An order intelligent management method, system and device for multi-channel sales

Abstract

This invention relates to the field of order management technology, specifically disclosing an intelligent order management method, system, and device for multi-channel sales. The method collects and integrates order information from various sales channels within a set sales cycle through an order management system. It performs verification processing using algorithms to reduce errors, omissions, and other discrepancies in order information. Based on the analyzed delivery demand of various orders, it matches the primary transportation path for each type of order. Simultaneously, by comprehensively monitoring the transportation status of various orders, it can monitor the transportation process in real time and comprehensively, accurately understand the transportation management behavior of orders, and ultimately evaluate the sales management status of various executed orders by judging the sales data of the orders. This improves the efficiency of multi-channel order sales management and achieves highly efficient order management.

Description

Technical Field

[0001] This invention relates to the field of order management technology, specifically to an intelligent order management method, system, and device for multi-channel sales. Background Technology

[0002] With the continuous development of multi-channel sales, the volume and types of orders are also increasing. Traditional order management methods usually rely on manual processing and operation, which not only leads to low order management efficiency, but also easily causes errors and duplications in the management process, resulting in a rapid increase in order management costs. At the same time, without the support of an intelligent management system, it is difficult to accurately track and deliver orders, leading to significant delays and omissions. This fails to meet the needs of rapid changes and complexity in order sales, and ultimately cannot improve the efficiency of order management.

[0003] For example, the invention patent with announcement number CN111861641B is a multi-channel order integration management system and method based on the communications industry. The multi-channel order integration management system has a first sub-warehouse channel module and a second sub-warehouse channel module. This allows users to query orders within their own sub-warehouse channel modules, reducing interference with the main order center module, reducing unnecessary queries to the order center database, and reducing the pressure on the main order center module. Each sub-warehouse only needs to call the unified creation, parsing, and classification module of the main order center module to achieve centralized management of communication number resources from all multiple channels, integrating the order processing mechanisms scattered across various business systems.

[0004] For example, invention patent CN109934652B describes a mobile internet-based supplier order management cloud platform. It includes a cloud platform server, a master / sub-supplier system, and a buyer mobile client. The master supplier system includes a master supplier mobile client and a master supplier server; the sub-supplier system includes sub-supplier mobile clients and sub-supplier servers. The buyer mobile client, supplier client, and cloud platform server are connected via mobile internet communication. The cloud platform server includes supplier registration, buyer registration, order management, and financial settlement modules. The supplier server includes supplier procurement management, sales management, and inventory management modules. The supplier client includes a supplier client cloud platform interface and a server interface. The buyer mobile client includes a buyer mobile client cloud platform interface. This mobile internet-based multi-channel collaborative supply solution between master and sub-suppliers features high order execution efficiency.

[0005] Based on the above technical solutions, it was found that the application did not verify the order information within the channels when managing multi-channel orders, which could lead to errors and omissions in the order information collected from the channels, which would be detrimental to subsequent order management. At the same time, if the transportation management of orders is not considered during the order management process, the comprehensive management of multi-channel orders will be inaccurate, so that the order execution efficiency cannot be effectively improved. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method, system, and device for intelligent order management in multi-channel sales, which can effectively solve the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides an intelligent order management method for multi-channel sales, comprising: acquiring order information from each sales channel within a set order sales cycle, and verifying the order information to obtain the delivery demand of each type of order; matching the first transportation path for each type of order based on the delivery demand of each type of order, and analyzing the transportation status of each type of order in the order management system to obtain transportation efficiency indicators for each type of order; recording each type of order after transportation completion as an executed order, and determining the sales data of each type of executed order in the order management system, integrating them to obtain sales management anomaly factors for each type of executed order, thereby evaluating the sales management status of each type of executed order.

[0008] According to another aspect of this application, an intelligent order management system for multi-channel sales is provided, comprising: an order information verification and processing module, used to acquire order information from each sales channel within a set order sales cycle, and to verify the order information to obtain the delivery demand of various types of orders; an order transportation status analysis module, used to match the first transportation path of various types of orders based on the delivery demand of various types of orders, and to analyze the transportation status of various types of orders in the order management system to obtain transportation efficiency indicators for various types of orders; and an order sales management evaluation module, used to record various types of orders after transportation completion as various types of executed orders, and to determine the sales data of various types of executed orders in the order management system, integrate them to obtain sales management anomaly factors for various types of executed orders, thereby evaluating the sales management status of various types of executed orders.

[0009] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory; and computer program instructions stored in the memory, the computer program instructions, when executed by the processor, causing the intelligent order management method for multi-channel sales.

[0010] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0011] (1) This invention provides an intelligent order management method, system and equipment for multi-channel sales. First, the order management system verifies the collected order information to know the inventory and price of the order information. Then, based on the analysis of the delivery demand of various orders, the first transportation route of each type of order is matched. At the same time, the transportation status of various orders is comprehensively considered to accurately understand the transportation management behavior of the orders. Finally, by judging the sales data of the orders, the sales management status of various executed orders is evaluated to improve the sales management efficiency of multi-channel orders.

[0012] (2) This invention collects and integrates order information from various sales channels within the set order sales cycle through an order management system, and performs verification processing through an algorithm. The purpose is to reduce errors, omissions, and other issues in order information, and obtain the delivery demand of various orders. This not only provides accurate data for subsequent order transportation, but also enables orders to be accurately matched with corresponding transportation routes, thereby reducing the transportation management cost of orders.

[0013] (3) This invention comprehensively analyzes the first transportation path of various types of orders and the transportation status of various types of orders, and can monitor the transportation process of orders in real time and comprehensively. By judging the sales data of completed orders, it can promptly discover abnormalities in order management. By accurately evaluating the sales management status of orders, it can provide corresponding solutions and ultimately achieve efficient order management. Attached Figure Description

[0014] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the method steps of the present invention;

[0016] Figure 2 This is a schematic diagram of the system module connections of the present invention;

[0017] Figure 3 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Exemplary Applications

[0020] The first aspect of this invention provides an intelligent order management method for multi-channel sales, comprising: S1: acquiring order information from each sales channel within a set order sales cycle, and verifying the order information to obtain the delivery demand of each type of order; S2: matching the first transportation path for each type of order based on the delivery demand of each type of order, and analyzing the transportation status of each type of order in the order management system to obtain the transportation efficiency index of each type of order; S3: recording each type of order after transportation completion as an executed order, determining the sales data of each type of executed order in the order management system, integrating them to obtain the sales management anomaly factor of each type of executed order, thereby evaluating the sales management status of each type of executed order.

[0021] Reference Figure 1 As shown, the order management system collects and integrates order information from various sales channels within the set order sales cycle, and performs verification processing to obtain the delivery demand of various orders.

[0022] The specific verification process is as follows:

[0023] The inventory verification information corresponding to various orders is processed through an anomaly detection algorithm to obtain the inventory verification compliance value for each type of order.

[0024] In this embodiment, the process for detecting and processing the inventory verification compliance values ​​corresponding to the above-mentioned various types of orders is as follows:

[0025] The inventory adequacy factor for each type of order is obtained by comparing the demand and inventory levels. The inventory storage quantity for each type of order is multiplied by the influencing factor for each individual inventory storage location stored in the order management intelligent database to obtain the inventory storage evaluation factor. Using an anomaly detection algorithm, the straight-line distance between the center landmark of each inventory storage location for each type of order and the average delivery center point for each type of order is measured to obtain the delivery distance for each inventory storage location. Based on the obtained delivery boundary distance, the number of inventory storage locations with delivery distances exceeding the delivery boundary distance is filtered and removed, and the remaining number of inventory storage locations is recorded as the inventory location benefit factor for each type of order. Finally, the inventory validity compliance value for each type of order is derived by combining the inventory adequacy factor, inventory storage evaluation factor, and inventory location benefit factor.

[0026] It should be explained that the values ​​in the aforementioned order management intelligent library are obtained by fitting multiple statistical order management data. For example, the preset number of order management anomalies is obtained by acquiring multiple order management anomaly numbers for various types of orders through different management data. Order management personnel then determine whether the order management of various types of orders under each order management anomaly number exceeds the management standard. The average of the order management anomaly numbers corresponding to the order management of various types of orders that do not exceed the management standard is processed and used as the preset number of order management anomalies. The values ​​in the order management intelligent library are obtained in this way. The values ​​will be adjusted according to changes in order type, transportation vehicle scale, and order completion. This embodiment does not impose any special limitations on this.

[0027] Further explanation is needed: the aforementioned impact factor for a single inventory storage represents the degree of influence of the inventory storage quantity, with a value ranging from 0.2 to 0.36, and specified as 0.33 in this example; the average distribution center point for each type of order is obtained by collecting multiple distribution center points for each type of order through address information from the order management system, and averaging them to obtain the average distribution center point for each type of order, representing the delivery location of the order; the delivery boundary distance is obtained by statistically analyzing historical delivery data, representing the maximum delivery distance; the inventory validity value for each type of order is the inventory benefit value obtained by adding the inventory adequacy factor, inventory storage evaluation factor, and inventory location advantage factor for each type of order.

[0028] The price verification information corresponding to various orders is processed through a neural network model to obtain the price verification compliance value for each type of order.

[0029] In this embodiment, the specific processing procedure for the price validation values ​​corresponding to the above-mentioned various order types is as follows:

[0030] Multiply the unit price and quantity of each type of order to obtain the total payment amount for each type of order. Then subtract the amount of coupons and discounts available for each type of order to obtain the amount to be paid for each type of order. Finally, add the shipping fee for each type of order to obtain the final payment amount for each type of order. Compare the payment definition amount for each type of order extracted from the order management intelligent library with the final payment amount for each type of order to obtain the price verification value for each type of order.

[0031] It should be noted that the payment limit for each of the above-mentioned order types represents the maximum allowed value of the payment amount.

[0032] Combine the inventory validation values ​​and price validation values ​​for various order types, and import the following conditions:

[0033]

[0034] Obtain the delivery demand δ for various types of orders DX In another specific embodiment, the network structure and delivery demand influencing parameters, such as order inventory status and order delivery urgency, are adjusted through a deep learning model to comprehensively predict order delivery demand, thereby obtaining a numerical value about order delivery demand.

[0035] In this embodiment, the larger the inventory verification and price verification values, the more correct the inventory and price verification of the order is. First, the corresponding order will be classified as a transportable order. At the same time, the inventory verification and price verification values ​​will be quantitatively evaluated. That is, if the inventory verification and price verification values ​​are larger, it indicates that the delivery degree of the transportable order is higher, which further reflects that the delivery demand of the transportable order is within the standard range, providing comprehensive data basis for keeping the transportation status of subsequent orders within the standard range.

[0036] In the formula, f DX The inventory verification value corresponding to the DX type order is obtained by comprehensively analyzing demand, inventory, inventory storage quantity, and the central landmark of each inventory storage location.

[0037] m DX The price validation value for order type DX is obtained by comprehensively analyzing the order unit price, order quantity, coupon amount, discount amount, and shipping cost.

[0038] F1 is the weighting factor corresponding to the inventory verification value, M2 is the weighting factor corresponding to the price verification value, and DX is the number of each type of order, DX = 1, 2, 3, ..., d, where d is the total number of order types.

[0039] In this example embodiment, the weighting factors corresponding to the inventory verification value and the price verification value are both obtained based on historical order delivery demand data. The weighting factor corresponding to the inventory verification value represents the weight of the change in the inventory verification value, with a value range of 0.55 to 0.88, and is specified as 0.8 in this example. The weighting factor corresponding to the price verification value represents the weight of the change in the price verification value, with a value range of 0.22 to 0.7, and is specified as 0.5 in this example.

[0040] Specifically, the process of collecting and integrating order information from various sales channels within the set order sales cycle is as follows:

[0041] Based on the association rule mining algorithm in the order management system, order information from each sales channel within the set order sales period is collected and integrated to obtain the order types, inventory verification information, and price verification information for each type of order within the set order sales period.

[0042] It should be explained that the above-mentioned collection of order information from various sales channels within the set order sales cycle is achieved by directly obtaining the order information from each sales channel within the order sales cycle through the association rule mining algorithm in the order management system. Then, the order types are obtained through intelligent classification, and the order information is divided according to the functions of inventory and price. In this way, the corresponding inventory verification information and price verification information for each type of order are collected. The order sales cycle is the duration from the start time of order information collection to the end time of order information collection, and its value will be adjusted according to the order type, the operating efficiency of the order management system, and the number of sales channels.

[0043] Furthermore, the inventory verification information and price verification information corresponding to the various types of orders include demand, inventory quantity, inventory storage quantity, and the central landmark of each inventory storage location. The price verification information includes the order unit price, order quantity, coupon amount, discount amount, and shipping cost.

[0044] It should be explained that the demand quantity mentioned above is obtained through the Enterprise Resource Planning (ERP) system within the order management system, representing the order quantity. The inventory quantity is obtained through the warehouse management system within the order management system, representing the current storage quantity of the order. The number of inventory storage locations is obtained through the warehouse management system within the order management system, representing the quantity of inventory stored. The center point of each inventory storage location is obtained through the warehouse management system within the order management system, representing the center point of the inventory storage location. The order unit price, order quantity, coupon usage amount, discount amount, and shipping cost are all obtained through the sales order system within the order management system. The order unit price represents the price of each order, the order quantity represents the order quantity for each type of order, the coupon usage amount represents the amount of coupon that can be used at order settlement, the discount amount represents the amount that can be discounted in the order, and the shipping cost represents the fee charged for delivering the goods.

[0045] In one specific embodiment, the present invention collects and integrates order information from various sales channels within a set order sales cycle through an order management system, and performs verification processing through algorithms to reduce errors, omissions, and other issues in order information, thereby obtaining the delivery demand of various orders. This not only provides accurate data for subsequent order transportation but also enables orders to be accurately matched with corresponding transportation routes, thereby reducing the transportation management costs of orders.

[0046] Based on the delivery demand of various orders, the primary transportation route for each type of order is matched, and the transportation status of various orders in the order management system is analyzed to derive transportation efficiency indicators for each type of order.

[0047] Furthermore, the specific analysis process for the transportation efficiency indicators of the various types of orders is as follows:

[0048] By analyzing the transportation status of various orders in the order management system, the number of delivery vehicles for each type of order and the corresponding delivery area for each delivery vehicle can be obtained.

[0049] It should be noted that the number of delivery vehicles and the delivery area corresponding to each delivery vehicle are obtained through the delivery management system in the order management system.

[0050] By comparing the number of delivery vehicles for each type of order with the delivery vehicles that are suitable for the first transportation route, the delivery vehicle management factors for each type of order are obtained.

[0051] By using spatial interpolation, relevant data on delivery areas for each delivery vehicle of various orders are obtained, and delivery area management factors for various orders are evaluated.

[0052] In this embodiment, the specific evaluation process for the delivery area management factors of the above-mentioned various types of orders is as follows:

[0053] From the delivery area data corresponding to each delivery vehicle for each type of order, the delivery distance and delivery time corresponding to each delivery vehicle for each type of order are extracted. Simultaneously, the altitude of each delivery location corresponding to each delivery vehicle for each type of order is located and extracted. The delivery distance and delivery time corresponding to each delivery vehicle for each type of order are then compared with the delivery adaptation distance and delivery permit duration corresponding to the first transportation route, respectively, to analyze the distance impact value and the time impact value for each delivery vehicle for each type of order. The altitude of each delivery location corresponding to each delivery vehicle for each type of order is matched with the delivery difficulty level corresponding to each altitude range defined in the order management intelligent library, and the delivery difficulty impact value for each delivery vehicle for each type of order is obtained through spatial interpolation. Finally, by combining the distance impact value, time impact value, and delivery difficulty impact value for each delivery vehicle for each type of order, the delivery area management factor for each type of order is obtained.

[0054] It should be explained that the above-mentioned delivery distance is the actual driving distance of the delivery vehicle obtained through the vehicle monitoring system, representing the distance the delivery vehicle needs to travel from the origin to the destination; the delivery time is the actual delivery time of the delivery vehicle recorded by the delivery management system, representing the time required for the delivery vehicle to travel from departure to arrival at the destination; the altitude of the delivery location can be obtained from the geographic information system, representing the altitude of the location relative to sea level; the delivery adaptability distance and the delivery permit duration are values ​​predicted through the transportation route information of the first transportation route; the delivery adaptability distance represents the standard driving distance of the delivery vehicle, and the delivery permit duration represents the maximum delivery time of the delivery vehicle.

[0055] It should be explained that the matching and integration process for the delivery difficulty impact values ​​of each delivery vehicle for the above-mentioned types of orders is as follows:

[0056] The altitude of each delivery location corresponding to each delivery vehicle for each type of order is matched with the delivery difficulty level corresponding to each altitude range defined in the order management intelligent library to obtain the delivery difficulty level of each delivery location corresponding to each delivery vehicle for each type of order. Then, the delivery difficulty levels of the delivery locations are integrated by spatial interpolation to obtain the delivery difficulty impact value of each delivery vehicle for each type of order.

[0057] In this example embodiment, the delivery difficulty level is divided into three levels: low delivery difficulty level, corresponding to an altitude range of [0, 1000m]; medium delivery difficulty level, corresponding to an altitude range of (1000m, 8000m]; and all other altitudes belong to the high delivery difficulty level. That is, if the altitude of the delivery location is 900m, the corresponding delivery difficulty level is low delivery difficulty level.

[0058] It should be explained that the comprehensive process for the delivery area management factors of the above-mentioned order types is as follows:

[0059] The distance impact value, time impact value, and delivery difficulty impact value corresponding to each delivery vehicle for each type of order are summed to obtain the distance impact value, time impact value, and delivery difficulty impact value for each type of order. Then, the delivery area management factor for each type of order is obtained by adding them together.

[0060] The transportation efficiency index for each type of order is obtained by adding the vehicle management factor for each type of order to the delivery area management factor for each type of order.

[0061] Specifically, the matching process for the first transportation route of various orders is as follows:

[0062] The delivery demand level of each type of order is matched with the first transportation path corresponding to each delivery demand level interval defined in the order management intelligent library to obtain the first transportation path of each type of order.

[0063] In this embodiment, the matching process for the first transportation route of the above-mentioned types of orders is as follows:

[0064] Based on the delivery demand level of various orders and the delivery demand level intervals defined in the order management intelligent library, various orders are matched with the first transportation path of the corresponding delivery demand level interval. Based on the matching results, the first transportation path of various orders is obtained. In this example, the first transportation path includes different delivery vehicles, routes, delivery times and other delivery requirements to meet the delivery demand level of orders and optimize delivery efficiency.

[0065] All orders completed after transportation are recorded as various types of executed orders. The sales data of various types of executed orders in the order management system are determined and integrated to obtain the sales management anomaly factors of various types of executed orders, thereby evaluating the sales management status of various types of executed orders.

[0066] Furthermore, the sales management status of various executed orders is evaluated, and the specific evaluation method is as follows:

[0067] The sales management anomaly factors of various types of executed orders are compared with the sales management anomaly thresholds defined in the order management intelligence library. The sales management anomaly threshold represents the maximum value of the sales management anomaly factor. If the sales management anomaly factor of a certain type of executed order is higher than the sales management anomaly threshold, an early warning is issued for the sales management status of that type of executed order. If the sales management anomaly factor of a certain type of executed order is lower than or equal to the sales management anomaly threshold, feedback is provided for the sales management status of that type of executed order.

[0068] In this embodiment, the above-mentioned early warning notification for the sales management status of this type of executed order is implemented as follows:

[0069] Firstly, the sales management system already has pre-defined early warning rules. Simultaneously, the sales management system monitors the sales management status of orders in real time. When the sales management anomaly factor for a certain type of executed order exceeds the sales management anomaly threshold—that is, when sales revenue decreases by more than a certain percentage, sales volume falls below expectations, or customer complaint rates increase—it indicates that the early warning rule has been triggered. The sales management system will automatically trigger an early warning notification, which can be indicated by an alarm light, to remind order management personnel to make corresponding adjustments to the order management situation and improve order management efficiency.

[0070] In this embodiment, the sales management status feedback for this type of executed order is provided in the following specific process:

[0071] Firstly, the sales management system already has pre-defined early warning rules. At the same time, the sales management system monitors the sales management status of orders in real time. When the sales management anomaly factor of a certain type of executed order is lower than or equal to the sales management anomaly threshold, that is, when all aspects of the sales order are within a reasonable range, the sales management system only needs to provide feedback to the order management personnel through pop-ups, emails, SMS, etc., and the order management personnel do not need to make any adjustments.

[0072] Specifically, the integration yields sales management anomaly factors for various types of executed orders. The specific integration process is as follows:

[0073] In another embodiment, the sales management anomaly factors of the above-mentioned various types of executed orders are predicted by statistical methods, that is, by statistically analyzing multiple factors that affect the sales management of orders, such as after-sales processing and delivery address, and by analyzing the abnormal trends in various data, the degree of abnormality in the sales management of orders can be predicted.

[0074] From the sales data of various executed orders, abnormal sales order data are statistically analyzed and categorized into abnormal sales order delivery location numbers, abnormal sales order receipt time numbers, and sales order return / exchange numbers. At the same time, the number of abnormal order management data for each type of executed order is obtained through duplicate removal operations.

[0075] It should be explained that the above statistical method for abnormal sales order data is the sales order system in the order management system. The number of abnormal orders for each type of executed order is obtained by removing duplicates. The purpose is to remove duplicates of the same order among the number of abnormal sales order delivery location, the number of abnormal sales order receipt time, and the number of returned or exchanged sales orders. For example, if the abnormal behavior of a certain type of executed order includes abnormal delivery location, abnormal receipt time, and returns / exchanges, then the number of abnormal orders for that type of executed order is 1.

[0076] Based on the number of order management anomalies for various types of executed orders, and combined with the preset number of order management anomalies stored in the order management intelligent library, the sales management anomaly factors for various types of executed orders are analyzed and processed. The analysis formula is as follows:

[0077]

[0078] In the formula, α ZX The ZXth type of order execution order represents the sales management anomaly factor. In this embodiment, a larger order management anomaly number indicates more frequent issues with delivery location, delivery time, and returns / exchanges, which is detrimental to order sales management. In other words, a larger order sales management anomaly factor indicates a larger anomaly factor. Furthermore, by comprehensively analyzing the order management anomaly preset number, the connection between the order sales management anomaly factor and the order management anomaly number becomes tighter, making it easier to derive a positive proportional relationship between the order management anomaly number and the sales management anomaly factor. Therefore, to improve the efficiency of order sales management, it is necessary to monitor the order management anomaly number in real time so as to take timely countermeasures and ultimately reduce the order sales management anomaly factor.

[0079] G ZX This represents the number of order management exceptions for orders of type ZX, and this represents the number of orders with sales management exceptions.

[0080] ΔG represents the preset number of order management exceptions, which represents the maximum number of orders allowed for sales management exceptions.

[0081] g represents the correction factor corresponding to the number of order management anomalies. It is obtained by statistical analysis of historical order anomaly management data and represents the degree of correction for changes in the number of order management anomalies. The value ranges from 0.6 to 0.8, and the value in this example is specified as 0.65.

[0082] ZX represents the number of each type of execution order, ZX = 1, 2, 3, ..., q, where q represents the total number of execution order types.

[0083] In one specific embodiment, the present invention comprehensively analyzes the first transportation path of various types of orders and the transportation status of various types of orders, which can monitor the transportation process of orders in real time and comprehensively. By judging the sales data of completed orders, it can promptly detect abnormalities in order management. By accurately evaluating the sales management status of orders, corresponding solutions can be provided, ultimately achieving efficient order management.

[0084] Exemplary System

[0085] Reference Figure 2 As shown, the second aspect of the present invention provides an intelligent order management system for multi-channel sales, including: an order information verification and processing module, used to obtain order information of each sales channel within a set order sales cycle, and to verify the order information to obtain the delivery demand of various types of orders;

[0086] The order transportation status analysis module is used to match the first transportation route for various orders based on their delivery demand, analyze the transportation status of various orders in the order management system, and derive transportation efficiency indicators for various orders.

[0087] The order sales management evaluation module is used to record various orders that have been completed after transportation as various types of executed orders, determine the sales data of various types of executed orders in the order management system, integrate them to obtain the sales management anomaly factors of various types of executed orders, and thus evaluate the sales management status of various types of executed orders.

[0088] In one example, an order management intelligence library is also included, which stores the pre-set number of order management anomalies, the sales management anomaly threshold, and the first transportation path corresponding to each delivery demand interval.

[0089] Exemplary electronic devices

[0090] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be an electronic device integrated with a first imaging device, or a standalone device independent of the first imaging device, which can communicate with the first imaging device to receive acquired input signals from it.

[0091] Figure 3 A block diagram of an electronic device according to an embodiment of this application is shown.

[0092] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0093] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0094] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the moving object tracking methods of the various embodiments of this application described above and / or other desired functions. Various contents such as the current frame image, the previous frame image, and the result image of differential processing may also be stored in the computer-readable storage medium.

[0095] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0096] For example, when the electronic device integrates the first imaging device, the input device 13 can be the first imaging device, such as a camera, for capturing frames of images of a moving object. When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the acquired input signals from the first imaging device.

[0097] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0098] The output device 14 can output various information to the outside, including the determined deflection angle information. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0099] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device may include any other suitable components depending on the specific application.

[0100] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for intelligent order management for multi-channel sales, characterized in that, Includes the following steps: (1) Through the order management system, collect and integrate the order information of each sales channel within the set order sales period, and perform verification processing to obtain the delivery demand of various orders. Specifically, according to the association rule mining algorithm in the order management system, collect the order information of each sales channel within the set order sales period, and integrate it to obtain the order types, inventory verification information and price verification information of each type of order within the set order sales period. The inventory verification information corresponding to various orders is processed through an anomaly detection algorithm to obtain the inventory verification compliance value for each type of order; the price verification information corresponding to various orders is processed through a basic algorithm to obtain the price verification compliance value for each type of order. By comprehensively analyzing the inventory validation values ​​and price validation values ​​for various order types, the delivery demand level for each order type can be obtained. ,include: ； in, f DX The inventory verification value corresponding to the DX type order is obtained by comprehensively analyzing demand, inventory, inventory storage quantity, and the central landmark of each inventory storage location. m DX The price validation value corresponding to the DX-th type of order is obtained by comprehensively analyzing the order unit price, order quantity, coupon amount, discount amount, and shipping cost. F1 is the weighting factor corresponding to the inventory validation value, M2 is the weighting factor corresponding to the price validation value, and DX is the number of each type of order, DX=1,2,3,…,d, where d is the total number of order types. (2) Based on the delivery demand of various orders, the first transportation route of various orders is matched, and the transportation status of various orders in the order management system is analyzed to obtain the transportation efficiency indicators of various orders. (3) Record all orders completed after transportation as various types of executed orders, determine the sales data of various types of executed orders in the order management system, integrate them to obtain the sales management anomaly factors of various types of executed orders, and thereby evaluate the sales management status of various types of executed orders; the specific integration process is as follows: From the sales data of various executed orders, the number of sales order anomalies is statistically analyzed for each type of executed order. These anomalies are categorized into three types: abnormal delivery locations, abnormal delivery times, and abnormal returns / exchanges. Furthermore, duplicate removal is performed to obtain the order management anomaly count for each type of executed order. Based on these anomalies and the pre-set anomaly counts stored in the order management intelligent library, the sales management anomaly factors for each type of executed order are analyzed and processed. The analysis formula is as follows: ； In the formula, a ZX This represents the sales management anomaly factor for order type ZX. G ZX This represents the number of order management exceptions for the ZXth type of order. ZX represents the preset number of order management anomalies, g represents the correction factor corresponding to the number of order management anomalies, ZX represents the number of each type of executed order, ZX=1,2,3,…,q, and q represents the total number of types of executed orders.

2. The intelligent order management method for multi-channel sales according to claim 1, characterized in that: The inventory verification information and price verification information corresponding to the various types of orders include demand, inventory quantity, inventory storage quantity, and the central landmark of each inventory storage location. The price verification information includes the order unit price, order quantity, coupon amount, discount amount, and shipping cost.

3. The intelligent order management method for multi-channel sales according to claim 1, characterized in that: The matching process for the first transportation route of various orders mentioned in step (2) is as follows: The delivery demand level of each type of order is matched with the first transportation path corresponding to each delivery demand level interval defined in the order management intelligent library to obtain the first transportation path of each type of order.

4. The intelligent order management method for multi-channel sales according to claim 1, characterized in that: The specific analysis process for the transportation efficiency indicators of various orders mentioned in step (2) is as follows: By analyzing the transportation status of various orders in the order management system, the number of delivery vehicles for each type of order and the delivery area corresponding to each delivery vehicle can be obtained. The number of delivery vehicles for each type of order is compared with the delivery vehicles that are suitable for the first transportation route to obtain the delivery vehicle management factors for each type of order. By using spatial interpolation, relevant data on delivery areas for each delivery vehicle of various orders are obtained, and the delivery area management factors for various orders are evaluated. The transportation efficiency index for each type of order is obtained by adding the vehicle management factor for each type of order to the delivery area management factor for each type of order.

5. The intelligent order management method for multi-channel sales according to claim 1, characterized in that: The evaluation of the sales management status of various types of executed orders described in step (3) is as follows: The sales management anomaly factors of various types of executed orders are compared with the sales management anomaly thresholds defined in the order management intelligent library. If the sales management anomaly factor of a certain type of executed order is higher than the sales management anomaly threshold, an early warning is issued for the sales management status of that type of executed order. If the sales management anomaly factor of a certain type of executed order is lower than or equal to the sales management anomaly threshold, feedback is provided for the sales management status of that type of executed order.

Images