Bill generation method, device and equipment for delayed order
By obtaining delayed orders from the logistics platform, automatically calculate compensation amounts and update bills, generate electronic bills and send notifications, and using machine learning and reinforcement learning models to predict order types and compensation strategies, the problem of inefficient bill generation in the existing technology is solved. Automated processing and customer satisfaction improvement have been achieved.
Patent Information
- Application Number
- CN202411358147.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2024-12-27
AI Technical Summary
In the prior art, bill generation of delayed orders requires manual calculation and import into the system, resulting in long and low efficiency of the process and inability to realize automated processing.
By obtaining delayed orders from the logistics platform, calculating compensation amounts based on preset rules, updating bill information, generating electronic bills, and sending notification messages through preset communication channels, using machine learning and reinforcement learning models to predict order types and compensation strategies, reducing manual labor Intervention.
It realizes automated processing of bills for delayed delivery of orders, improves work efficiency, reduces manual intervention, and improves customer satisfaction.
Smart Images
Figure CN119205029A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of computer technology, and in particular to a method, device and equipment for generating a bill for a delayed order. Background Art
[0002] Currently, delayed delivery is handled by obtaining data offline, calculating manually, and then deducting fees through the settlement team. It is hoped that online configuration rules can be used to generate bills, push them for settlement and deduction. Delivery assessment has become a daily routine, but the deduction bills still require manual data extraction and calculation every day, which must then be imported into the system and approved before they can be recorded. The process is long and inefficient. Summary of the invention
[0003] The present invention provides a method, device and equipment for generating bills for delayed orders, which can realize automatic processing of bills for delayed delivery orders and improve work efficiency.
[0004] In one aspect, the present invention provides a method for generating a bill for a delayed order, the method comprising: Obtain delayed logistics orders with delayed delivery in the logistics platform; Calculate the compensation amount for the delayed logistics order according to preset rules; Acquire an order database corresponding to the logistics platform, and adjust the billing information in the order database according to the compensation amount of the delayed logistics order to obtain updated billing information; generating an electronic bill based on the updated bill information; Determine the target account corresponding to the electronic bill, send the electronic bill to the target account, and send a notification message to the target account through a preset communication channel, wherein the notification message includes a delay message of the delayed logistics order and bill change information.
[0005] Optionally, the method further includes: Obtaining logistics orders of the logistics platform in a historical period and using them as sample logistics orders; Extract the order submission time, estimated delivery time and actual delivery time of the sample logistics order; and determine the sample order type label of the sample logistics order according to the estimated delivery time and the actual delivery time of the sample logistics order; the sample order type includes a sample delayed order type and a sample non-delayed order type; Construct correlation features that affect order delays and determine sample correlation features for each sample logistics order; The order submission time, estimated delivery time, actual delivery time and sample-related features of the sample logistics order are used as sample input features; Input the sample input features into a preset model to perform order type prediction processing to obtain a sample order type result; According to the difference between the sample order type result and the sample order type label, the preset model is trained for order type prediction to obtain a delayed order prediction model.
[0006] Optionally, the obtaining of delayed logistics orders with delayed delivery in the logistics platform includes: Obtaining order submission time, estimated delivery time, actual delivery time, and target-related features corresponding to all logistics orders of the logistics platform as target input features; Inputting the target input features corresponding to each logistics order into the delayed order prediction model to perform order type prediction processing to obtain the target order type of each logistics order; The logistics orders whose target order type is the delayed order type are determined as delayed logistics orders.
[0007] Optionally, the method further includes: Obtain an order data set corresponding to a historical compensation logistics order, and determine a historical compensation strategy label corresponding to the historical compensation physical order; Inputting the order data set corresponding to the historical compensation logistics order into the reinforcement learning model to perform compensation strategy prediction processing to obtain the compensation strategy prediction result; According to the difference between the compensation strategy prediction result and the historical compensation strategy label, the reinforcement learning model is trained to obtain a compensation strategy prediction model; The calculating the compensation amount for the delayed logistics order according to the preset rules includes: Inputting the order data set of the delayed logistics order into the compensation strategy prediction model to perform compensation strategy prediction processing to obtain a target compensation strategy; Determining the preset rule based on the target compensation strategy; The compensation amount for the delayed logistics order is calculated according to the preset rules.
[0008] Optionally, the method further includes: Obtaining feedback information from the target account regarding the delayed logistics order; Inputting the feedback information into an emotion recognition model for emotion recognition processing to obtain a target emotion result of the target account; The target emotion result corresponding to the delayed logistics order is sent to the order management system, so that the order management system adjusts the order compensation strategy according to the target emotion result.
[0009] Optionally, the training method of the emotion recognition model includes: Obtain historical feedback information of historical accounts for historical delayed orders; the historical feedback information is marked with a historical sentiment label of the historical account; Inputting the historical feedback information into a sentiment analysis model for text analysis processing to obtain a historical sentiment prediction result; The emotion analysis model is trained according to the difference between the historical emotion prediction result and the historical emotion label to obtain the emotion recognition model.
[0010] Optionally, after sending the notification message to the target account through the preset communication channel, the method further includes: The generation process of the electronic bill and the feedback information sent to the target account are recorded; the feedback information includes the sending message of the electronic bill and the notification message.
[0011] Another aspect provides a device for generating a bill for a delayed order, the device comprising: The delayed order acquisition module is used to acquire delayed logistics orders with delayed delivery in the logistics platform; A compensation amount calculation module, used to calculate the compensation amount of the delayed logistics order according to preset rules; A bill updating module, used to obtain an order database corresponding to the logistics platform, and adjust the bill information in the order database according to the compensation amount of the delayed logistics order to obtain updated bill information; An order generation module, used for generating an electronic bill according to the updated bill information; A message sending module is used to determine the target account corresponding to the electronic bill, send the electronic bill to the target account, and send a notification message to the target account through a preset communication channel, wherein the notification message includes a delay message of the delayed logistics order and bill change information.
[0012] Optionally, the device further comprises: A sample order acquisition module is used to acquire the logistics orders of the logistics platform in the historical period and use them as sample logistics orders; A time extraction module is used to extract the order submission time, estimated delivery time and actual delivery time of the sample logistics order; and determine the sample order type label of the sample logistics order according to the estimated delivery time and the actual delivery time of the sample logistics order; the sample order type includes a sample delayed order type and a sample non-delayed order type; A sample correlation feature determination module is used to construct correlation features that affect order delays and determine sample correlation features for each sample logistics order; A sample input feature determination module, used to use the order submission time, estimated delivery time, actual delivery time and the sample associated features of the sample logistics order as sample input features; A sample result determination module is used to input the sample input features into a preset model to perform order type prediction processing to obtain a sample order type result; The model training module is used to perform order type prediction training on the preset model according to the difference between the sample order type result and the sample order type label to obtain a delayed order prediction model.
[0013] Optionally, the delayed order acquisition module is further used to acquire order submission time, estimated delivery time, actual delivery time and target-related features corresponding to all logistics orders of the logistics platform as target input features; Inputting the target input features corresponding to each logistics order into the delayed order prediction model to perform order type prediction processing to obtain the target order type of each logistics order; The logistics orders whose target order type is the delayed order type are determined as delayed logistics orders.
[0014] Optionally, the device further comprises: A historical data acquisition module, used to acquire an order data set corresponding to a historical compensation logistics order, and determine a historical compensation strategy label corresponding to the historical compensation physical order; A compensation prediction module is used to input the order data set corresponding to the historical compensation logistics order into the reinforcement learning model to perform compensation strategy prediction processing to obtain a compensation strategy prediction result; A compensation model training module, used to train the reinforcement learning model according to the difference between the compensation strategy prediction result and the historical compensation strategy label to obtain a compensation strategy prediction model; The compensation amount calculation module is further used to input the order data set of the delayed logistics order into the compensation strategy prediction model to perform compensation strategy prediction processing to obtain a target compensation strategy; Determining the preset rule based on the target compensation strategy; The compensation amount for the delayed logistics order is calculated according to the preset rules.
[0015] Optionally, the device further comprises: A feedback information acquisition module, used to acquire feedback information of the target account regarding the delayed logistics order; A target result determination module, used for inputting the feedback information into an emotion recognition model for emotion recognition processing to obtain a target emotion result of the target account; A result sending module is used to send the target emotion result corresponding to the delayed logistics order to the order management system, so that the order management system adjusts the order compensation strategy according to the target emotion result.
[0016] Optionally, the device further comprises: A historical information acquisition module, used to acquire historical feedback information of historical accounts for historical delayed orders; the historical feedback information is marked with a historical sentiment label of the historical account; A historical result prediction module is used to input the historical feedback information into a sentiment analysis model for text analysis processing to obtain a historical sentiment prediction result; The training module is used to train the emotion analysis model according to the difference between the historical emotion prediction result and the historical emotion label to obtain the emotion recognition model.
[0017] Optionally, the device further comprises: A recording module is used to record the generation process of the electronic bill and the feedback information sent to the target account; the feedback information includes the sending message of the electronic bill and the notification message.
[0018] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method for generating bills for delayed orders as described above.
[0019] On the other hand, a computer storage medium is provided, which stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the bill generation method for delayed orders as described above.
[0020] On the other hand, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes to implement the above-mentioned method for generating bills for delayed orders.
[0021] The method, device and equipment for generating bills for delayed orders provided by the present invention have the following technical effects: The present invention obtains delayed logistics orders with delayed delivery in a logistics platform; calculates the compensation amount of the delayed logistics order according to preset rules; obtains the order database corresponding to the logistics platform, and adjusts the billing information in the order database according to the compensation amount of the delayed logistics order to obtain updated billing information; generates an electronic bill according to the updated billing information; determines the target account corresponding to the electronic bill, sends the electronic bill to the target account, and sends a notification message to the target account through a preset communication channel, the notification message includes the delay message of the delayed logistics order and billing change information. The present invention realizes the automated processing of bills for delayed delivery orders, reduces manual intervention, and improves work efficiency; at the same time, through reasonable compensation for delayed delivery orders, it helps to improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] In order to more clearly illustrate the technical solutions and advantages of the embodiments of this specification or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0023] Figure 1 is a schematic diagram of a bill generation system for delayed orders provided in an embodiment of the present specification; Figure 2 It is a flowchart of a method for generating a bill for a delayed order provided in an embodiment of this specification; Figure 3 It is a flowchart of a method for training a delayed order prediction model provided by an embodiment of this specification; Figure 4 It is a flowchart of a method for training a compensation strategy prediction model provided by an embodiment of this specification; Figure 5 It is a flowchart of a training method for an emotion recognition model provided in an embodiment of this specification; Figure 6 It is a structural schematic diagram of a bill generating device for delayed orders provided in an embodiment of this specification; Figure 7 It is a structural diagram of a server provided in an embodiment of this specification. DETAILED DESCRIPTION
[0024] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0025] It should be noted that the terms "first", "second", etc. 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 interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.
[0026] See also Figure 1 , Figure 1 is a schematic diagram of a bill generation system for delayed orders provided in an embodiment of this specification, such as Figure 1 As shown, the bill generation system for delayed orders may include at least a server 01 and a client 02 .
[0027] Specifically, in the embodiments of this specification, the server 01 may include an independently operated server, or a distributed server, or a server cluster composed of multiple servers, and may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Server 01 may include a network communication unit, a processor, a memory, and the like. Specifically, the server 01 can be used to generate an electronic bill for a delayed logistics order and send the electronic bill to the target account.
[0028] Specifically, in the embodiments of this specification, the client 02 may include physical devices such as smart phones, desktop computers, tablet computers, laptop computers, digital assistants, smart wearable devices, smart speakers, vehicle terminals, smart TVs, etc., and may also include software running in physical devices, such as web pages provided by some service providers to users, or applications provided by these service providers to users. Specifically, the client 02 may be used to display electronic bills.
[0029] The following describes a method for generating a bill for a delayed order of the present invention. Figure 2 It is a flowchart of a method for generating a bill for a delayed order provided in an embodiment of this specification. This specification provides method operation steps as described in the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in sequence or in parallel (for example, in a parallel processor or multi-threaded processing environment) according to the method shown in the embodiment or the drawings. Specifically, Figure 2 As shown, the method may include: S201: Obtain delayed logistics orders with delayed delivery in the logistics platform. In the embodiment of the present specification, all delayed logistics orders with delayed delivery in the logistics platform can be obtained.
[0030] S203: Calculate the compensation amount for the delayed logistics order according to preset rules.
[0031] In the embodiments of this specification, preset rules can be set to determine the corresponding compensation amount according to the delay time of the delayed logistics order, order package attributes and other information. For example, the longer the delay time, the greater the compensation amount.
[0032] S205: Acquire an order database corresponding to the logistics platform, and adjust the billing information in the order database according to the compensation amount of the delayed logistics order to obtain updated billing information.
[0033] In the embodiments of the present specification, the billing information in the order database may be automatically updated, including but not limited to price adjustments and compensation discounts.
[0034] S207: Generate an electronic bill according to the updated bill information.
[0035] In the embodiment of the present specification, an electronic bill including updated bill information may be generated.
[0036] S209: Determine the target account corresponding to the electronic bill, send the electronic bill to the target account, and send a notification message to the target account through a preset communication channel, wherein the notification message includes a delay message of the delayed logistics order and bill change information.
[0037] In the embodiments of the present specification, electronic bills can be sent to relevant customers, and customers can be notified of delayed delivery and bill changes through preset communication channels, including but not limited to phone calls, text messages, and application push notifications.
[0038] In the embodiments of this specification, Figure 3 As shown, the method also includes: S301: Obtaining logistics orders of the logistics platform in a historical period and using them as sample logistics orders; S303: extracting the order submission time, estimated delivery time and actual delivery time of the sample logistics order; and determining the sample order type label of the sample logistics order according to the estimated delivery time and the actual delivery time of the sample logistics order; the sample order type includes a sample delayed order type and a sample non-delayed order type; S305: constructing correlation features that affect order delays, and determining sample correlation features of each sample logistics order; S307: Taking the order submission time, estimated delivery time, actual delivery time and the sample associated features of the sample logistics order as sample input features; S309: Inputting the sample input features into a preset model to perform order type prediction processing to obtain a sample order type result; S3011: According to the difference between the sample order type result and the sample order type label, the preset model is trained for order type prediction to obtain a delayed order prediction model.
[0039] In the embodiments of this specification, a delayed order prediction model can be trained using a machine learning algorithm (such as a random forest, a neural network, etc.), which can predict which orders may be delayed based on historical data, prepare in advance and notify the user. The order submission time, estimated delivery time and actual delivery time of the sample logistics order can be extracted; and the sample order type label of the sample logistics order can be determined based on the estimated delivery time and the actual delivery time of the sample logistics order; thereby screening out sample delayed orders and sample non-delayed orders. The target loss data can be calculated based on the difference between the sample order type result and the sample order type label, and the preset model can be trained for order type prediction based on the target loss data until the training end condition is met to obtain a delayed order prediction model.
[0040] Data collection: Extract key indicators such as order submission time, estimated delivery time, and actual delivery time from historical order data.
[0041] Feature engineering: Construct relevant features that affect order delays, such as seasonal factors, holiday effects, weather conditions, etc.
[0042] Model training: Use supervised learning algorithms, such as XGBoost or LSTM neural networks, to train historical data to obtain a delayed order prediction model.
[0043] Model deployment: Deploy the trained model to the order management system to predict potential delayed orders in real time.
[0044] In the embodiment of this specification, the step of obtaining a delayed logistics order with delayed delivery in the logistics platform includes: Obtaining order submission time, estimated delivery time, actual delivery time, and target-related features corresponding to all logistics orders of the logistics platform as target input features; Inputting the target input features corresponding to each logistics order into the delayed order prediction model to perform order type prediction processing to obtain the target order type of each logistics order; The logistics orders whose target order type is the delayed order type are determined as delayed logistics orders.
[0045] In the embodiments of the present specification, the method for determining the target association features is similar to the method for determining the sample association features; the trained model can be deployed to the order management system, and the order submission time, estimated delivery time, actual delivery time and target association features corresponding to all logistics orders of the logistics platform can be obtained as target input features; the target input features corresponding to each logistics order are input into the delayed order prediction model for order type prediction processing to obtain the target order type of each logistics order, so that potential delayed orders can be predicted in real time, and delayed orders can be determined quickly and accurately.
[0046] In the embodiments of this specification, Figure 4 As shown, the method also includes: S401: Acquire an order data set corresponding to a historical compensation logistics order, and determine a historical compensation strategy label corresponding to the historical compensation physical order; S403: Inputting the order data set corresponding to the historical compensation logistics order into the reinforcement learning model to perform compensation strategy prediction processing to obtain a compensation strategy prediction result; S405: Training the reinforcement learning model according to the difference between the compensation strategy prediction result and the historical compensation strategy label to obtain a compensation strategy prediction model.
[0047] Exemplarily, the calculating the compensation amount for the delayed logistics order according to a preset rule includes: Inputting the order data set of the delayed logistics order into the compensation strategy prediction model to perform compensation strategy prediction processing to obtain a target compensation strategy; Determining the preset rule based on the target compensation strategy; The compensation amount for the delayed logistics order is calculated according to the preset rules.
[0048] In the embodiments of the present specification, a data set of historical compensation cases can be compiled, including factors such as compensation amount, customer feedback, order value, etc.
[0049] Model building: Use reinforcement learning algorithms, such as Q-Learning or Deep Q-Networks, to optimize compensation strategies to maximize customer satisfaction while minimizing enterprise costs.
[0050] Strategy evaluation: Evaluate the effects of different compensation strategies through simulation tests, and select the optimal strategy for application in actual scenarios.
[0051] In the embodiments of the present specification, the target compensation strategy for delayed logistics orders can be quickly determined through the compensation strategy prediction model, thereby further determining the compensation amount.
[0052] In the embodiment of this specification, the method further includes: Obtaining feedback information from the target account regarding the delayed logistics order; Inputting the feedback information into an emotion recognition model for emotion recognition processing to obtain a target emotion result of the target account; The target emotion result corresponding to the delayed logistics order is sent to the order management system, so that the order management system adjusts the order compensation strategy according to the target emotion result.
[0053] In the embodiments of this specification, Figure 5 As shown, the training method of the emotion recognition model includes: S501: Obtain historical feedback information of a historical account for historical delayed orders; the historical feedback information is marked with a historical sentiment label of the historical account; S503: Inputting the historical feedback information into a sentiment analysis model for text analysis processing to obtain a historical sentiment prediction result; S505: According to the difference between the historical emotion prediction result and the historical emotion label, the emotion analysis model is trained to obtain the emotion recognition model.
[0054] In the embodiments of this specification, natural language processing technology is used to analyze customer feedback information, quantify customer satisfaction with delayed delivery and its handling method, and provide data support for subsequent improvements. The sentiment analysis model can be trained according to the difference between the historical sentiment prediction results and the historical sentiment labels until the difference between the historical sentiment prediction results and the historical sentiment labels is less than a preset value, the training ends, and the sentiment analysis model after the training is used as the emotion recognition model.
[0055] Data collection: Collect customer feedback on delayed orders, including emails, social media comments, etc.
[0056] Text analysis: Use sentiment analysis technology to process text data and identify customers' emotional tendencies.
[0057] Result feedback: The analysis results are fed back to the order management system for continuous improvement of compensation strategies and service quality.
[0058] In the embodiment of this specification, the delayed delivery report is developed: it supports viewing the data such as the amount to be delivered, the amount not delivered, and the achievement rate value 24H, 36H, 48H, 72H, and 96H after the collection. The logic of the amount to be delivered: eliminate the returned items / / international items / receipts and the express items that have been normally signed / distributed (consistent with the elimination rules of 8), take the first collection time, and remove the duplicates within 15 days of repeated collection. The scanning type includes the submission date 16 receipt scan / 14 collection scan / 81 branch designated subordinate location scan / 65 collection scan m / 5 package scan. The logic of the amount not delivered: the express items that have not generated the delivery record within the specified time. The judgment of the delivered items: the scan of the distribution type generated before the specified time (518000 / 528455 is regarded as distribution, 66 arrival scan m, 67 outgoing scan m is a virtual contact proposed distribution record and is not counted as a delivery) / normal receipt record 10-1 / initial return record 88-44 is a delivery.
[0059] For example, the online assessment report adds the rules of scheduled collection / scheduled delivery early / delayed delivery. After maintenance, the specific outlets or service departments are excluded to form the final bill, which is pushed after confirmation and automatically connected to the prepayment. The specific form is attached. Scheduled collection rule maintenance fields: order source / whether it is direct sales / assessment target value / assessment rate / assessment field acceptor 1 / assessment field 2 Timed delivery rule maintenance fields: Order source / branch-delivery distribution mileage / window type / other identification assessment target value / assessment rate / assessment field 1 / assessment field 2 Maintenance fields for delayed delivery rules on acceptance date: Order source / branch-delivery distribution mileage / window type / other identifiers / assessment target value / assessment rate / assessment field 1 / assessment field 2.
[0060] In the embodiment of the present specification, after sending the notification message to the target account through the preset communication channel, the method further includes: The generation process of the electronic bill and the feedback information sent to the target account are recorded; the feedback information includes the sending message of the electronic bill and the notification message.
[0061] In the embodiments of the present specification, the entire bill generation process and customer feedback information may be recorded for subsequent analysis and improvement.
[0062] In an exemplary embodiment, a method for generating a bill for a delayed order is provided, comprising: Step 1: Receive and record information on all delayed delivery orders; Step 2: Calculate the compensation amount for each delayed delivery order according to the preset rules; Step 3: Automatically update the billing information in the order database, including but not limited to price adjustments, compensation discounts, etc.; Step 4: Generate an electronic bill containing updated bill information; Step 5: Send the electronic bill to the relevant customers and inform them of the delayed delivery and bill changes through the predetermined communication channels; Step 6. Record the entire bill generation process and customer feedback for subsequent analysis and improvement.
[0063] It can be seen from the technical solutions provided by the above embodiments of this specification that the embodiments of this specification obtain delayed logistics orders with delayed delivery in the logistics platform; calculate the compensation amount of the delayed logistics order according to preset rules; obtain the order database corresponding to the logistics platform, and adjust the billing information in the order database according to the compensation amount of the delayed logistics order to obtain updated billing information; generate an electronic bill according to the updated billing information; determine the target account corresponding to the electronic bill, send the electronic bill to the target account, and send a notification message to the target account through a preset communication channel, the notification message includes the delay message of the delayed logistics order and bill change information. The present invention realizes the automated processing of bills for delayed delivery orders, reduces manual intervention, and improves work efficiency; at the same time, through reasonable compensation for delayed delivery orders, it helps to improve customer satisfaction.
[0064] The embodiment of this specification also provides a bill generation device for delayed orders, such as Figure 6 As shown, the device comprises: The delayed order acquisition module 610 is used to acquire delayed logistics orders with delayed delivery in the logistics platform; A compensation amount calculation module 620, used to calculate the compensation amount of the delayed logistics order according to a preset rule; The bill updating module 630 is used to obtain the order database corresponding to the logistics platform, and adjust the bill information in the order database according to the compensation amount of the delayed logistics order to obtain updated bill information; An order generation module 640, configured to generate an electronic bill according to the updated bill information; The message sending module 650 is used to determine the target account corresponding to the electronic bill, send the electronic bill to the target account, and send a notification message to the target account through a preset communication channel, wherein the notification message includes a delay message of the delayed logistics order and bill change information.
[0065] Optionally, the device further comprises: A sample order acquisition module is used to acquire the logistics orders of the logistics platform in the historical period and use them as sample logistics orders; A time extraction module is used to extract the order submission time, estimated delivery time and actual delivery time of the sample logistics order; and determine the sample order type label of the sample logistics order according to the estimated delivery time and the actual delivery time of the sample logistics order; the sample order type includes a sample delayed order type and a sample non-delayed order type; A sample correlation feature determination module is used to construct correlation features that affect order delays and determine sample correlation features for each sample logistics order; A sample input feature determination module, used to use the order submission time, estimated delivery time, actual delivery time and the sample associated features of the sample logistics order as sample input features; A sample result determination module is used to input the sample input features into a preset model to perform order type prediction processing to obtain a sample order type result; The model training module is used to perform order type prediction training on the preset model according to the difference between the sample order type result and the sample order type label to obtain a delayed order prediction model.
[0066] Optionally, the delayed order acquisition module is further used to acquire order submission time, estimated delivery time, actual delivery time and target-related features corresponding to all logistics orders of the logistics platform as target input features; Inputting the target input features corresponding to each logistics order into the delayed order prediction model to perform order type prediction processing to obtain the target order type of each logistics order; The logistics orders whose target order type is the delayed order type are determined as delayed logistics orders.
[0067] Optionally, the device further comprises: A historical data acquisition module, used to acquire an order data set corresponding to a historical compensation logistics order, and determine a historical compensation strategy label corresponding to the historical compensation physical order; A compensation prediction module is used to input the order data set corresponding to the historical compensation logistics order into the reinforcement learning model to perform compensation strategy prediction processing to obtain a compensation strategy prediction result; A compensation model training module, used to train the reinforcement learning model according to the difference between the compensation strategy prediction result and the historical compensation strategy label to obtain a compensation strategy prediction model; The compensation amount calculation module is further used to input the order data set of the delayed logistics order into the compensation strategy prediction model to perform compensation strategy prediction processing to obtain a target compensation strategy; Determining the preset rule based on the target compensation strategy; The compensation amount for the delayed logistics order is calculated according to the preset rules.
[0068] Optionally, the device further comprises: A feedback information acquisition module, used to acquire feedback information of the target account regarding the delayed logistics order; A target result determination module, used for inputting the feedback information into an emotion recognition model for emotion recognition processing to obtain a target emotion result of the target account; A result sending module is used to send the target emotion result corresponding to the delayed logistics order to the order management system, so that the order management system adjusts the order compensation strategy according to the target emotion result.
[0069] Optionally, the device further comprises: A historical information acquisition module, used to acquire historical feedback information of historical accounts for historical delayed orders; the historical feedback information is marked with a historical sentiment label of the historical account; A historical result prediction module is used to input the historical feedback information into a sentiment analysis model for text analysis processing to obtain a historical sentiment prediction result; The training module is used to train the emotion analysis model according to the difference between the historical emotion prediction result and the historical emotion label to obtain the emotion recognition model.
[0070] Optionally, the device further comprises: A recording module is used to record the generation process of the electronic bill and the feedback information sent to the target account; the feedback information includes the sending message of the electronic bill and the notification message.
[0071] The device and method embodiments in the described device embodiments are based on the same inventive concept.
[0072] An embodiment of the present specification provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the method for generating a bill for a delayed order as provided in the above method embodiment.
[0073] An embodiment of the present invention also provides a computer storage medium, which can be set in a terminal to store at least one instruction or at least one program related to a method for generating a bill for a delayed order in a method embodiment. The at least one instruction or at least one program is loaded and executed by the processor to implement the method for generating a bill for a delayed order provided in the above method embodiment.
[0074] The embodiment of the present invention also provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes to implement the method for generating a bill for a delayed order provided in the above method embodiment.
[0075] Optionally, in the embodiments of this specification, the storage medium may be located in at least one of the multiple network servers of the computer network. Optionally, in this embodiment, the storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.
[0076] The memory described in the embodiments of this specification can be used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, application programs required for functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.
[0077] The method for generating a bill for a delayed order provided in the embodiments of this specification can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. For example, running on a server, Figure 7 1 is a hardware structure block diagram of a server of a method for generating bills for delayed orders provided in an embodiment of this specification. Figure 7As shown, the server 700 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPU) 710 (the central processing unit 710 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 730 for storing data, and one or more storage media 720 (such as one or more mass storage devices) for storing application programs 723 or data 722. Among them, the memory 730 and the storage medium 720 can be short-term storage or permanent storage. The program stored in the storage medium 720 may include one or more modules, each of which may include a series of instruction operations on the server. Furthermore, the central processing unit 710 can be configured to communicate with the storage medium 720 and execute a series of instruction operations in the storage medium 720 on the server 700. The server 700 may also include one or more power supplies 760, one or more wired or wireless network interfaces 750, one or more input and output interfaces 740, and / or one or more operating systems 721, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.
[0078] The input / output interface 740 may be used to receive or send data via a network. A specific example of the network may include a wireless network provided by a communication provider of the server 700. In one example, the input / output interface 740 includes a network adapter (Network Interface Controller, NIC), which may be connected to other network devices via a base station so as to communicate with the Internet. In one example, the input / output interface 740 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.
[0079] It can be understood by those skilled in the art that Figure 7 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 7 More or fewer components as shown, or with Figure 7 Different configurations are shown.
[0080] It can be seen from the embodiments of the method, device, electronic device or storage medium for generating bills for delayed orders provided by the present invention that the present invention obtains delayed logistics orders for delayed delivery in a logistics platform; calculates the compensation amount of the delayed logistics order according to preset rules; obtains the order database corresponding to the logistics platform, and adjusts the bill information in the order database according to the compensation amount of the delayed logistics order to obtain updated bill information; generates an electronic bill according to the updated bill information; determines the target account corresponding to the electronic bill, sends the electronic bill to the target account, and sends a notification message to the target account through a preset communication channel, the notification message includes the delay message of the delayed logistics order and bill change information. The present invention realizes the automated processing of bills for delayed delivery orders, reduces manual intervention, and improves work efficiency; at the same time, through reasonable compensation for delayed delivery orders, it helps to improve customer satisfaction.
[0081] It should be noted that the above sequence of the embodiments of this specification is for description only and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0082] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0083] A person skilled in the art will appreciate that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.
[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims
1. A method for generating bills for delayed orders, characterized in that: The method comprises: Obtain delayed logistics orders with delayed delivery in the logistics platform; Calculate the compensation amount for the delayed logistics order according to preset rules; Acquire an order database corresponding to the logistics platform, and adjust the billing information in the order database according to the compensation amount of the delayed logistics order to obtain updated billing information; generating an electronic bill based on the updated bill information; Determine the target account corresponding to the electronic bill, send the electronic bill to the target account, and send a notification message to the target account through a preset communication channel, wherein the notification message includes a delay message of the delayed logistics order and bill change information.
2. The method according to claim 1, characterized in that The method further comprises: Obtaining logistics orders of the logistics platform in a historical period and using them as sample logistics orders; Extract the order submission time, estimated delivery time and actual delivery time of the sample logistics order; and determine the sample order type label of the sample logistics order according to the estimated delivery time and the actual delivery time of the sample logistics order; the sample order type includes a sample delayed order type and a sample non-delayed order type; Construct correlation features that affect order delays and determine sample correlation features for each sample logistics order; The order submission time, estimated delivery time, actual delivery time and sample-related features of the sample logistics order are used as sample input features; Input the sample input features into a preset model to perform order type prediction processing to obtain a sample order type result; According to the difference between the sample order type result and the sample order type label, the preset model is trained for order type prediction to obtain a delayed order prediction model.
3. The method according to claim 2, characterized in that The obtaining of delayed logistics orders with delayed delivery in the logistics platform includes: Obtaining order submission time, estimated delivery time, actual delivery time, and target-related features corresponding to all logistics orders of the logistics platform as target input features; Inputting the target input features corresponding to each logistics order into the delayed order prediction model to perform order type prediction processing to obtain the target order type of each logistics order; The logistics orders whose target order type is the delayed order type are determined as delayed logistics orders.
4. The method according to claim 1, characterized in that: The method further comprises: Obtain an order data set corresponding to a historical compensation logistics order, and determine a historical compensation strategy label corresponding to the historical compensation physical order; Inputting the order data set corresponding to the historical compensation logistics order into the reinforcement learning model to perform compensation strategy prediction processing to obtain the compensation strategy prediction result; According to the difference between the compensation strategy prediction result and the historical compensation strategy label, the reinforcement learning model is trained to obtain a compensation strategy prediction model; The calculating the compensation amount for the delayed logistics order according to the preset rules includes: Inputting the order data set of the delayed logistics order into the compensation strategy prediction model to perform compensation strategy prediction processing to obtain a target compensation strategy; Determining the preset rule based on the target compensation strategy; The compensation amount for the delayed logistics order is calculated according to the preset rules.
5. The method according to claim 1, characterized in that The method further comprises: Obtaining feedback information from the target account regarding the delayed logistics order; Inputting the feedback information into an emotion recognition model for emotion recognition processing to obtain a target emotion result of the target account; The target emotion result corresponding to the delayed logistics order is sent to the order management system, so that the order management system adjusts the order compensation strategy according to the target emotion result.
6. The method according to claim 5, characterized in that The training method of the emotion recognition model includes: Obtain historical feedback information of historical accounts for historical delayed orders; the historical feedback information is marked with a historical sentiment label of the historical account; Inputting the historical feedback information into a sentiment analysis model for text analysis processing to obtain a historical sentiment prediction result; The emotion analysis model is trained according to the difference between the historical emotion prediction result and the historical emotion label to obtain the emotion recognition model.
7. The method according to claim 1, characterized in that After sending the notification message to the target account through the preset communication channel, the method further includes: The generation process of the electronic bill and the feedback information sent to the target account are recorded; the feedback information includes the sending message of the electronic bill and the notification message.
8. A bill generation device for delayed orders, characterized in that: The device comprises: The delayed order acquisition module is used to acquire delayed logistics orders with delayed delivery in the logistics platform; A compensation amount calculation module, used to calculate the compensation amount of the delayed logistics order according to preset rules; A bill updating module, used to obtain an order database corresponding to the logistics platform, and adjust the bill information in the order database according to the compensation amount of the delayed logistics order to obtain updated bill information; An order generation module, used for generating an electronic bill according to the updated bill information; A message sending module is used to determine the target account corresponding to the electronic bill, send the electronic bill to the target account, and send a notification message to the target account through a preset communication channel, wherein the notification message includes a delay message of the delayed logistics order and bill change information.
9. An electronic device, characterized in that: The device includes: a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the method for generating a bill for a delayed order as described in any one of claims 1-7.
10. A computer storage medium, characterized in that: The computer storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the bill generation method for delayed orders as described in any one of claims 1-7.