Order charging difference analysis method and device, equipment and storage medium

Abstract

The invention discloses an order charging difference analysis method, and the method comprises the steps: obtaining logistics order data, which is used for obtaining the basic information and distribution service information of logistics orders; key features of the order are extracted; constructing an order charging difference analysis model, and training the order charging difference analysis model by using the extracted key features and logistics order data; presetting an order charging difference threshold, inputting to-be-analyzed order data into the order charging difference analysis model, and outputting an order charging difference prediction value; and carrying out charging difference comparison, namely comparing the order charging difference prediction value with an order charging difference threshold value, and judging the difference of the orders. According to the invention, through the automatic analysis model, orders with relatively large fee distribution differences can be rapidly identified, and through prediction of the fee distribution differences, logistics companies can more accurately plan budget and reasonably allocate resources; high-cost orders or delivery routes are identified, so that optimization of the delivery routes and service modes is facilitated, and the overall operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a method, device, equipment and storage medium for analyzing difference in order dispatch fees. Background Art

[0002] The calculation and management of logistics order dispatch fees are key links in operating cost control and service quality assurance. With the continuous expansion of logistics business scale and the increase in business complexity, various differences often occur in order dispatch fees. For example, there may be large differences in dispatch fee standards for orders in different regions, with different weights and volumes, orders with different delivery time requirements, and orders of different service types (such as ordinary delivery, expedited delivery, door-to-door installation, etc.). If these differences cannot be effectively analyzed and managed, they may lead to problems such as inaccurate cost accounting of logistics companies, unreasonable allocation of distribution resources, and unfair income of distribution personnel, which in turn affects the company's operating efficiency and market competitiveness. At present, traditional logistics dispatch fee management relies more on manual experience and simple data records, lacks systematic, accurate and efficient difference analysis methods, and is difficult to meet the needs of refined management of modern logistics.

[0003] In order to effectively and systematically analyze and manage the differences in order dispatch fees, reasonably arrange distribution resources, and improve the refined management capabilities of logistics companies, it is necessary to invent an order dispatch fee difference analysis method, device, equipment and storage medium. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problems in the prior art that the differences in order dispatch fees cannot be effectively and systematically analyzed and managed, distribution resources cannot be reasonably arranged, and the ability of logistics companies to carry out refined management cannot be improved.

[0005] A first aspect of the present invention provides a method for analyzing order dispatch fee differences, the method comprising: Obtain logistics order data, which is used to obtain basic information of logistics orders and delivery service information; Extracting key features of the order based on the basic information of the order and the delivery service information of the order; Constructing an order dispatch fee difference analysis model, and training the order dispatch fee difference analysis model using the extracted key features and logistics order data; Preset an order dispatch fee difference threshold, input the order data to be analyzed into the order dispatch fee difference analysis model, and output the order dispatch fee difference prediction value; The dispatch fee difference comparison is to compare the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to determine the difference size of the order.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the obtaining of logistics order data is used to obtain basic information and delivery service information of the logistics order, including: A logistics database system connected to the logistics order; Obtaining basic information, delivery service information, dispatch fee information of the logistics order and historical order data of the logistics database through the logistics database system; The basic information of the logistics order, the delivery service information and the dispatch fee information of the logistics order are processed to remove invalid data and duplicate data.

[0007] Optionally, in a second implementation of the first aspect of the present invention, extracting key features of the order according to the basic information of the order and the delivery service information of the order includes: According to the basic information of the logistics order, the shipping place and the receiving place of the logistics order are regionally coded; Extracting regional features of the departure area and destination area of ​​the logistics order according to the regional codes of the shipping place and the receiving place of the logistics order; Extracting the cargo volume-to-weight ratio feature of the logistics order according to the weight and volume of the order; According to the delivery timeliness requirement and delivery service type of the logistics order, the delivery service level characteristics of the logistics order are extracted.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the step of constructing an order dispatch fee difference analysis model and training the order dispatch fee difference analysis model using the extracted key features and logistics order data includes: Constructing a decision tree model for analyzing the difference in order dispatch fees; Training the difference analysis decision tree model using the extracted key features and the historical order data of the logistics database; Taking the regional characteristics of the departure area and destination area of ​​the logistics order, the volume-to-weight ratio characteristics of the goods in the logistics order, and the delivery service level characteristics of the logistics order as input variables; The difference between the actual delivery fee amount of the logistics order and the standard delivery fee amount of the logistics order is used as the output variable of the difference analysis decision tree model.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the difference analysis model is trained using the extracted key features and the historical order data of the logistics database, including: During the training process, the information gain of each key feature for dividing the difference in dispatch fees is calculated; The key feature with the largest information gain is selected as the basis for dividing the current node, and the decision tree model is recursively constructed.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, the preset order dispatch fee difference threshold, inputting the order data to be analyzed into the order dispatch fee difference analysis model, and outputting the order dispatch fee difference prediction value include: Presetting a difference threshold of the logistics order dispatch fee; Inputting the logistics order data to be analyzed into the logistics order dispatch fee difference analysis model; Outputting the predicted difference value of the logistics order dispatch fee to be analyzed; The difference between the actual delivery fee amount of the logistics order and the standard delivery fee amount of the logistics order is determined based on the difference prediction value of the delivery fee of the logistics order.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the dispatch fee difference comparison, comparing the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to determine the difference size of the order, includes: Comparing the predicted difference value of the logistics order dispatch fee to be analyzed with the difference threshold value of the order dispatch fee; If the predicted difference value of the logistics order dispatch fee to be analyzed is greater than or equal to the difference threshold of the order dispatch fee, it is determined that the difference of the order is large, and the logistics order is marked as an abnormal order; If the predicted difference value of the logistics order dispatch fee to be analyzed is less than the difference threshold of the order dispatch fee, it is determined that the difference of the order is small, and the logistics order is marked as a normal order.

[0012] A second aspect of the present invention provides an order dispatch fee difference analysis device, comprising: The module for obtaining logistics order data is used to obtain basic information and delivery service information of logistics orders; A key feature extraction module, used to extract key features of the order based on the basic information of the order and the delivery service information of the order; Constructing an order dispatch fee difference analysis model module, which is used to train the order dispatch fee difference analysis model using the extracted key features and logistics order data; A dispatch fee difference analysis module is used to preset an order dispatch fee difference threshold, input the order data to be analyzed into the order dispatch fee difference analysis model, and output the order dispatch fee difference prediction value; The judgment and comparison module is used to compare the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to judge the difference size of the order.

[0013] A third aspect of the present invention provides a computer device, the computer device comprising a memory and at least one processor, the memory storing instructions; The at least one processor calls the instructions in the memory to enable the computer device to execute the order dispatch fee difference analysis method as described above.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is loaded by a processor to execute the order dispatch fee difference analysis method as described above.

[0015] The order delivery fee difference analysis method of the present invention can quickly identify orders with large delivery fee differences through an automated analysis model, helping logistics companies to respond and adjust in a timely manner; by predicting delivery fee differences, logistics companies can plan budgets more accurately and allocate resources reasonably; identifying high-cost orders or delivery routes helps to optimize delivery paths and service methods, thereby reducing overall operating costs; and being able to clearly show customers the composition of delivery fees and the reasons for the differences, thereby increasing the transparency of logistics services and enhancing customer trust. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for analyzing order dispatch fee differences provided by an embodiment of the present invention; Figure 2 A flow chart of obtaining logistics order data in the order dispatch fee difference analysis method provided in an embodiment of the present invention; Figure 3 A flowchart of extracting key processes in the order dispatch fee difference analysis method provided in an embodiment of the present invention; Figure 4 A flow chart of constructing an order dispatch fee difference analysis model in the order dispatch fee difference analysis method provided in an embodiment of the present invention; Figure 5 A flowchart of training a model in the order dispatch fee difference analysis method provided in an embodiment of the present invention; Figure 6 A flow chart of order dispatch fee difference prediction in the order dispatch fee difference analysis method provided in an embodiment of the present invention; Figure 7 A flow chart of the order dispatch fee difference ratio in the order dispatch fee difference analysis method provided in an embodiment of the present invention; Figure 8 A schematic diagram of the structure of an order dispatch fee difference analysis device provided in an embodiment of the present invention; Fig. 9 Another structural schematic diagram of the order dispatch fee difference analysis device provided by an embodiment of the present invention; Fig.10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention provides an order dispatch fee difference analysis method, the method comprising: obtaining logistics order data for obtaining basic information and delivery service information of the logistics order; extracting key features of the order according to the basic information of the order and the delivery service information of the order; constructing an order dispatch fee difference analysis model, and training the order dispatch fee difference analysis model using the extracted key features and logistics order data; presetting an order dispatch fee difference threshold, inputting the order data to be analyzed into the order dispatch fee difference analysis model, and outputting a predicted value of the order dispatch fee difference; and comparing the dispatch fee difference, comparing the predicted value of the order dispatch fee difference with the order dispatch fee difference threshold, and judging the size of the difference of the order.

[0018] The order delivery fee difference analysis method of the present invention can quickly identify orders with large delivery fee differences through an automated analysis model, helping logistics companies to respond and adjust in a timely manner; by predicting delivery fee differences, logistics companies can plan budgets more accurately and allocate resources reasonably; identifying high-cost orders or delivery routes helps to optimize delivery paths and service methods, thereby reducing overall operating costs; and being able to clearly show customers the composition of delivery fees and the reasons for the differences, thereby increasing the transparency of logistics services and enhancing customer trust.

[0019] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the present invention provides a method for analyzing order dispatch fee differences, the method comprising: 101. Obtain logistics order data, which is used to obtain basic information of logistics orders and delivery service information; 102. Extract key features of the order based on the basic information of the order and the delivery service information of the order; 103. Construct an order dispatch fee difference analysis model, and train the order dispatch fee difference analysis model using the extracted key features and logistics order data; 104. Preset an order dispatch fee difference threshold, input the order data to be analyzed into the order dispatch fee difference analysis model, and output the order dispatch fee difference prediction value; 105. Comparing the difference in dispatch fees: comparing the predicted value of the difference in dispatch fees for the order with the threshold value of the difference in dispatch fees for the order to determine the difference in the order.

[0021] The order delivery fee difference analysis method of the present invention can quickly identify orders with large delivery fee differences through an automated analysis model, helping logistics companies to respond and adjust in a timely manner; by predicting delivery fee differences, logistics companies can plan budgets more accurately and allocate resources reasonably; identifying high-cost orders or delivery routes helps to optimize delivery paths and service methods, thereby reducing overall operating costs; and being able to clearly show customers the composition of delivery fees and the reasons for the differences, thereby increasing the transparency of logistics services and enhancing customer trust.

[0022] See also Figure 2 In the second embodiment of the method for analyzing the difference in order delivery fees in the embodiment of the present invention, based on the above embodiment, the method of obtaining logistics order data is used to obtain basic information and delivery service information of the logistics order, including: 201. Connecting to the logistics database system of the logistics order; 202. Obtaining basic information, delivery service information, delivery fee information of the logistics order and historical order data of the logistics database through the logistics database system; Based on rich and accurate historical order data, management can make more intelligent and data-driven business decisions. Through in-depth analysis of historical data, long-term trends and seasonal patterns can be identified to help plan future resource allocation and service adjustments.

[0023] 203. Process the basic information of the logistics order, the distribution service information and the delivery fee information of the logistics order to remove invalid data and duplicate data.

[0024] In some embodiments, data related to order delivery fees are collected from the logistics database system, including basic order information, delivery service information, delivery fee information, and delivery personnel information.

[0025] In some embodiments, the basic order information includes but is not limited to order number, shipping location, receiving location, order weight, and order volume.

[0026] In some embodiments, the delivery service information includes but is not limited to delivery time requirements and service types.

[0027] In some embodiments, the delivery fee information includes but is not limited to the actual delivery fee amount and the standard delivery fee amount.

[0028] In some embodiments, the delivery personnel information includes but is not limited to the delivery personnel number and the region to which they belong.

[0029] The embodiments of the present invention remove invalid data, simplify the data processing process, reduce unnecessary steps, and improve overall work efficiency; at the same time, cleaning invalid and duplicate data reduces the storage requirements of the database and reduces storage costs.

[0030] See also Figure 3 In a third embodiment of the method for analyzing differences in order delivery fees in an embodiment of the present invention, extracting key features of the order based on basic information of the order and delivery service information of the order includes: 301. According to the basic information of the logistics order, the shipping place and the receiving place of the logistics order are regionally coded; By assigning regional codes to the shipping and receiving locations, the standardization and consistency of geographic location information is ensured, facilitating subsequent data processing and analysis.

[0031] 302. Extracting regional features of the departure area and destination area of ​​the logistics order according to the regional codes of the shipping place and the receiving place of the logistics order; Based on the area coding, the departure area and destination area can be identified more accurately, the transportation route can be optimized, and unnecessary detours and waiting time can be reduced.

[0032] 303. Extracting a cargo volume-to-weight ratio feature of the logistics order according to the weight and volume of the order; 304. Extract the delivery service level characteristics of the logistics order according to the delivery time requirement and the delivery service type of the logistics order.

[0033] The embodiments of the present invention can identify long-term trends and seasonal patterns and assist business decision-making by analyzing the regional characteristics and delivery service level characteristics of a large number of orders; by encoding and extracting features from the basic information of logistics orders, not only can data quality and operational efficiency be significantly improved, but also the continuous optimization and development of the business can be promoted.

[0034] See also Figure 4 In a fourth embodiment of the method for analyzing difference in order dispatch fees in an embodiment of the present invention, the order dispatch fee difference analysis model is constructed, and the order dispatch fee difference analysis model is trained using the extracted key features and logistics order data, including: 401. Constructing a decision tree model for analyzing the difference in order dispatch fees; 402. Training the difference analysis decision tree model using the extracted key features and the historical order data of the logistics database; 403. Taking the regional characteristics of the departure area and the destination area of ​​the logistics order, the volume-to-weight ratio characteristics of the goods in the logistics order, and the delivery service level characteristics of the logistics order as input variables; 404. The difference between the actual delivery fee amount of the logistics order and the standard delivery fee amount of the logistics order is used as an output variable of the difference analysis decision tree model.

[0035] The embodiment of the present invention can more accurately predict the difference between the actual dispatch fee and the standard dispatch fee for each order and reduce errors through a decision tree model based on key features; by constructing and applying an order dispatch fee difference analysis decision tree model, the accuracy and efficiency of the dispatch fee difference analysis can be significantly improved.

[0036] See also Figure 5 In a fifth embodiment of the method for analyzing order dispatch fee differences in an embodiment of the present invention, the use of the extracted key features and the historical order data of the logistics database to train the difference analysis model includes: 501. During the training process, the information gain of each key feature for dividing the difference in dispatch fees is calculated; 502. Select the key feature with the largest information gain as the basis for dividing the current node, and recursively construct a decision tree model.

[0037] The decision tree model constructed based on information gain in the model of the embodiment of the present invention has a clear decision path and is easy to understand and explain. The information gain directly reflects the importance of each feature, helps to identify which factors have the greatest impact on the difference in dispatch fees, and provides guidance for business optimization; features with higher information gain can usually better divide the data set, so that the model converges to the optimal solution faster and shortens the training time.

[0038] See also Figure 6 , the sixth embodiment of the order dispatch fee difference analysis method in the embodiment of the present invention, the preset order dispatch fee difference threshold, inputting the order data to be analyzed into the order dispatch fee difference analysis model, and outputting the order dispatch fee difference prediction value, including: 601. Preset a difference threshold of the logistics order dispatch fee; 602. Input the logistics order data to be analyzed into the logistics order dispatch fee difference analysis model; 603. Outputting the predicted difference value of the logistics order dispatch fee to be analyzed; 604. Determine the difference between the actual delivery fee amount of the logistics order and the standard delivery fee amount of the logistics order based on the difference prediction value of the delivery fee of the logistics order.

[0039] Based on the trained model, the embodiment of the present invention can more accurately predict the difference between the actual dispatch fee and the standard dispatch fee for each order, thereby reducing errors; it provides a reliable numerical basis for dispatch fee review and settlement, thereby reducing the uncertainty caused by human judgment.

[0040] See also Figure 7 In the seventh embodiment of the method for analyzing the difference in order dispatch fees in the embodiment of the present invention, the dispatch fee difference comparison, comparing the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to determine the difference size of the order, includes: 701. Compare the predicted difference value of the logistics order dispatch fee to be analyzed with the difference threshold of the order dispatch fee; 702. If the predicted difference value of the logistics order dispatch fee to be analyzed is greater than or equal to the difference threshold of the order dispatch fee, it is determined that the difference of the order is large, and the logistics order is marked as an abnormal order; 703. If the predicted difference value of the logistics order dispatch fee to be analyzed is less than the difference threshold of the order dispatch fee, it is determined that the difference of the order is small, and the logistics order is marked as a normal order.

[0041] The embodiment of the present invention automatically classifies orders by setting thresholds, thereby reducing a large amount of manual review work and improving work efficiency; provides a clear standard to distinguish normal and abnormal orders, providing a reliable basis for subsequent processing; based on the results of model prediction and threshold comparison, the identification of abnormal orders is more objective and accurate, reducing errors caused by subjective judgment.

[0042] The above describes the order dispatch fee difference analysis method in the embodiment of the present invention. The following describes the order dispatch fee difference analysis device in the embodiment of the present invention. Figure 8 In one embodiment of the present invention, an order dispatch fee difference analysis device includes: The module 801 for obtaining logistics order data is used to obtain basic information and delivery service information of logistics orders; A key feature extraction module 802 is used to extract key features of the order based on the basic information of the order and the delivery service information of the order; Constructing an order dispatch fee difference analysis model module 803, for training the order dispatch fee difference analysis model using the extracted key features and logistics order data; The dispatch fee difference analysis module 804 is used to preset an order dispatch fee difference threshold, input the order data to be analyzed into the order dispatch fee difference analysis model, and output the order dispatch fee difference prediction value; The judgment and comparison module 805 is used to compare the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to judge the difference size of the order.

[0043] The order delivery fee difference analysis device of the present invention can quickly identify orders with large delivery fee differences through an automated analysis model, helping logistics companies to respond and adjust in a timely manner; by predicting delivery fee differences, logistics companies can plan budgets more accurately and allocate resources reasonably; identifying high-cost orders or delivery routes helps to optimize delivery paths and service methods, thereby reducing overall operating costs; and can clearly show customers the composition of delivery fees and the reasons for the differences, thereby increasing the transparency of logistics services and enhancing customer trust.

[0044] See also Fig. 9 Another embodiment of the order dispatch fee difference analysis device in the embodiment of the present invention includes: The module 801 for obtaining logistics order data is used to obtain basic information and delivery service information of logistics orders; In some embodiments, the module 801 for obtaining logistics order data includes: A connection unit 8011 is used to connect to the logistics database system of the logistics order; An acquisition unit 8012 is used to acquire basic information, delivery service information, dispatch fee information of the logistics order and historical order data of the logistics database through the logistics database system; Based on rich and accurate historical order data, management can make more intelligent and data-driven business decisions. Through in-depth analysis of historical data, long-term trends and seasonal patterns can be identified to help plan future resource allocation and service adjustments.

[0045] The processing unit 8013 is used to process the basic information of the logistics order, the distribution service information and the dispatch fee information of the logistics order, and remove invalid data and duplicate data.

[0046] In some embodiments, data related to order delivery fees are collected from the logistics database system, including basic order information, delivery service information, delivery fee information, and delivery personnel information.

[0047] In some embodiments, the basic order information includes but is not limited to order number, shipping location, receiving location, order weight, and order volume.

[0048] In some embodiments, the delivery service information includes but is not limited to delivery time requirements and service types.

[0049] In some embodiments, the delivery fee information includes but is not limited to the actual delivery fee amount and the standard delivery fee amount.

[0050] In some embodiments, the delivery personnel information includes but is not limited to the delivery personnel number and the region to which they belong.

[0051] The embodiments of the present invention remove invalid data, simplify the data processing process, reduce unnecessary steps, and improve overall work efficiency; at the same time, cleaning invalid and duplicate data reduces the storage requirements of the database and reduces storage costs.

[0052] A key feature extraction module 802 is used to extract key features of the order based on the basic information of the order and the delivery service information of the order; In some embodiments, the key feature extraction module 802 includes: The coding unit 8021 is used to perform regional coding on the shipping place and the receiving place of the logistics order according to the basic information of the logistics order; By assigning regional codes to the shipping and receiving locations, the standardization and consistency of geographic location information is ensured, facilitating subsequent data processing and analysis.

[0053] The first extraction unit 8022 is used to extract the regional characteristics of the departure area and destination area of ​​the logistics order according to the regional codes of the shipping place and the receiving place of the logistics order; Based on the area coding, the departure area and destination area can be identified more accurately, the transportation route can be optimized, and unnecessary detours and waiting time can be reduced.

[0054] The second extraction unit 8023 is used to extract the cargo volume-to-weight ratio feature of the logistics order according to the weight and volume of the order; The third extraction unit 8024 is used to extract the delivery service level characteristics of the logistics order according to the delivery time requirement and delivery service type of the logistics order.

[0055] The embodiments of the present invention can identify long-term trends and seasonal patterns and assist business decision-making by analyzing the regional characteristics and delivery service level characteristics of a large number of orders; by encoding and extracting features from the basic information of logistics orders, not only can data quality and operational efficiency be significantly improved, but also the continuous optimization and development of the business can be promoted.

[0056] Constructing an order dispatch fee difference analysis model module 803, for training the order dispatch fee difference analysis model using the extracted key features and logistics order data; In some embodiments, the order dispatch fee difference analysis model building module 803 includes: A construction unit 8031 ​​is used to construct the order dispatch fee difference analysis decision tree model; A training unit 8032, configured to train the difference analysis decision tree model using the extracted key features and the historical order data of the logistics database; The first input unit 8033 is used to take the regional characteristics of the departure area and destination area of ​​the logistics order, the cargo volume-to-weight ratio characteristics of the logistics order, and the delivery service level characteristics of the logistics order as input variables; The first output unit 8034 is used to use the difference between the actual delivery fee amount of the logistics order and the standard delivery fee amount of the logistics order as the output variable of the difference analysis decision tree model.

[0057] The embodiment of the present invention can more accurately predict the difference between the actual dispatch fee and the standard dispatch fee for each order and reduce errors through a decision tree model based on key features; by constructing and applying an order dispatch fee difference analysis decision tree model, the accuracy and efficiency of the dispatch fee difference analysis can be significantly improved.

[0058] The dispatch fee difference analysis module 804 is used to preset an order dispatch fee difference threshold, input the order data to be analyzed into the order dispatch fee difference analysis model, and output the order dispatch fee difference prediction value; In some embodiments, the dispatch fee difference analysis module 804 includes: A preset unit 8041 is used to preset a difference threshold of the logistics order dispatch fee; The second input unit 8042 is used to input the logistics order data to be analyzed into the logistics order dispatch fee difference analysis model; The second output unit 8043 is used to output the difference prediction value of the logistics order dispatch fee to be analyzed; The judgment unit 8044 is used to judge the difference between the actual dispatch fee amount of the logistics order and the standard dispatch fee amount of the logistics order according to the difference prediction value of the dispatch fee of the logistics order.

[0059] Based on the trained model, the embodiment of the present invention can more accurately predict the difference between the actual dispatch fee and the standard dispatch fee for each order, thereby reducing errors; it provides a reliable numerical basis for dispatch fee review and settlement, thereby reducing the uncertainty caused by human judgment.

[0060] The judgment and comparison module 805 is used to compare the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to judge the difference size of the order.

[0061] In some embodiments, the judgment and comparison module 805 includes: A comparison unit 8051 is used to compare the difference prediction value of the logistics order dispatch fee to be analyzed with the difference threshold of the order dispatch fee; The first marking unit 8052 is used to judge that the difference of the order is large and mark the logistics order as an abnormal order if the difference prediction value of the logistics order dispatch fee to be analyzed is greater than or equal to the difference threshold of the order dispatch fee; The second marking unit 8053 is used to judge that the difference of the order is small and mark the logistics order as a normal order if the difference prediction value of the logistics order dispatch fee to be analyzed is less than the difference threshold of the order dispatch fee.

[0062] The embodiment of the present invention automatically classifies orders by setting thresholds, thereby reducing a large amount of manual review work and improving work efficiency; provides a clear standard to distinguish normal and abnormal orders, providing a reliable basis for subsequent processing; based on the results of model prediction and threshold comparison, the identification of abnormal orders is more objective and accurate, reducing errors caused by subjective judgment.

[0063] above Figure 8 and Fig. 9 The order dispatch fee difference analysis method in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0064] Fig.10 It is a structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device 900 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 910 (for example, one or more processors) and a memory 920, and one or more storage media 930 (for example, one or more mass storage devices) storing application programs 933 or data 932. Among them, the memory 920 and the storage medium 930 can be short-term storage or permanent storage. The program stored in the storage medium 930 may include one or more upper modules (not shown in the figure) for vehicle dispatching at a distribution center, and each module may include a series of instruction operations in the electronic device 900. Furthermore, the processor 910 may be configured to communicate with the storage medium 930 to execute a series of instruction operations in the storage medium 930 on the electronic device 900.

[0065] The electronic device 900 may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input and output interfaces 950, and / or one or more operating systems 931, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Fig.10The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0066] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the distribution center vehicle dispatching method.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0068] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0069] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing order dispatch fee differences, characterized in that: The method comprises: Obtain logistics order data, which is used to obtain basic information of logistics orders and delivery service information; Extracting key features of the order based on the basic information of the order and the delivery service information of the order; Constructing an order dispatch fee difference analysis model, and training the order dispatch fee difference analysis model using the extracted key features and logistics order data; Preset an order dispatch fee difference threshold, input the order data to be analyzed into the order dispatch fee difference analysis model, and output the order dispatch fee difference prediction value; The dispatch fee difference comparison is to compare the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to determine the difference size of the order.

2. The order dispatch fee difference analysis method according to claim 1 is characterized in that: The acquisition of logistics order data is used to obtain basic information and delivery service information of the logistics order, including: A logistics database system connected to the logistics order; Obtaining basic information, delivery service information, dispatch fee information of the logistics order and historical order data of the logistics database through the logistics database system; The basic information of the logistics order, the delivery service information and the dispatch fee information of the logistics order are processed to remove invalid data and duplicate data.

3. The order dispatch fee difference analysis method according to claim 2 is characterized in that: Extracting key features of the order based on the basic information of the order and the delivery service information of the order includes: According to the basic information of the logistics order, the shipping place and the receiving place of the logistics order are regionally coded; Extracting regional features of the departure area and destination area of ​​the logistics order according to the regional codes of the shipping place and the receiving place of the logistics order; Extracting the cargo volume-to-weight ratio feature of the logistics order according to the weight and volume of the order; According to the delivery timeliness requirement and delivery service type of the logistics order, the delivery service level characteristics of the logistics order are extracted.

4. The order dispatch fee difference analysis method according to claim 3 is characterized in that: The constructing of the order dispatch fee difference analysis model, using the extracted key features and logistics order data to train the order dispatch fee difference analysis model, includes: Constructing a decision tree model for analyzing the difference in order dispatch fees; Training the difference analysis decision tree model using the extracted key features and the historical order data of the logistics database; Taking the regional characteristics of the departure area and destination area of ​​the logistics order, the volume-to-weight ratio characteristics of the goods in the logistics order, and the delivery service level characteristics of the logistics order as input variables; The difference between the actual delivery fee amount of the logistics order and the standard delivery fee amount of the logistics order is used as the output variable of the difference analysis decision tree model.

5. The order dispatch fee difference analysis method according to claim 4 is characterized in that: The difference analysis model is trained using the extracted key features and the historical order data of the logistics database, including: During the training process, the information gain of each key feature for dividing the difference in dispatch fees is calculated; The key feature with the largest information gain is selected as the basis for dividing the current node, and the decision tree model is recursively constructed.

6. The order dispatch fee difference analysis method according to claim 3 is characterized in that: The preset order dispatch fee difference threshold value, inputting the order data to be analyzed into the order dispatch fee difference analysis model, and outputting the order dispatch fee difference prediction value, includes: Presetting a difference threshold of the logistics order dispatch fee; Inputting the logistics order data to be analyzed into the logistics order dispatch fee difference analysis model; Outputting the predicted difference value of the logistics order dispatch fee to be analyzed; The difference between the actual delivery fee amount of the logistics order and the standard delivery fee amount of the logistics order is determined based on the difference prediction value of the delivery fee of the logistics order.

7. The order dispatch fee difference analysis method according to claim 6 is characterized in that: The dispatch fee difference comparison compares the order dispatch fee difference prediction value with the order dispatch fee difference threshold to determine the difference size of the order, including: Comparing the predicted difference value of the logistics order dispatch fee to be analyzed with the difference threshold value of the order dispatch fee; If the predicted difference value of the logistics order dispatch fee to be analyzed is greater than or equal to the difference threshold of the order dispatch fee, it is determined that the difference of the order is large, and the logistics order is marked as an abnormal order; If the predicted difference value of the logistics order dispatch fee to be analyzed is less than the difference threshold of the order dispatch fee, it is determined that the difference of the order is small, and the logistics order is marked as a normal order.

8. An order dispatch fee difference analysis device, characterized in that: include: The module for obtaining logistics order data is used to obtain basic information and delivery service information of logistics orders; A key feature extraction module, used to extract key features of the order based on the basic information of the order and the delivery service information of the order; Constructing an order dispatch fee difference analysis model module, which is used to train the order dispatch fee difference analysis model using the extracted key features and logistics order data; A dispatch fee difference analysis module is used to preset an order dispatch fee difference threshold, input the order data to be analyzed into the order dispatch fee difference analysis model, and output the order dispatch fee difference prediction value; The judgment and comparison module is used to compare the order dispatch fee difference prediction value with the order dispatch fee difference threshold value to judge the difference size of the order.

9. A computer device, characterized in that: The computer device includes a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the computer device to execute the order dispatch fee difference analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is loaded by a processor to execute the order dispatch fee difference analysis method according to any one of claims 1 to 7.

Images