Order target allocation method and device, storage medium and program product
By introducing an order target allocation method in the home-decoration order processing system, using mixed integer linear planning and genetic algorithm models to automatically process orders, solving the problems of inefficiency and resource waste in traditional systems, and achieving efficient and accurate order allocation.
Patent Information
- Application Number
- CN202411686854.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-22
- Publication Date
- 2025-04-11
AI Technical Summary
Traditional home decoration order processing systems rely on manual decision-making and fixed rules, are inefficient and prone to errors and waste of resources. Especially when order volume increases, it becomes particularly important to optimize the processing process.
It provides an order target allocation method. By obtaining data information from the order management system and the preset system, input it into the order allocation model, combining the mixed integer linear programming sub-model and the genetic algorithm sub-model, the initial allocation result is generated, and the prediction results are adjusted to output the final order target allocation result.
It realizes automated order processing, improves order processing efficiency, reduces labor costs, and improves the accuracy and efficiency of order allocation.
Smart Images
Figure CN119809743A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present disclosure relate to the technical field of home improvement order processing, and in particular to an order target allocation method, device, storage medium, and program product. Background Art
[0002] In the home improvement industry, order processing is the core link of project execution. Orders are usually initiated by project managers on traditional order processing systems, and arrive at the order fulfillment center for processing and fulfillment of goods. The order processing process usually involves multiple steps, including order receipt, allocation, scheduling and resource management. Traditional order processing systems often rely on manual decision-making and fixed rules, which are inefficient and prone to errors and resource waste. As the number of orders increases, it becomes particularly important to optimize the processing process.
[0003] Therefore, there is an urgent need for an order target allocation method that can automatically process orders, improve order processing efficiency, and reduce labor costs. Summary of the invention
[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide an order target allocation method, device, storage medium and program product.
[0005] A first aspect of an embodiment of the present disclosure provides an order target allocation method, comprising:
[0006] Obtaining an order data table from an order management system; wherein the order data table includes first data information;
[0007] Acquire second data information corresponding to the order data table from a preset system; wherein the second data information is used to represent the flow data information of the commodity category in the order data table;
[0008] Inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result;
[0009] The order allocation prediction result is obtained, and the order target allocation result of the order data table is output according to the order allocation prediction result and the order initial allocation result.
[0010] In one example, the order allocation model includes a first sub-model and a second sub-model, the first sub-model is a mixed integer linear programming sub-model, and the second sub-model is a genetic algorithm sub-model;
[0011] The step of inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result includes:
[0012] Inputting the first data information and the second data information into the first sub-model to determine a first allocation result of the order;
[0013] Inputting the first order allocation result into the second sub-model to generate a plurality of second order allocation results;
[0014] The initial order allocation result is determined from among the plurality of second order allocation results based on the number of iterations of the genetic algorithm in the second sub-model.
[0015] In one example, the constraints of the mixed integer linear programming sub-model are determined by the user's desired delivery time, delivery cost, order processing time, and priority of the order data table.
[0016] In one example, the method includes:
[0017] Acquire real-time order scheduling information, adjust the order target allocation result of the order data table according to the real-time order scheduling information, and obtain a scheduling allocation result; wherein the scheduling allocation result is used to indicate the reallocation of orders according to current resources or transportation routes.
[0018] In one example, before inputting the first data information and the second data information into the order allocation model to obtain the initial order allocation result, the method further includes:
[0019] Preliminary data processing is performed on the first data information and the second data information to obtain processed first data information and processed second data information.
[0020] In one example, the order allocation prediction result is determined based on a trained order allocation prediction model, including:
[0021] Obtaining a historical order data table and obtaining historical data information of the historical order data table;
[0022] Training an initial order allocation prediction model based on the historical data information to obtain a trained order allocation prediction model;
[0023] The time period information to which the time information of the order data table belongs is input into the trained order allocation prediction model to obtain the order allocation prediction result.
[0024] In one example, the method further includes:
[0025] Track the progress information of the order target allocation results of the order data table. If any abnormality is found, a notification message will be sent to the user end.
[0026] A second aspect of an embodiment of the present disclosure provides an order target allocation device, the device comprising:
[0027] A first acquisition module is used to acquire an order data table from an order management system; wherein the order data table includes first data information;
[0028] A second acquisition module is used to acquire second data information corresponding to the order data table from a preset system; wherein the second data information is used to represent the flow data information of the commodity category in the order data table;
[0029] An input module, used for inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result;
[0030] The output module is used to obtain the order allocation prediction result, and output the order target allocation result of the order data table according to the order allocation prediction result and the order initial allocation result.
[0031] A third aspect of an embodiment of the present disclosure provides an electronic device, comprising: a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method of the first aspect above.
[0032] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect described above can be implemented.
[0033] A fifth aspect of an embodiment of the present disclosure provides a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.
[0034] The disclosed embodiment provides an order target allocation method, device, storage medium and program product, the method comprising: obtaining an order data table from an order management system; wherein the order data table includes first data information; obtaining second data information corresponding to the order data table from a preset system; wherein the second data information is used to characterize the flow data information of the commodity category in the order data table; inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result; obtaining an order allocation prediction result, and outputting an order target allocation result of the order data table according to the order allocation prediction result and the initial order allocation result. The adoption of this technical solution can automatically process orders, improve order processing efficiency, and reduce labor costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0037] Figure 1 It is a flowchart of an order target allocation method provided by an embodiment of the present disclosure;
[0038] Figure 2 It is a flowchart of an order target allocation method provided by an embodiment of the present disclosure;
[0039] Figure 3 is a structural schematic diagram of an order target allocation device provided by an embodiment of the present disclosure;
[0040] Figure 4 is a structural schematic diagram of an order target allocation device provided by an embodiment of the present disclosure;
[0041] Figure 5 It is a structural diagram of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0042] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.
[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.
[0044] Figure 1 1 is a flow chart of an order target allocation method provided by an embodiment of the present disclosure, and the method can be executed by an electronic device. The electronic device can be exemplarily understood as a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a smart TV, etc. Figure 1 As shown, the method provided in this embodiment includes the following steps:
[0045] S101. Obtain an order data table from an order management system; wherein the order data table includes first data information.
[0046] In one example, the order management system is used to store an order data table in real time, wherein the first data information in the order data table includes at least the following: order date, order quantity, commodity category, warehouse information, supplier information, order completion time, order delivery time, order delivery time, and the performance time of each node.
[0047] S102. Acquire second data information corresponding to the order data table from a preset system; wherein the second data information is used to represent the flow data information of the commodity category in the order data table.
[0048] In one example, the preset system may be an inventory system, a procurement system, and a warehousing system. The inventory system includes inventory information, specifically inventory information of commodity categories in the order data table. The procurement system includes procurement information. The warehousing system includes warehousing information, i.e., operation node information of each warehouse. The second data information is the flow data information of the commodity categories in the order data table, specifically, the flow data information of the commodity categories includes inventory information of the commodity categories, procurement information of the commodity categories, and warehousing information of the commodity categories.
[0049] In one example, the commodity category is determined according to the order data table, and then the flow data information of the commodity category is obtained from the inventory system, the procurement system and the warehousing system respectively according to the commodity category.
[0050] S103: Input the first data information and the second data information into an order allocation model to obtain an initial order allocation result.
[0051] In one example, the order allocation model is used to allocate orders corresponding to the order data table according to the first data information and the second data information. The initial order allocation result may include at least one of the following: the supplier of the order, the dispatcher of the order, and the operator of the order.
[0052] S104: Obtain order allocation prediction results, and output order target allocation results of the order data table according to the order allocation prediction results and the order initial allocation results.
[0053] In one example, the order allocation prediction result is the order allocation result predicted in advance. The order allocation prediction result is obtained based on the historical order data table. That is, the order allocation prediction result of this time is predicted based on the order allocation result of the historical order data table. The order allocation prediction result and the order initial allocation result are considered together to output the order target allocation result of the order data table. For example, the order allocation prediction result is that the supplier of the order is the supplier A of the order and the supplier B of the order, the delivery party of the order is the delivery party C of the order, and the operator of the order is the operator D of the order. The order initial allocation result is that the supplier of the order is the supplier A of the order and the supplier C of the order, the delivery party of the order is the delivery party C of the order, and the operator of the order is the operator A of the order. It can be seen from this that there are differences between the supplier of the order and the operator of the order, and adjustments can be made according to the current situation. For example, the current situation is that the supplier B of the order does not cooperate and the operator D of the order is not in office, then the order initial allocation result can be determined as the order target allocation result of the order data table.
[0054] The disclosed embodiment provides an order target allocation method, which includes: obtaining an order data table from an order management system; wherein the order data table includes first data information; obtaining second data information corresponding to the order data table from a preset system; wherein the second data information is used to characterize the flow data information of the commodity category in the order data table; inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result; obtaining an order allocation prediction result, and outputting an order target allocation result of the order data table according to the order allocation prediction result and the initial order allocation result. The adoption of this technical solution can automatically process orders, improve order processing efficiency, and reduce labor costs.
[0055] Figure 2 The flowchart of an order target allocation method provided by the embodiment of the present disclosure is shown. The embodiment of the present disclosure is optimized on the basis of the above embodiment, and the embodiment of the present disclosure can be combined with various optional solutions in one or more of the above embodiments.
[0056] like Figure 2 As shown, the order target allocation method may include the following steps:
[0057] S201. Obtain an order data table from an order management system; wherein the order data table includes first data information.
[0058] In an example, this step may refer to the content of step S101.
[0059] S202. Acquire second data information corresponding to the order data table from a preset system; wherein the second data information is used to represent the flow data information of the commodity category in the order data table.
[0060] In an example, this step may refer to the content of step S102.
[0061] S203: Perform preliminary data processing on the first data information and the second data information to obtain processed first data information and processed second data information.
[0062] In one example, the preliminary data processing includes missing value supplementation, deleting duplicate data, processing abnormal data and unifying the format. Among them, missing value supplementation can be achieved by using mean filling, mode filling or deleting data containing missing values. Processing abnormal data is to identify and correct unreasonable data. In this embodiment, the above data processing is performed on the first data information and the second data information to obtain the processed first data information and the processed second data information.
[0063] S204, the order allocation model includes a first sub-model and a second sub-model, the first sub-model is a mixed integer linear programming sub-model, and the second sub-model is a genetic algorithm sub-model; the first data information and the second data information are input into the first sub-model to determine the first order allocation result.
[0064] In one example, the order allocation model may be a neural network model or other models, for example, a linear regression model or a Bayesian regression model, which is not limited here.
[0065] In an example, the first data information may be processed first data information, and the second data information may be processed second data information.
[0066] In this embodiment, the order allocation model may include a first sub-model and a second sub-model. The first sub-model is a mixed integer linear programming sub-model. The constraints of the mixed integer linear programming sub-model are determined by the user's expected delivery time, delivery cost, order processing time and priority of the order data table.
[0067] In one example, the mixed integer linear programming submodel may include problem modeling, and the specific problem modeling may be to convert the order allocation problem into a mathematical model, the purpose of which is to complete the fulfillment time in the shortest time, minimize the total cost, or maximize resource utilization. The decision variable is to define a variable to indicate whether the order is allocated to a specific resource. The objective function may be to set a goal to minimize the sum of all transportation costs. The solution process may be to use a linear programming solver, specifically, the first data information and the second data information may be input into the mixed integer linear programming submodel, and then the mixed integer linear programming submodel may be solved by Guro i or CPLEX to obtain the first allocation result of the order.
[0068] S205: Input the first order allocation result into the second sub-model to generate multiple second order allocation results.
[0069] In one example, the second sub-model is a genetic algorithm sub-model. Specifically, the genetic algorithm sub-model generates a plurality of second order allocation results from a first order allocation result by an initial population generation method.
[0070] S206. Determine an initial order allocation result from a plurality of second order allocation results based on the number of iterations of the genetic algorithm in the second sub-model.
[0071] In one example, the number of iterations of the genetic algorithm can be preset. Specifically, the implementation of the genetic algorithm evaluates the pros and cons of the second allocation result of each order by defining a fitness function, comprehensively considering the cost and resource utilization, and generating new solutions by selecting excellent solutions for combination and mutation. This process simulates biological evolution to explore better solutions, and continuously evaluates, selects and mutates until the number of iterations is met, and then the final iteration result is determined as the initial order allocation result.
[0072] S207. Obtain order allocation prediction results, and output order target allocation results of the order data table according to the order allocation prediction results and the order initial allocation results; wherein the order allocation prediction results are determined according to the trained order allocation prediction model.
[0073] In one example, the order allocation prediction result is determined based on the trained order allocation prediction model, including:
[0074] Obtain the historical order data table and obtain the historical data information of the historical order data table;
[0075] An initial order allocation prediction model is trained based on historical data information to obtain a trained order allocation prediction model;
[0076] The time period information to which the time information in the order data table belongs is input into the trained order allocation prediction model to obtain the order allocation prediction result.
[0077] In one example, the historical data information of the historical order data table includes at least one of the following: historical order date, historical order volume, historical commodity category, historical warehouse information, historical supplier information, historical order completion time, historical order outbound time, historical order delivery time, and historical performance time of each node. The initial order allocation prediction model can be a decision tree or a time series model. Specifically, the time series model can be an autoregressive integrated moving average model, a seasonal autoregressive integrated moving average model, or an exponential smoothing model.
[0078] In this embodiment, the decision tree can be a tree-structured model used for decision analysis and prediction. It implements classification or regression by segmenting data layer by layer, each node represents a feature, each branch represents a certain value of a feature, and each leaf node represents the final prediction result. In this embodiment, the decision tree can be used to predict the order volume, customer demand, delivery time, etc. in the order allocation prediction results.
[0079] In an example, the time period information to which the time information of the order data table belongs may be the month of the order data table, the quarter of the order data table, or the fulfillment time of the order data table.
[0080] In one example, the initial order allocation prediction model is trained by historical data information to obtain a trained order allocation prediction model, and then the order allocation prediction result is obtained based on the trained order allocation prediction model. In this embodiment, the trained order allocation prediction model can also be evaluated. Specifically, part of the historical order data table can be used to evaluate the trained order allocation prediction model, and specific calculation indicators such as mean square error can be used to quantify the prediction effect of the trained order allocation prediction model.
[0081] S208. Obtain real-time order scheduling information, adjust the order target allocation result of the order data table according to the real-time order scheduling information, and obtain the scheduling allocation result; wherein the scheduling allocation result is used to indicate the reallocation of orders according to current resources or transportation routes.
[0082] In one example, the way to adjust the order target allocation result of the order data table according to the real-time order scheduling information can be to adjust resource allocation or optimize the transportation route, etc., and then obtain the scheduling allocation result, and then reallocate the order according to the scheduling allocation result. The advantage of this setting is that the order target allocation result can be dynamically adjusted according to the actual situation, so that the final scheduling allocation result is more in line with the actual needs.
[0083] In one example, the method further includes:
[0084] Track the progress information of the order target allocation results of the order data table. If any abnormality is found, a notification message will be sent to the user end.
[0085] In one example, the process information of the order target allocation result can be order status update, processing progress, abnormal situation and change reminder. The user end can be a user, supplier, operator, etc. The benefit of such a setting is to ensure the timeliness and accuracy of information transmission.
[0086] The disclosed embodiment provides an order target allocation method, which includes: an order allocation model includes a first sub-model and a second sub-model, the first sub-model is a mixed integer linear programming sub-model, and the second sub-model is a genetic algorithm sub-model; the first data information and the second data information are input into the first sub-model to determine the first order allocation result. The first order allocation result is input into the second sub-model to generate multiple second order allocation results. Based on the number of iterations of the genetic algorithm in the second sub-model, the initial order allocation result is determined from the multiple second order allocation results. The order allocation prediction result is obtained, and the order target allocation result of the order data table is output according to the order allocation prediction result and the initial order allocation result; wherein the order allocation prediction result is determined according to the trained order allocation prediction model. The order real-time scheduling information is obtained, and the order target allocation result of the order data table is adjusted according to the order real-time scheduling information to obtain the scheduling allocation result; wherein the scheduling allocation result is used to indicate the reallocation of orders according to current resources or transportation routes. The technical solution can intelligently adjust the order target allocation result, thereby improving the efficiency and accuracy of order processing and improving user experience.
[0087] Figure 3 1 is a schematic diagram of the structure of an order target allocation device provided by an embodiment of the present disclosure. The order target allocation device can be understood as the above electronic device or a part of the functional modules in the above electronic device. Figure 3 As shown, the order target allocation device 30 includes:
[0088] The first acquisition module 301 is used to acquire an order data table from an order management system; wherein the order data table includes first data information.
[0089] The second acquisition module 302 is used to acquire second data information corresponding to the order data table from a preset system; wherein the second data information is used to represent the flow data information of the commodity category in the order data table.
[0090] The input module 303 is used to input the first data information and the second data information into the order allocation model to obtain an initial order allocation result.
[0091] The output module 304 is used to obtain the order allocation prediction result, and output the order target allocation result of the order data table according to the order allocation prediction result and the order initial allocation result.
[0092] The device provided in this embodiment can execute the method of any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.
[0093] Figure 4 1 is a schematic diagram of the structure of an order target allocation device provided by an embodiment of the present disclosure. The order target allocation device can be understood as the above electronic device or a part of the functional modules in the above electronic device. Figure 4 As shown, the order target allocation device 40 includes:
[0094] The first acquisition module 401 is used to acquire an order data table from an order management system; wherein the order data table includes first data information.
[0095] The second acquisition module 402 is used to acquire second data information corresponding to the order data table from a preset system; wherein the second data information is used to represent the flow data information of the commodity category in the order data table.
[0096] The input module 403 is used to input the first data information and the second data information into the order allocation model to obtain an initial order allocation result.
[0097] The output module 404 is used to obtain the order allocation prediction result, and output the order target allocation result of the order data table according to the order allocation prediction result and the order initial allocation result.
[0098] In one example, the order allocation model includes a first sub-model and a second sub-model, the first sub-model is a mixed integer linear programming sub-model, and the second sub-model is a genetic algorithm sub-model;
[0099] The input module 403 includes:
[0100] A first determination submodule, used for inputting the first data information and the second data information into the first submodel to determine a first allocation result of the order;
[0101] A generating submodule, used for inputting the first order allocation result into the second submodel to generate a plurality of second order allocation results;
[0102] The second determination submodule is used to determine the initial order allocation result from multiple order second allocation results based on the number of iterations of the genetic algorithm in the second submodel.
[0103] In one example, the constraints of the mixed integer linear programming sub-model are determined by the user's desired delivery time, delivery cost, order processing time, and priority of the order data table.
[0104] In one example, the apparatus 40 includes:
[0105] The third acquisition module 405 is used to obtain real-time order scheduling information, adjust the order target allocation result of the order data table according to the real-time order scheduling information, and obtain the scheduling allocation result; wherein the scheduling allocation result is used to indicate the reallocation of orders according to current resources or transportation routes.
[0106] In one example, before the input module, the apparatus 40 further includes:
[0107] The processing module 406 is used to perform preliminary data processing on the first data information and the second data information to obtain processed first data information and processed second data information.
[0108] In one example, the order allocation prediction result is determined based on the trained order allocation prediction model, including:
[0109] Obtain the historical order data table and obtain the historical data information of the historical order data table;
[0110] An initial order allocation prediction model is trained based on historical data information to obtain a trained order allocation prediction model;
[0111] The time period information to which the time information in the order data table belongs is input into the trained order allocation prediction model to obtain the order allocation prediction result.
[0112] In one example, the apparatus 40 further includes:
[0113] The sending module 407 is used to track the progress information of the order target allocation result of the order data table, and if an abnormality is found, send a notification message to the user end.
[0114] The device provided in this embodiment can execute the method of any of the above embodiments, and its execution method and beneficial effects are similar, which will not be repeated here.
[0115] An embodiment of the present disclosure further provides an electronic device, which includes: a memory, in which a computer program is stored; and a processor, which is used to execute the computer program. When the computer program is executed by the processor, the method of any of the above embodiments can be implemented.
[0116] For example, Figure 5 Schematic diagram of the structure of an electronic device in the embodiment of the present disclosure. Figure 5, which shows a schematic diagram of the structure of an electronic device 1000 suitable for implementing the embodiment of the present disclosure. The electronic device 1000 in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.
[0117] like Figure 5 As shown, the electronic device 1000 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.
[0118] Typically, the following devices may be connected to the I / O interface 1005: an input device 1006 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1008 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device 1000 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 1000 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.
[0119] 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 non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 1009, or installed from a storage device 1008, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.
[0120] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may 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 with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.
[0121] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.
[0122] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.
[0123] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains an order data table from an order management system; wherein the order data table includes first data information; obtains second data information corresponding to the order data table from a preset system; wherein the second data information is used to characterize the flow data information of the commodity category in the order data table; inputs the first data information and the second data information into an order allocation model to obtain an initial order allocation result; obtains an order allocation prediction result, and outputs an order target allocation result of the order data table based on the order allocation prediction result and the initial order allocation result.
[0124] Specifically, a computer program product includes a computer program, which is implemented when executed by a processor: obtaining an order data table from an order management system; wherein the order data table includes first data information; obtaining second data information corresponding to the order data table from a preset system; wherein the second data information is used to characterize the flow data information of the commodity category in the order data table; inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result; obtaining an order allocation prediction result, and outputting an order target allocation result of the order data table according to the order allocation prediction result and the initial order allocation result. The computer program code for performing the operation of the present disclosure can be written in one or more programming languages or a combination thereof, and the above-mentioned programming languages include but are not limited to object-oriented programming languages—such as Java, Small tale, C++, and also conventional procedural programming languages—such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can 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 can be connected to an external computer (e.g., through the Internet using an Internet service provider).
[0125] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0126] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.
[0127] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.
[0128] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0129] The embodiments of the present disclosure also provide a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method of any of the above embodiments can be implemented. The execution method and beneficial effects are similar and will not be repeated here.
[0130] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0131] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for allocating order targets, characterized in that: include: Obtaining an order data table from an order management system; wherein the order data table includes first data information; Acquire second data information corresponding to the order data table from a preset system; wherein the second data information is used to represent the flow data information of the commodity category in the order data table; Inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result; The order allocation prediction result is obtained, and the order target allocation result of the order data table is output according to the order allocation prediction result and the order initial allocation result.
2. The method according to claim 1, characterized in that: The order allocation model includes a first sub-model and a second sub-model, the first sub-model is a mixed integer linear programming sub-model, and the second sub-model is a genetic algorithm sub-model; The step of inputting the first data information and the second data information into an order allocation model to obtain an initial order allocation result includes: Inputting the first data information and the second data information into the first sub-model to determine a first allocation result of the order; Inputting the first order allocation result into the second sub-model to generate a plurality of second order allocation results; The initial order allocation result is determined from among the plurality of second order allocation results based on the number of iterations of the genetic algorithm in the second sub-model.
3. The method according to claim 2, characterized in that The constraints of the mixed integer linear programming sub-model are determined by the user's expected delivery time, delivery cost, order processing time and the priority of the order data table.
4. The method according to claim 1, characterized in that: The method comprises: Acquire real-time order scheduling information, adjust the order target allocation result of the order data table according to the real-time order scheduling information, and obtain a scheduling allocation result; wherein the scheduling allocation result is used to indicate the reallocation of orders according to current resources or transportation routes.
5. The method according to claim 1, characterized in that Before inputting the first data information and the second data information into the order allocation model to obtain the initial order allocation result, the method further includes: Preliminary data processing is performed on the first data information and the second data information to obtain processed first data information and processed second data information.
6. The method according to claim 1, characterized in that The order allocation prediction result is determined based on the trained order allocation prediction model, including: Obtaining a historical order data table and obtaining historical data information of the historical order data table; Training an initial order allocation prediction model based on the historical data information to obtain a trained order allocation prediction model; The time period information to which the time information of the order data table belongs is input into the trained order allocation prediction model to obtain the order allocation prediction result.
7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Track the progress information of the order target allocation results of the order data table. If any abnormality is found, a notification message will be sent to the user end.
8. An electronic device, characterized in that: include: A processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.
10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.