diagram of a terminal device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0018] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0019] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0021] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0022] Figure 1 An exemplary system architecture 100 is shown to which an embodiment of the item flow method of the present disclosure can be applied.

[0023] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0024] Users can use terminal devices 101, 102, 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be installed with various communication client applications, such as information processing applications, item flow applications, data analysis applications, etc.

[0025] Terminal devices 101, 102, and 103 can be either hardware or software. When hardware is used, they can be various terminal devices with display screens, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers. When software is used, they can be installed in the terminal devices listed above. They can be implemented as multiple software programs or software modules (e.g., to provide input of a set of item information), or as a single software program or software module. This is not specifically limited here.

[0026] The server 105 may be a server that provides various services, such as a server that stores the set of identifications of items to be adjusted input by the terminal devices 101, 102, and 103. The server may process the received set of item information and feed back the processing results (e.g., the result of adjusting the attributes of the target item) to the terminal device.

[0027] It should be noted that the item circulation method provided in the embodiment of the present disclosure can be executed by the server 105 or by a terminal device.

[0028] It should be noted that the server 105 can also directly store the item information set locally, and the server 105 can directly extract the local item information set and obtain the target item attribute adjustment result after processing. At this time, the exemplary system architecture 100 may not include the terminal devices 101, 102, 103 and the network 104.

[0029] It should also be noted that the terminal devices 101, 102, and 103 may also be installed with an item circulation application, and in this case, the processing method may also be executed by the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also not include the server 105 and the network 104.

[0030] It should be noted that server 105 can be either hardware or software. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers or as a single server. When the server is software, it can be implemented as multiple software programs or software modules (for example, to provide goods circulation services) or as a single software program or software module. This is not specifically limited here.

[0031] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0032] Continue to refer Figure 2 , shows a process 200 of some embodiments of the article circulation method according to the present disclosure. The article circulation method includes the following steps:

[0033] Step 201 : Detect the inventory of a target warehouse and the characteristics of the items stored in the target warehouse to obtain detection information.

[0034] In some embodiments, the execution entity of the item circulation method (for example Figure 1 The server (shown) detects the inventory of the target warehouse and the characteristics of the items stored in the target warehouse to obtain detection information. Specifically, the target warehouse can be a logistics warehouse of the network platform. The characteristics of the items stored in the target warehouse can be the shelf life of the items, the temperature of the items, or the weight of the items.

[0035] Step 202: In response to the detection information meeting a predetermined condition, obtaining an item information set of a target warehouse.

[0036] In some embodiments, the execution entity of the item circulation method (for example Figure 1In response to the detection information meeting a predetermined condition, the server (shown in FIG. 1 ) obtains a set of item information sets from a target warehouse. Specifically, the predetermined condition may be within a shelf life. In response to the shelf life meeting the predetermined condition, the item information set from the target warehouse is obtained. The predetermined condition may also be that the temperature is within a reasonable value range. In response to the temperature meeting the predetermined condition, the item information set from the target warehouse is obtained. The predetermined condition may also be that the weight is within a reasonable range. In response to the weight meeting the predetermined condition, the item information set from the target warehouse is obtained. The item information set includes a first number of item information sets. The item information set includes an item type feature set, an item quantity, and a rule set. The rules in the rule set are rules that constrain the item flow combination. Specifically, the item type features in the item type feature set may be features of the basic stockkeeping unit (SKU) for the item's inventory inflow and outflow. Specifically, a SKU may be a large red package of apples or a small bottle of yogurt. Specifically, the item type feature set may correspond to an item acquisition request, that is, one item information set corresponds to one item acquisition request. An example of an item information set can be seen in Table 1.

[0037]

[0038] Table 1

[0039] Here, sku1, sku2, sku3, and sku4 represent the item type characteristics corresponding to the first, second, third, and fourth SKUs, respectively. Specifically, the rules in the rule set constrain the combination of item flows. An example of a rule set can be found in Table 2.

[0040]

[0041] Table 2

[0042] Among them, p1, p2, p3, and p4 are the rule identifiers of the rules in the rule set. The "specific method, conditions, restrictions, and corresponding items" are the content of the rule. A row in Table 2 corresponds to a rule. The rule set in Table 2 includes 4 rules.

[0043] Step 203: Generate a set of first candidate item information sets and a set of second candidate item information sets based on the item information set set.

[0044] In some embodiments, the execution entity generates a set of first candidate item information sets and a set of second candidate item information sets based on the set of item information sets. Specifically, for each item information set in the set of item information sets, the first candidate item information set and the second candidate item information set are determined based on the set of rules for the item information set, thereby obtaining a set of first candidate item information sets and a set of second candidate item information sets.

[0045] Optionally, for each rule in the rule set, in response to the rule being a first-type rule, an initial first candidate item information set corresponding to the rule is generated based on the item information set set to obtain an initial first candidate item information set set. Specifically, the first-type rule may be a rule that indicates that the value transfer result brought about by the rule does not change with different item combinations. The initial first candidate item information set includes an initial first candidate item type feature set, an initial first candidate item quantity, and an initial first candidate item corresponding rule. For the initial first candidate item information set set, a set of first candidate item information set sets is generated based on the initial first candidate item corresponding rule. The first candidate item information sets in the first candidate item information set set include a first candidate item type feature set, a first candidate item quantity, and a first candidate item corresponding rule, and the first candidate item corresponding rule in each first candidate item information set in the first candidate item information set set is the same.

[0046] Optionally, for each rule in the rule set, in response to the rule being a second-type rule, an initial second candidate item information set corresponding to the rule is generated based on the item information set set to obtain an initial second candidate item information set set. Specifically, the second-type rule may be a rule that indicates that the value transfer result brought about by the rule will vary with different item combinations. The initial second candidate item information set includes an initial second candidate item type feature set, an initial second candidate item quantity, and an initial second candidate item corresponding rule. For the initial second candidate item information set set, a set of second candidate item information set sets is generated based on the initial second candidate item corresponding rule, wherein the second candidate item information sets in the second candidate item information set set include a second candidate item type feature set, a second candidate item quantity, and a second candidate item corresponding rule, and the second candidate item corresponding rule in each second candidate item information set in the second candidate item information set set is the same.

[0047] Specifically, based on the example in Table 2, for each of the four rules in the item information set, determine whether the resulting value transfer varies with the item combination. Rule p1 provides a 10 yuan value transfer for any item with 3 items. Regardless of the item combination matching this rule, such as one each of sku1, sku2, and sku3, or three items of sku1, the resulting value transfer remains 10 yuan, independent of the item combination. Rule p2 provides a 2 yuan discount for each item with 3 items of the same item. Regardless of the item combination, the resulting value transfer remains 2*3=6 yuan, independent of the item combination. Rule p3 provides a 10 yuan discount for every item with 3 items of different items. The resulting value transfer is dependent on the item combination and is determined by the price of the lowest-priced item in the item combination. Therefore, the set of item information corresponding to p1 and p2 is determined as the first candidate item information set. The item information corresponding to p3 is determined as the second candidate item information set. Specifically, the second candidate item information set includes one second candidate item. Specifically, the first candidate item information set can be determined as the item information set that can generate results based on SKU modeling, and the second candidate item information set can be determined as the item information set that can generate results based on sub-strategy modeling. Specifically, SKU modeling uses the number of SKUs used in each rule as the independent variable for linear programming modeling. Sub-strategy modeling uses the number of rule usages as the independent variable for linear programming modeling. Linear programming modeling involves three key elements: the definition of decision variables, the definition of the objective function, and the definition of constraints. The objective function is to maximize the item attribute adjustment result. The item attribute adjustment result of SKU modeling requires calculating the item attribute adjustment result of the rule based on the number of SKUs used. The item attribute adjustment result amount of sub-strategy modeling is calculated by multiplying the item attribute adjustment result of the rule generated by the item combination by the number of rule usages. Constraints include, but are not limited to, one of the following: item quantity constraint (cannot exceed the number of items purchased by the user), item condition constraint (number of different items, number of identical items, or number of any item), number limit constraint (total number of times the item can be used), and constraint that multiple rules cannot be hit simultaneously.

[0048] Step 204 : Generate a first optimized information set and a second optimized information set based on the set of first candidate item information sets and the set of second candidate item information sets.

[0049] In some embodiments, the execution entity generates a first optimization information set and a second optimization information set based on the set of first candidate item information sets and the set of second candidate item information sets. The first optimization information in the first optimization information set is optimization information for item combinations, and the second optimization information in the second optimization information set is optimization information for item combinations. Optionally, the first optimization information in the first optimization information set is generated based on item type characteristics, while the second optimization information in the second optimization information set is not generated based on item type characteristics.

[0050] Optionally, for each first candidate item information set in the set of first candidate item information sets, first optimization information for the first candidate item information set is generated based on the first candidate item corresponding rule corresponding to the first candidate item information set, to obtain a first optimization information set. Optionally, for each first candidate item information set in the set of first candidate item information sets, a first constraint rule set is determined based on the first candidate item corresponding rule corresponding to the first candidate item information set. A first decision variable set and a first objective function are determined based on the first candidate item information set, where the first decision variable in the first decision variable set is a combination of item type features in the first candidate item information set. The set of the first constraint rule set, the first decision variable set, and the first objective function is determined as the first optimization information.

[0051] Specifically, according to the example in Table 2, it is determined that the first constraint rule set includes p1 and p2.

[0052] Let x 11 is the number of sku1 used, x 12 is the number of sku2 used, x 13 is the number of times sku3 is used, and z1 is the number of times rule p1 is used. 11 、x 12 、x 13 The set of z1 and z2 is determined as the first decision variable set. The first objective function is determined as follows:

[0053] (Objective function) max 9*x 11 +8*x 12 +7*x 13 -10*z1

[0054]

[0055] Let x 12 is the number of sku1 used, x 22 is the number of sku2 used, x 24 is the number of sku4 used, z 21is the number of times sku1 hits rule p2, z 22 is the number of times sku2 hits rule p2, z 24 is the number of times sku4 hits rule p2. 12 、x 22 、x 24 、z 21 、z 22 、z 24 The set of is included in the first decision variable set. The following function is included in the first objective function:

[0056] (Objective function) max x 21 *2+x 22 *2+x 24 *2

[0057] (Requires at least 3 identical items)

[0058] For each second candidate item information set in the set of second candidate item information sets, second optimization information for the second candidate item information set is generated based on the second candidate item corresponding rule corresponding to the second candidate item information set, to obtain a second optimization information set. Optionally, a second constraint rule set is determined based on the second candidate item corresponding rule corresponding to the second candidate item information set. A second decision variable set and a second objective function are determined based on the first candidate item information set, where the second decision variable in the second decision variable set is the number of item type features included in the second candidate item information set. The second constraint rule set, the second decision variable set, and the second objective function are determined as the second optimization information.

[0059] Specifically, according to the example in Table 2, it is determined that the second constraint rule set includes p3. Let the item combinations (sub-strategies) that satisfy p3 be (sku1, sku2, sku3), (sku1, sku2, sku4), (sku2, sku3, sku4), (sku1, sku3, sku4). The number of times (sku1, sku2, sku3) is used is x 31 , the number of times (sku1, sku2, sku4) is used is x 32 , the number of times (sku2, sku3, sku4) is used is x 33 , the number of times (sku1, sku3, sku4) is used is x 34 . 31 、x 32 、x 33 The set of is determined as the second decision variable set. The second objective function is determined as follows:

[0060] (Objective function) max x 31 *7+x 32 *6+x 33 *6+x 34 *6

[0061] Step 205: Generate a target item attribute adjustment result based on the first optimization information set and the second optimization information set.

[0062] In some embodiments, the execution entity generates a target item attribute adjustment result based on the first optimization information set and the second optimization information set. The target item attribute adjustment result is the item attribute adjustment result obtained after the item is optimized and combined. Optionally, a target optimization function and a target optimization rule set are determined based on the first optimization information set and the second optimization information set. Specifically, the target optimization function is a function composed of the first objective function and the second objective function. The target optimization rule set is a set of the first constraint rule set and the second constraint rule set. The item quantity constraint rule and the hit rule are determined, wherein the item quantity constraint rule is a rule for controlling the item quantity, and the hit rule is a rule for controlling the number of item circulations. Optimization processing is performed based on the target optimization function, the target optimization rule set, the quantity constraint rule, and the hit rule to generate a target optimization result.

[0063] Specifically, according to the example in Table 2, the set x of the first decision variable set and the second decision variable set is 11 、x 12 、x 13 ,z1,x 12 、x 22 、x 24 、z 21 、z 22 、z 24 、x 31 、x 32 、x 33 Determine the decision variables for linear programming. Determine the following objective optimization function, item quantity constraint rules, and hit rules:

[0064] (Objective Function)

[0065] max x 11 *9+x 12 *8+x 13 *7-10*z1+x 21 *2+x 22 *2+x 24 *2

[0066] +x 31 *7+x 32 *6+x 33 *6+x 34 *6

[0067]

[0068] After obtaining the objective function, item quantity constraint rules, and hit rules, an integer linear programming method can be used to generate the target item attribute adjustment result. Specifically, the target item attribute adjustment result can be the target optimization result obtained by the integer linear programming method. Specifically, the integer linear programming method can be to restrict (all or some) variables in the linear programming to integers.

[0069] Step 206: Receive item attribute adjustment start information input by the user.

[0070] In some embodiments, the execution entity receives item attribute adjustment start information input by the user. Specifically, in response to detecting an operation authorization signal, the item attribute adjustment start information input by the user is obtained. The operation authorization signal may be a signal generated by the user performing a target operation on the target control. The target control may be included in an authorization prompt box. The authorization prompt box may be displayed on the target terminal device. The target terminal device may be a terminal device logged in with the corresponding account of the user. The terminal device may be a "mobile phone" or a "computer". The target operation may be a "click operation" or a "slide operation". The target control may be a "confirm button".

[0071] As an example, the authorization prompt box can be as follows Figure 3 As shown. The authorization prompt box may include: a prompt information display portion 301 and a control 302. The prompt information display portion 301 may be used to display prompt information. The prompt information may be "Do you allow the item attribute adjustment startup information to be obtained?" The control 302 may be a "confirm button" or a "cancel button."

[0072] Step 207 : According to the target item attribute adjustment result and the item attribute adjustment start information, the vehicle corresponding to the item attribute adjustment start information is controlled to complete operations related to item flow scheduling.

[0073] In some embodiments, the execution entity controls the vehicle corresponding to the item attribute adjustment initiation information to complete operations related to item flow scheduling based on the target item attribute adjustment result and the item attribute adjustment initiation information. Specifically, based on the item attribute adjustment initiation information input by the user, the vehicle corresponding to the item attribute adjustment initiation information is controlled to start, and the vehicle completes the item flow corresponding to the item attribute adjustment initiation information. Specifically, the item attribute adjustment operation may be executing a corresponding value transfer operation based on the target item attribute adjustment result. In response to the completion of the value transfer operation, the vehicle starts and completes the item flow scheduling operation.

[0074] Figure 2 An embodiment provided herein has the following beneficial effects: detecting the inventory of a target warehouse and the characteristics of the items stored therein to obtain detection information; obtaining an item information set in response to the detection information meeting predetermined conditions; generating a set of first candidate item information sets and a set of second candidate item information sets; generating a first optimized information set and a second optimized information set; generating a target item attribute adjustment result; receiving item attribute adjustment initiation information input by a user from a front-end device; and controlling the front-end device to complete related operations for item attribute adjustment. This embodiment determines the first candidate item information set and the second candidate item information set based on the item information set, and generates the first optimized information set and the second optimized information set according to corresponding rules. It has strong scalability, fast response speed in controlling front-end device operations, and high accuracy in the target item attribute adjustment result obtained based on the optimized item combination, thereby improving the operational level of item circulation and enabling timely activation of vehicles to achieve item circulation, thereby avoiding item loss due to untimely circulation.

[0075] Further references Figure 4 As an implementation of the above methods in the above figures, the present disclosure provides some embodiments of an article circulation device. These device embodiments are similar to Figure 2 Corresponding to the above method embodiments, the apparatus can be specifically applied to various terminal devices.

[0076] like Figure 4As shown, in some embodiments, an item circulation device 400 includes: a detection unit 401, an acquisition unit 402, a first generation unit 403, a second generation unit 404, a third generation unit 405, a receiving unit 406, and a control unit 407. The detection unit 401 is configured to detect the inventory of a target warehouse and the characteristics of the items stored in the target warehouse to obtain detection information. The acquisition unit 402 is configured to obtain a set of item information sets in response to the detection information meeting a predetermined condition. The set of item information sets includes a first number of item information sets, each including an item type feature set, an item quantity, and a rule set, wherein the rules in the rule set are rules that constrain the item circulation combination method. The first generation unit 403 is configured to generate a set of first candidate item information sets and a set of second candidate item information sets based on the set of item information sets. The second generation unit 404 is configured to generate a first optimization information set and a second optimization information set based on the set of first candidate item information sets and the set of second candidate item information sets, wherein the first optimization information in the first optimization information set is optimization information for the item combination method, and the second optimization information in the second optimization information set is optimization information for the item combination method. The third generation unit 405 is configured to generate a target item attribute adjustment result based on the first optimization information set and the second optimization information set, wherein the target item attribute adjustment result is the item attribute adjustment result obtained after the item combination is optimized. The receiving unit 406 is configured to receive item attribute adjustment initiation information input by the user. The control unit 407 is configured to control the vehicle corresponding to the item attribute adjustment initiation information to complete operations related to item flow scheduling based on the target item attribute adjustment result and the item attribute adjustment initiation information.

[0077] It is understood that the units described in the device 400 are similar to those in the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 400 and the units included therein, and will not be repeated here.

[0078] Reference below Figure 5 , which shows a schematic structural diagram of a computer system 500 of a terminal device suitable for implementing an embodiment of the present disclosure. Figure 5 The terminal device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0079] like Figure 5As shown, computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 506 into a random access memory (RAM) 503. Various programs and data required for the operation of system 500 are also stored in RAM 503. CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0080] The following components are connected to the I / O interface 505: a storage unit 506 including a hard disk, etc.; and a communication unit 507 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication unit 507 performs communication processing via a network such as the Internet. A drive 508 is also connected to the I / O interface 505 as needed. A removable medium 509, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed in the drive 508 as needed, so that a computer program read therefrom can be installed into the storage unit 506 as needed.

[0081] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 507, and / or installed from the removable medium 509. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the method of the present disclosure are executed. It should be noted that the computer-readable medium described in the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0082] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

Claims

1. A method for transferring goods, comprising: detecting the inventory of a target warehouse and characteristics of items stored in the target warehouse to obtain detection information; In response to the detection information meeting a predetermined condition, obtaining a set of item information sets of the target warehouse, wherein the set of item information sets includes a first number of item information sets, each of which includes an item type feature set, an item quantity, and a rule set, wherein the rules in the rule set are rules constraining a combination of item flow patterns; Based on the item information set, generating a set of first candidate item information set sets and a set of second candidate item information set sets; generating a first optimization information set and a second optimization information set based on the set of the first candidate item information sets and the set of the second candidate item information sets, wherein the first optimization information in the first optimization information set is optimization information of an item combination, and the second optimization information in the second optimization information set is optimization information of an item combination; generating a target item attribute adjustment result based on the first optimization information set and the second optimization information set, wherein the target item attribute adjustment result is an item attribute adjustment result obtained after the item is optimized; Receive item attribute adjustment startup information input by the user; According to the target item attribute adjustment result and the item attribute adjustment start information, the vehicle corresponding to the item attribute adjustment start information is controlled to complete item flow scheduling related operations.

2. The method according to claim 1, wherein The first optimization information in the first optimization information set is generated based on item type features, and the second optimization information in the second optimization information set is not generated based on item type features.

3. The method according to claim 2, wherein: The step of generating a set of first candidate item information sets and a set of second candidate item information sets based on the set of item information sets includes: For each rule in the rule set, in response to the rule being a first type rule, generating an initial first candidate item information set corresponding to the rule based on the item information set to obtain an initial first candidate item information set, wherein the initial first candidate item information set includes an initial first candidate item type feature set, a quantity of initial first candidate items, and a rule corresponding to the initial first candidate items; For the initial first candidate item information set set, a set of first candidate item information set sets is generated according to the initial first candidate item correspondence rule, wherein the first candidate item information sets in the first candidate item information set set include a first candidate item type feature set, a first candidate item quantity, and a first candidate item correspondence rule, and the first candidate item correspondence rule in each first candidate item information set in the first candidate item information set set is the same.

4. The method according to claim 3, wherein: The step of generating a set of first candidate item information sets and a set of second candidate item information sets based on the set of item information sets further includes: For each rule in the rule set, in response to the rule being a second type rule, generating an initial second candidate item information set corresponding to the rule based on the item information set to obtain an initial second candidate item information set, wherein the initial second candidate item information set includes an initial second candidate item type feature set, an initial second candidate item quantity, and a rule corresponding to the initial second candidate item; For the initial second candidate item information set, a set of second candidate item information set sets is generated based on the initial second candidate item correspondence rule, wherein the second candidate item information sets in the second candidate item information set include a second candidate item type feature set, a second candidate item quantity, and a second candidate item correspondence rule, and the second candidate item correspondence rule in each second candidate item information set in the second candidate item information set is the same.

5. The method according to claim 4, wherein The generating of a first optimized information set and a second optimized information set based on the set of the first candidate item information sets and the set of the second candidate item information sets includes: For each first candidate item information set in the set of first candidate item information sets, generating first optimization information of the first candidate item information set according to a first candidate item corresponding rule corresponding to the first candidate item information set, to obtain the first optimization information set; For each second candidate item information set in the set of second candidate item information set sets, second optimization information of the second candidate item information set set is generated according to the second candidate item corresponding rule corresponding to the second candidate item information set set to obtain the second optimization information set.

6. The method according to claim 5, wherein: Generating a target optimization result based on the first optimization information set and the second optimization information set includes: Determining a target optimization function and a target optimization rule set according to the first optimization information set and the second optimization information set; Determine an item quantity constraint rule and a hit rule, wherein the item quantity constraint rule is a rule for controlling the item quantity, and the hit rule is a rule for controlling the number of item turnovers; Optimization processing is performed according to the target optimization function, the target optimization rule set, the quantity constraint rule and the hit rule to generate the target optimization result.

7. The method according to claim 6, wherein: The step of generating first optimization information of the first candidate item information set according to the first candidate item corresponding rule corresponding to the first candidate item information set includes: determining a first constraint rule set according to a first candidate item corresponding rule corresponding to the first candidate item information set; determining a first decision variable set and a first objective function based on the first candidate item information set, wherein a first decision variable in the first decision variable set is a combination of item type features in the first candidate item information set; A set of the first constraint rule set, the first decision variable set, and the first objective function is determined as the first optimization information.

8. The method according to claim 7, wherein: The step of generating second optimization information of the second candidate item information set according to the second candidate item corresponding rule corresponding to the second candidate item information set includes: determining a second constraint rule set according to a second candidate item corresponding rule corresponding to the second candidate item information set; determining a second decision variable set and a second objective function based on the first candidate item information set, wherein a second decision variable in the second decision variable set is the number of item type features included in the second candidate item information set; A set of the second constraint rule set, the second decision variable set, and the second objective function is determined as the second optimization information.

9. An article circulation device, comprising: a detection unit configured to detect the inventory of a target warehouse and characteristics of items stored in the target warehouse to obtain detection information; an acquiring unit configured to acquire, in response to the detection information satisfying a predetermined condition, a set of item information sets of the target warehouse, wherein the set of item information sets includes a first number of item information sets, each of the item information sets including an item type feature set, an item quantity, and a rule set, wherein the rules in the rule set are rules constraining a combination of item flow patterns; a first generating unit configured to generate a set of first candidate item information sets and a set of second candidate item information sets based on the item information set; a second generating unit configured to generate a first optimization information set and a second optimization information set based on the set of the first candidate item information sets and the set of the second candidate item information sets, wherein the first optimization information in the first optimization information set is optimization information of an item combination, and the second optimization information in the second optimization information set is optimization information of an item combination; a third generating unit configured to generate a target item attribute adjustment result based on the first optimization information set and the second optimization information set, wherein the target item attribute adjustment result is an item attribute adjustment result obtained after the item is optimized; a receiving unit configured to receive item attribute adjustment start information input by a user; The control unit is configured to control the vehicle corresponding to the item attribute adjustment start information to complete item flow scheduling related operations according to the target item attribute adjustment result and the item attribute adjustment start information.

10. A terminal device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

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