Warehouse transporting order matching method and device and server

Abstract

The invention provides a shipping order matching method and device and a server, and relates to the technical field of logistics orders, and the method comprises the steps: obtaining shipping information and order information, the shipping information comprising warehouse capacity information, warehouse geographical distance information and warehouse inventory aging information; performing order intelligent matching processing on the warehouse capacity information, the warehouse geographical distance information, the warehouse inventory time efficiency information and the order information through a decision engine in a preset intelligent routing decision system, and determining an order matching strategy set; and performing cost analysis processing on each order matching strategy in the order matching strategy set by using a warehouse lease step calculation function in a preset cost optimization model, determining a cost coefficient corresponding to each order matching strategy, and determining the order matching strategy with the lowest cost coefficient as a target order matching strategy. According to the invention, the matching efficiency of the shipping order can be obviously improved, and the logistics cost of the order is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics orders, and in particular, to a method, device, and server for matching shipping warehouse orders. Background Art

[0002] With the development of e-commerce and the increase in the number of orders, in order to improve the shipping efficiency and reduce the logistics cost, merchants need to build more and more shipping warehouses to store the goods corresponding to the orders in the warehouses. However, in the face of a large number of orders, how to improve the matching efficiency between the shipping warehouses and the orders and reduce the logistics cost has become a huge challenge for the shipping warehouse system. At present, related technologies propose that traditional logistics systems mainly rely on manual allocation of warehouses, but the matching efficiency of this solution is low, and the inventory turnover rate is also low. In addition, relying on manual experience to match shipping warehouses and orders makes it difficult to comprehensively analyze the costs, so it is easy to result in high logistics costs. Summary of the Invention

[0003] In view of this, an object of the present invention is to provide a method, device, and server for matching shipping warehouse orders, which can significantly improve the matching efficiency of shipping warehouse orders and reduce the logistics cost of orders.

[0004] In a first aspect, an embodiment of the present invention provides a method for matching shipping warehouse orders, the method including: obtaining shipping warehouse information and order information, where the shipping warehouse information includes: warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information; performing order intelligent matching processing on the warehouse capacity information, warehouse geographical distance information, warehouse inventory timeliness information, and order information through a decision engine in a preset intelligent routing decision system to determine an order matching strategy set; using a warehouse rent ladder calculation function in a preset cost optimization model to perform cost analysis processing on each order matching strategy in the order matching strategy set to determine a cost coefficient corresponding to each order matching strategy, and determining the order matching strategy with the lowest cost coefficient as the target order matching strategy.

[0005] In an implementation manner, after the step of obtaining shipping warehouse information and order information, it includes: performing dynamic adjustment processing on the bundling relationship of the order information through a preset rule engine to determine the target bundling rule of the order information, and performing weight analysis processing on the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information through a multi-dimensional dynamic matching algorithm to determine the target weight coefficient of the shipping warehouse information.

[0006] In one implementation, the steps of dynamically adjusting the bundling relationship of order information through a preset rule engine to determine the target bundling rule of the order information include: through the preset rule engine, based on the order information, matching the product bundling rule corresponding to the order, and through intelligent analysis and processing of the product bundling rule corresponding to the order, determining the complex bundling logic of the order, where the product bundling rule includes: product combination, mandatory accessories, and embargo combination, and the complex bundling logic includes: multi-level bundling and conditional bundling; determining the product bundling rule and the complex bundling logic as the target bundling rule corresponding to the order.

[0007] In one implementation, the steps of performing weight analysis on warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information through a multi-dimensional dynamic matching algorithm to determine the target weight coefficient of the shipping warehouse information include: through the multi-dimensional dynamic matching algorithm, performing three-dimensional weight analysis on warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information to determine the initial weight coefficients corresponding to the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information; performing dynamic weight adjustment on the initial weight coefficients according to real-time market fluctuation data to determine the target weight coefficients corresponding to the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information.

[0008] In one implementation, the steps of performing order intelligent matching on warehouse capacity information, warehouse geographical distance information, warehouse inventory timeliness information, and order information through the decision engine in a preset intelligent routing decision system to determine the order matching strategy set include: obtaining the preferred warehouse information within the preset search radius through the map API interface in the preset intelligent routing decision system; through the decision engine, performing path planning according to the target bundling rule corresponding to the order, the preferred warehouse information, and the target weight coefficients corresponding to the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information of the preferred warehouse to determine the order matching strategy set.

[0009] In one implementation, after the step of determining the order matching strategy set, it includes: performing abnormal monitoring on the order information to determine the abnormal orders cancelled by the user side; intercepting the abnormal orders through a preset three-level interception mechanism and synchronizing the interception results to the preset intelligent routing decision system in real time for warehouse management optimization.

[0010] In one implementation, the steps of intercepting the abnormal orders through a preset three-level interception mechanism include: performing order interception on the unshipped orders, freezing the abnormal orders, performing order recall on the picked orders, sending an order recovery instruction to the picking end, and performing order interception on the shipped orders, sending a package interception instruction to the transportation end.

[0011] Second aspect, an embodiment of the present invention further provides a shipping order matching device, which includes: an information acquisition module for acquiring shipping information and order information, where the shipping information includes: warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information; an intelligent analysis module for performing order intelligent matching processing on the warehouse capacity information, warehouse geographical distance information, warehouse inventory timeliness information, and order information through a decision engine in a preset intelligent routing decision system to determine an order matching strategy set; a cost optimization module for performing cost analysis processing on each order matching strategy in the order matching strategy set by using a warehouse rent ladder calculation function in a preset cost optimization model, determining a cost coefficient corresponding to each order matching strategy, and determining the order matching strategy with the lowest cost coefficient as the target order matching strategy.

[0012] Third aspect, an embodiment of the present invention further provides a server, which includes a processor and a memory. The memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method provided in any one of the first aspect.

[0013] Fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer executable instructions. When the computer executable instructions are called and executed by a processor, the computer executable instructions cause the processor to implement the method provided in any one of the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects:

[0015] A shipping order matching method, device, and server provided by an embodiment of the present invention, after acquiring shipping information and order information, perform order intelligent matching processing on the warehouse capacity information, warehouse geographical distance information, warehouse inventory timeliness information, and order information through a decision engine in a preset intelligent routing decision system to determine an order matching strategy set. Finally, use the warehouse rent ladder calculation function in the preset cost optimization model to perform cost analysis processing on each order matching strategy in the order matching strategy set, determine a cost coefficient corresponding to each order matching strategy, and determine the order matching strategy with the lowest cost coefficient as the target order matching strategy. The embodiments of the present invention can dynamically adjust the order matching strategy by analyzing the warehouse weight and order bundling relationship and combining warehouse optimization, thereby significantly improving the matching efficiency of shipping orders and reducing the logistics cost of orders.

[0016] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims, and drawings.

[0017] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, provides a detailed description as follows. Brief Description of the Drawings

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 A flowchart of a method for matching shipping warehouse orders provided by an embodiment of the present invention;

[0020] Figure 2 A flowchart of another method for matching shipping warehouse orders provided by an embodiment of the present invention;

[0021] Figure 3 A structural diagram of a device for matching shipping warehouse orders provided by an embodiment of the present invention;

[0022] Figure 4 A structural diagram of a server provided by an embodiment of the present invention Detailed Embodiments

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0024] Currently, with the development of e-commerce and the increase in the number of orders, in order to improve the shipping efficiency and reduce the logistics cost, merchants need to build more and more shipping warehouses to store the goods corresponding to the orders in the shipping warehouses. However, in the face of a large number of orders, how to improve the matching efficiency between the shipping warehouses and the orders and reduce the logistics cost has become a huge challenge for the shipping warehouse system. Related technologies propose that traditional logistics systems mainly rely on manual allocation of warehouses, but the matching efficiency of this solution is low, and the inventory turnover rate is also low. In addition, relying on manual experience to match shipping warehouses and orders makes it difficult to comprehensively analyze the costs, so it is easy to result in high logistics costs. Based on this, the shipping warehouse order matching method, device, and server provided by the embodiments of the present invention can dynamically adjust and process the order matching strategy by analyzing the warehouse weights and order bundling relationships and combining warehouse optimization, thereby significantly improving the matching efficiency of shipping warehouse orders and reducing the logistics cost of orders.

[0025] See Figure 1 The flowchart of a method for matching shipping orders as shown, and this method mainly includes the following steps S102 to S106:

[0026] Step S102, obtain shipping information and order information. Among them, the shipping information includes: warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information. In one implementation, the initial weights of the shipping information can be set as: warehouse capacity weight 25%, warehouse geographical distance weight 40%, and warehouse inventory timeliness weight 35%.

[0027] Step S104, through the decision engine in the preset intelligent routing decision system, perform order intelligent matching processing on the warehouse capacity information, warehouse geographical distance information, warehouse inventory timeliness information, and order information to determine the order matching strategy set. In one implementation, after determining the order matching strategy set, it is also necessary to perform exception monitoring processing on the order information to determine the abnormal orders cancelled by the user side, and through the preset three-level interception mechanism, intercept the abnormal orders and synchronize the interception results to the preset intelligent routing decision system in real time for warehouse management optimization. Specifically, order interception processing can be performed on the unshipped orders to freeze the abnormal orders, order recall processing can be performed on the picked orders, and an order recovery instruction can be sent to the picking end. For the shipped orders, order interception processing can be performed, and a package interception instruction can be sent to the transportation end. Further, when performing exception monitoring, a monitoring service can be established to poll the API interface every 15 seconds, so as to trigger an internal work order within 3 seconds of the exception event for real-time three-level interception. In addition, abnormal work orders can be automatically generated and the abnormal work orders can be synchronized and notified to the customer service system.

[0028] Step S106, use the warehouse rent ladder calculation function in the preset cost optimization model to perform cost analysis processing on each order matching strategy in the order matching strategy set, determine the cost coefficient corresponding to each order matching strategy, and determine the order matching strategy with the lowest cost coefficient as the target order matching strategy. In one implementation, the warehouse rent ladder calculation function can be set to charge at the standard rate within 30 days of the inventory age, and the overdue part increases by 0.5% daily. In addition, based on the dynamic pricing strategy, the warehouse allocation cost can be adjusted according to the inventory status and distribution requirements.

[0029] The above-mentioned method for matching shipping orders provided by the embodiments of the present invention can dynamically adjust the order matching strategy by analyzing the warehouse weights and order bundling relationships and combining warehouse optimization, thereby significantly improving the matching efficiency of shipping orders and reducing the logistics cost of orders.

[0030] See Figure 2The specific process schematic diagram of a method for matching shipping orders is shown. An embodiment of the present invention also provides an implementation manner for matching shipping and orders, specifically as follows (1) to (3):

[0031] (1) Through a preset rule engine, perform dynamic adjustment processing on the bundling relationship of order information to determine the target bundling rule of the order information. Specifically, through the preset rule engine, based on the order information, match the product bundling rule corresponding to the order, and through intelligent analysis processing of the product bundling rule corresponding to the order, determine the complex bundling logic of the order. Finally, determine the product bundling rule and the complex bundling logic as the target bundling rule corresponding to the order. Among them, the product bundling rule includes: product combination, accessories must be paired, and embargo combination; the complex bundling logic includes: multi-level bundling and conditional bundling. In one implementation manner, through the setting of the product bundling rule, it can be avoided that under the existing warehouse matching logic for bundled products, the matching is only based on the size of the product and the remaining space in the warehouse, resulting in the bundled products being sent to different warehouses separately, or the bundled products in the same warehouse being distributed far apart, thereby reducing the consumption of redundant logistics resources.

[0032] (2) Through a multi-dimensional dynamic matching algorithm, perform weight analysis processing on the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information to determine the target weight coefficient of the shipping information. Specifically, through the multi-dimensional dynamic matching algorithm, perform three-dimensional weight analysis processing on the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information to determine the initial weight coefficients corresponding to the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information, and perform dynamic weight adjustment processing on the initial weight coefficients according to real-time market fluctuation data to determine the target weight coefficients corresponding to the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information. In one implementation manner, the multi-dimensional dynamic matching algorithm can perform matching through multiple dimensions to achieve more accurate results. By dynamically adjusting the weight coefficients of the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information through the multi-dimensional dynamic matching algorithm, the matching strategy can be optimized. In addition, the inventory status and warehouse capacity can be calculated in real time to ensure the optimal matching.

[0033] (3) Obtain the preferred warehouse information within the preset search radius through the map API interface in the preset intelligent routing decision system, and through the decision engine, perform path planning processing based on the target bundling rules corresponding to the order, the preferred warehouse information, and the target weight coefficients corresponding to the warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information of the preferred warehouse, to determine the order matching strategy set. In one implementation, the decision engine can adopt a multi-objective optimization algorithm to find the optimal balance among distribution cost, timeliness, and transportation cost according to the necessary logistics combination of the goods represented by the target bundling rules, the set of preferred warehouses represented by the preferred warehouse information, and the specific capacity, location, and inventory of the above-mentioned preferred warehouses. In addition, it can also be dynamically adjusted according to the requirements of the user side, such as urgent delivery and low-cost delivery.

[0034] In summary, the present invention can dynamically adjust and process the order matching strategy by analyzing the warehouse weights and order bundling relationships and combining warehouse optimization, thereby significantly improving the matching efficiency of shipping and warehousing orders and reducing the logistics cost of orders.

[0035] For the shipping and warehousing order matching method provided in the foregoing embodiments, the embodiments of the present invention provide a shipping and warehousing order matching device. Refer to Figure 3 the structural schematic diagram of a shipping and warehousing order matching device shown below. The device includes the following parts:

[0036] An information acquisition module 302, which acquires shipping and warehousing information and order information. Among them, the shipping and warehousing information includes: warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information;

[0037] An intelligent analysis module 304, through the decision engine in the preset intelligent routing decision system, performs order intelligent matching processing on the warehouse capacity information, warehouse geographical distance information, warehouse inventory timeliness information, and order information to determine the order matching strategy set;

[0038] A cost optimization module 306, using the warehouse rent ladder calculation function in the preset cost optimization model, performs cost analysis processing on each order matching strategy in the order matching strategy set to determine the cost coefficient corresponding to each order matching strategy, and determines the order matching strategy with the lowest cost coefficient as the target order matching strategy.

[0039] The above-mentioned shipping and warehousing order matching device provided by the embodiments of the present application can significantly improve the matching efficiency of shipping and warehousing orders and reduce the logistics cost of orders.

[0040] In one implementation, after the steps of obtaining the warehouse transportation information and the order information, the intelligent analysis module 304 is further configured to: through a preset rule engine, perform dynamic adjustment processing on the bundling relationship of the order information to determine the target bundling rule of the order information, and through a multi-dimensional dynamic matching algorithm, perform weight analysis processing on the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information to determine the target weight coefficient of the warehouse transportation information.

[0041] In one implementation, when performing the step of determining the target bundling rule of the order information through a preset rule engine to perform dynamic adjustment processing on the bundling relationship of the order information, the intelligent analysis module 304 is further configured to: through a preset rule engine, based on the order information, match the product bundling rule corresponding to the order, and through intelligent analysis processing on the product bundling rule corresponding to the order, determine the complex bundling logic of the order, where the product bundling rule includes: product combination, accessory must-match, and embargo combination, and the complex bundling logic includes: multi-level bundling and conditional bundling; determine the product bundling rule and the complex bundling logic as the target bundling rule corresponding to the order.

[0042] In one implementation, when performing the step of determining the target weight coefficient of the warehouse transportation information through a multi-dimensional dynamic matching algorithm to perform weight analysis processing on the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information, the intelligent analysis module 304 is further configured to: through a multi-dimensional dynamic matching algorithm, perform three-dimensional weight analysis processing on the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information to determine the initial weight coefficients corresponding to the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information; perform dynamic weight adjustment processing on the initial weight coefficients according to the real-time market fluctuation data to determine the target weight coefficients corresponding to the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information.

[0043] In one implementation, when performing the step of determining the order intelligent matching strategy set by performing order intelligent matching processing on the warehouse capacity information, the warehouse geographical distance information, the warehouse inventory timeliness information, and the order information through the decision engine in the preset intelligent routing decision system, the intelligent analysis module 304 is further configured to: obtain the preferred warehouse information within the preset search radius through the map API interface in the preset intelligent routing decision system; through the decision engine, perform path planning processing according to the target bundling rule corresponding to the order, the preferred warehouse information, and the target weight coefficients corresponding to the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information of the preferred warehouse to determine the order intelligent matching strategy set.

[0044] In one implementation, after performing the step of determining the order matching policy set, the above-mentioned cost optimization module 306 is further configured to: perform abnormal monitoring and processing on the order information to determine abnormal orders cancelled by the user side; intercept the abnormal orders through a preset three-level interception mechanism, and synchronize the interception results to a preset intelligent routing decision system in real time for optimizing warehouse management.

[0045] In one implementation, when performing the step of intercepting the abnormal orders through a preset three-level interception mechanism, the above-mentioned cost optimization module 306 is further configured to: perform order interception processing on the unshipped orders, freeze the abnormal orders, perform order recall processing on the picked orders, send an order recovery instruction to the picking end, and perform order interception processing on the shipped orders, send a package interception instruction to the transportation end.

[0046] The device provided by the embodiments of the present invention has the same implementation principle and the same technical effects as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding contents in the foregoing method embodiments.

[0047] The embodiments of the present invention provide a server. Specifically, the server includes a processor and a storage device; a computer program is stored on the storage device, and when the computer program is run by the processor, it executes the method according to any one of the above-mentioned implementations.

[0048] Figure 4 FIG. 13 is a schematic structural diagram of a server provided by an embodiment of the present invention. The server 100 includes: a processor 40, a memory 41, a bus 42, and a communication interface 43. The processor 40, the communication interface 43, and the memory 41 are connected through the bus 42; the processor 40 is configured to execute an executable module stored in the memory 41, such as a computer program.

[0049] Among them, the memory 41 may include a high-speed random access memory (RAM, Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 43 (which may be wired or wireless), a communication connection is established between the system network element and at least one other network element, and the Internet, a wide area network, a local area network, a metropolitan area network, etc. can be used.

[0050] The bus 42 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 4 only a bidirectional arrow is used in FIG. 13, but it does not mean that there is only one bus or one type of bus.

[0051] Among them, the memory 41 is used to store a program. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device defined by the flow process disclosed in any embodiment of the foregoing embodiments of the present invention can be applied to or implemented by the processor 40.

[0052] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor 40 or by instructions in the form of software. The above-mentioned processor 40 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and combines its hardware to complete the steps of the above method.

[0053] The computer program product of the readable storage medium provided by the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the foregoing method embodiments. For specific implementation, reference can be made to the foregoing method embodiments, which will not be elaborated here.

[0054] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0055] Finally, it should be noted that the above-mentioned embodiments are only specific implementation manners of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments, or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for matching warehouse transportation orders, characterized in that, The method includes: Obtain warehousing information and order information, where the warehousing information includes: warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information; Through the decision-making engine in the preset intelligent routing decision system, perform order intelligent matching processing on the warehouse capacity information, the warehouse geographical distance information, the warehouse inventory timeliness information, and the order information to determine an order matching strategy set; Utilize the warehouse rent ladder calculation function in the preset cost optimization model to perform cost analysis processing on each order matching strategy in the order matching strategy set, determine the cost coefficient corresponding to each order matching strategy, and determine the order matching strategy with the lowest cost coefficient as the target order matching strategy.

2. The method for matching shipping orders according to claim 1, wherein After the step of obtaining warehousing information and order information, it includes: Through a preset rule engine, perform dynamic adjustment processing on the bundling relationship of the order information to determine the target bundling rule of the order information, and through a multi-dimensional dynamic matching algorithm, perform weight analysis processing on the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information to determine the target weight coefficient of the warehousing information.

3. The method for matching warehousing orders according to claim 2, wherein, The step of through a preset rule engine, performing dynamic adjustment processing on the bundling relationship of the order information to determine the target bundling rule of the order information includes: Through a preset rule engine, based on the order information, match the product bundling rule corresponding to the order, and through intelligent analysis processing of the product bundling rule corresponding to the order, determine the complex bundling logic of the order, where the product bundling rule includes: product combination, accessory required, and embargo combination, and the complex bundling logic includes: multi-level bundling and conditional bundling; Determine the product bundling rule and the complex bundling logic as the target bundling rule corresponding to the order.

4. The method for matching warehousing orders according to claim 2, wherein The step of through a multi-dimensional dynamic matching algorithm, performing weight analysis processing on the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information to determine the target weight coefficient of the warehousing information includes: Through a multi-dimensional dynamic matching algorithm, perform three-dimensional weight analysis processing on the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information to determine the initial weight coefficients corresponding to the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information; Perform dynamic weight adjustment processing on the initial weight coefficients according to real-time market fluctuation data to determine the target weight coefficients corresponding to the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information.

5. The method for matching warehousing orders according to claim 1, wherein, The step of through the decision-making engine in the preset intelligent routing decision system, performing order intelligent matching processing on the warehouse capacity information, the warehouse geographical distance information, the warehouse inventory timeliness information, and the order information to determine an order matching strategy set includes: Obtain preferred warehouse information within a preset search radius through the map API interface in the preset intelligent routing decision system; Through the decision-making engine, path planning processing is performed according to the target bundling rule corresponding to the order, the preferred warehouse information, and the target weight coefficients corresponding to the warehouse capacity information, the warehouse geographical distance information, and the warehouse inventory timeliness information of the preferred warehouse, to determine the order matching strategy set.

6. The method for matching shipping orders according to claim 1, wherein, After the step of determining the order matching strategy set, it includes: Performing abnormal listening processing on the order information to determine abnormal orders cancelled by the user side; Intercepting the abnormal orders through a preset three-level interception mechanism, and synchronizing the interception results to a preset intelligent routing decision-making system in real time for warehouse management optimization.

7. The warehouse transportation order matching method according to claim 6, characterized in that, The step of intercepting the abnormal orders through a preset three-level interception mechanism includes: Performing order interception processing on unshipped orders, freezing the abnormal orders, performing order recall processing on picked orders, sending an order recovery instruction to the picking end, and performing order interception processing on shipped orders, sending a package interception instruction to the transportation end.

8. A warehousing order matching device, characterized in that, The device includes: An information acquisition module that acquires shipping warehouse information and order information, where the shipping warehouse information includes: warehouse capacity information, warehouse geographical distance information, and warehouse inventory timeliness information; An intelligent analysis module that performs order intelligent matching processing on the warehouse capacity information, the warehouse geographical distance information, the warehouse inventory timeliness information, and the order information through a decision-making engine in a preset intelligent routing decision-making system to determine an order matching strategy set; A cost optimization module that uses a warehouse rent ladder calculation function in a preset cost optimization model to perform cost analysis processing on each order matching strategy in the order matching strategy set, determines the cost coefficient corresponding to each order matching strategy, and determines the order matching strategy with the lowest cost coefficient as the target order matching strategy.

9. A server, characterized in that, It includes a processor and a memory, the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer executable instructions, and when the computer executable instructions are called and executed by the processor, the computer executable instructions cause the processor to implement the method according to any one of claims 1 to 7.

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