Order payment management method and device, equipment and storage medium

Abstract

The invention relates to the field of logistics, and discloses an order payment management method, device and equipment and a storage medium, and the method is used for managing unpaid orders and generating settlement reports. The method comprises the steps of obtaining corresponding order information based on an order number of an uncompleted payment order, extracting a logistics state of the uncompleted payment order, screening out the uncompleted payment order with the logistics state being delivery, and marking the uncompleted payment order as a to-be-processed order; the payment state of the to-be-processed order and courier state information are acquired regularly, and if the payment state of the to-be-processed order is unpaid, the order information of the to-be-processed order and the courier state information are input into a trained multi-layer perceptron model; a reminding priority level and a reminding mode which are output by the trained multi-layer perceptron model and correspond to the order to be processed are obtained, a reminding message is generated, and the reminding message is pushed to a courier; and marking the to-be-processed order with the payment state being paid as a completed order so as to summarize and generate a settlement report.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular, to an order payment management method, device, equipment, and storage medium. Background Art

[0002] With the development of e-commerce business, the express delivery service is closely combined with payment services such as collection of payment for goods and cash on delivery. However, couriers face many problems when processing orders with payment information. For example, it is inconvenient to query the payment status. Among a batch of orders assigned to a certain courier, due to the inability to quickly know the payment status, it is found that some cash on delivery orders are not paid when delivering, which affects the delivery efficiency; it is easy to miscollect or miss collecting money. For example, when collecting payment for goods, due to the lack of effective reminder, the courier forgets to collect the payment from a certain customer; the settlement process after collecting payment for goods is cumbersome and error-prone. The courier needs to manually sort out a large amount of order payment information, which is prone to data errors; it is difficult to grasp the overall payment situation of the orders in charge in real time, and it is impossible to plan an efficient delivery route according to the payment situation. At the same time, the existing logistics system focuses on the control of the package transportation process and lacks support for order payment management.

[0003] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0004] The present invention provides an order payment management method, device, equipment, and storage medium for managing unpaid orders and generating settlement reports.

[0005] In the first aspect of the present invention, an order payment management method is provided. The order payment management method includes: obtaining the order number of an unpaid order, and obtaining order information corresponding to the unpaid order based on the order number of the unpaid order; extracting the logistics status of the unpaid order based on the order information, screening out the unpaid orders with the logistics status of being delivered, and marking them as orders to be processed; regularly obtaining the payment status and courier status information of the orders to be processed. If the payment status of the order to be processed is unpaid, input the order information and courier status information of the order to be processed into a trained multi-layer perceptron model, and obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model; generating a reminder message based on the reminder priority level and reminder method corresponding to the order to be processed, and pushing the reminder message to the courier; when the payment status of the order to be processed changes from unpaid to paid, marking the order to be processed with the payment status of paid as a completed order, and regularly collecting the order numbers and corresponding collection information of the completed orders to generate a settlement report by summarization.

[0006] Optionally, in the first implementation manner of the first aspect of the present invention, the obtaining the order number of the uncompleted payment order and obtaining the order information corresponding to the uncompleted payment order based on the order number of the uncompleted payment order includes: batch obtaining the order numbers of the uncompleted payment orders, constructing an SQL query statement based on the order numbers of the uncompleted payment orders; executing the SQL query statement to obtain the order information corresponding to the uncompleted payment order; converting the order information corresponding to the uncompleted payment order into a dictionary form and storing it in a pre-constructed order information list.

[0007] Optionally, in the second implementation manner of the first aspect of the present invention, the obtaining the logistics status of the uncompleted payment order, screening out the uncompleted payment orders with the logistics status of being delivered, and marking them as orders to be processed includes: traversing the order information of each uncompleted payment order in the order information list to determine whether there is a logistics status field; if so, adding the value of the logistics status field and the corresponding uncompleted payment order to the logistics status information list; screening out the uncompleted payment orders corresponding to the value of the logistics status field being delivered from the logistics status information list, and marking the screened uncompleted payment orders as orders to be processed.

[0008] Optionally, in the third implementation manner of the first aspect of the present invention, the regularly obtaining the payment status and the courier status information of the order to be processed, and if the payment status of the order to be processed is unpaid, inputting the order information and the courier status information of the order to be processed into a trained multi-layer perceptron model to obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model includes: constructing and training a multi-layer perceptron model to obtain a trained multi-layer perceptron model; regularly obtaining the payment status and the courier status information of the order to be processed, the payment status of the order to be processed including unpaid and paid, and the courier status information including the courier's busyness degree and the distance between the courier's current location and the recipient's location; if the payment status of the order to be processed is unpaid, inputting the order information and the courier status information of the order to be processed into the trained multi-layer perceptron model to obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model.

[0009] Optionally, in the fourth implementation manner of the first aspect of the present invention, the building and training of the multi-layer perceptron model to obtain a trained multi-layer perceptron model includes: building a multi-layer perceptron model using a deep learning framework, the multi-layer perceptron model including an input layer, a hidden layer, and an output layer, the input layer being used to receive the order information and courier status information of the order to be processed, the hidden layer being used to determine the reminder priority level and reminder method corresponding to the order to be processed according to the information output by the input layer, and outputting the determination result through the output layer; collecting historical order data, the historical order data including the order type, payment amount, courier busyness level, distance between the current location of the courier and the recipient location of the historical order, as well as the reminder priority level and reminder method; using the historical order data to train the multi-layer perceptron model, and optimizing the parameters of the multi-layer perceptron model using forward propagation and backward propagation during the training process to obtain a trained multi-layer perceptron model.

[0010] Optionally, in the fifth implementation manner of the first aspect of the present invention, the generating a reminder message based on the reminder priority level and reminder method corresponding to the order to be processed and pushing the reminder message to the courier includes: pre-building a reminder message generation template corresponding to the reminder priority level and reminder method, and classifying and storing the reminder message generation template in a template library; based on the reminder priority level and reminder method corresponding to the order to be processed, calling the corresponding reminder message generation template from the template library; filling the order information of the order to be processed into the called reminder message generation template to generate a reminder message, and pushing the reminder message to the courier.

[0011] Optionally, in the sixth implementation manner of the first aspect of the present invention, when the payment status of the order to be processed changes from unpaid to paid, marking the order to be processed with a payment status of paid as a completed order, and regularly collecting the order numbers and corresponding collection information of the completed orders to summarize and generate a settlement report includes: when the payment status of the order to be processed changes from unpaid to paid, marking the order to be processed with a payment status of paid as a completed order; regularly collecting the order numbers and corresponding collection information of the completed orders, and using a pre-trained convolutional neural network model to classify the completed orders into normal orders and abnormal orders; summarizing the normal orders and the abnormal orders respectively, and generating a settlement report.

[0012] In a second aspect of the present invention, an order payment management device is provided, including: an acquisition module, configured to acquire the order number of an unpaid order, and acquire order information corresponding to the unpaid order based on the order number of the unpaid order; a marking module, configured to extract the logistics status of the unpaid order based on the order information, screen out the unpaid orders with the logistics status of being dispatched, and mark them as orders to be processed; a judgment module, configured to regularly acquire the payment status and the courier status information of the orders to be processed, and if the payment status of the order to be processed is unpaid, input the order information and the courier status information of the order to be processed into a trained multi-layer perceptron model to obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model; a push module, configured to generate a reminder message based on the reminder priority level and reminder method corresponding to the order to be processed, and push the reminder message to the courier; a settlement module, configured to, when the payment status of the order to be processed changes from unpaid to paid, mark the order to be processed with the payment status of paid as a completed order, and regularly collect the order numbers and corresponding collection information of the completed orders to summarize and generate a settlement report.

[0013] Optionally, in the first implementation manner of the second aspect of the present invention, the acquisition module includes: a first acquisition unit, configured to batch-acquire the order numbers of unpaid orders, and construct an SQL query statement based on the order numbers of the unpaid orders; a second acquisition unit, configured to execute the SQL query statement to acquire the order information corresponding to the unpaid order; a conversion unit, configured to convert the order information corresponding to the unpaid order into a dictionary form and store it in a pre-constructed order information list.

[0014] Optionally, in the second implementation manner of the second aspect of the present invention, the marking module includes: a first judgment unit, configured to traverse the order information of each unpaid order in the order information list to judge whether there is a logistics status field; an addition unit, configured to, when there is a logistics status field, add the value of the logistics status field and the corresponding unpaid order to the logistics status information list; a first marking unit, configured to screen out the unpaid orders corresponding to the value of the logistics status field being dispatched from the logistics status information list, and mark the screened-out unpaid orders as orders to be processed.

[0015] Optionally, in the third implementation manner of the second aspect of the present invention, the judgment module includes: a training unit configured to construct and train a multi-layer perceptron model to obtain a trained multi-layer perceptron model; a third acquisition unit configured to periodically acquire the payment status and courier status information of the order to be processed, where the payment status of the order to be processed includes unpaid and paid, and the courier status information includes the courier's busyness level and the distance between the courier's current location and the recipient's location; a second judgment unit configured to, when the payment status of the order to be processed is unpaid, input the order information and courier status information of the order to be processed into the trained multi-layer perceptron model to obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model.

[0016] Optionally, in the fourth implementation manner of the second aspect of the present invention, the push module includes: a construction unit configured to pre-construct a reminder message generation template corresponding to the reminder priority level and reminder method, and classify and store the reminder message generation template in a template library; a generation unit configured to, based on the reminder priority level and reminder method corresponding to the order to be processed, call the corresponding reminder message generation template from the template library; a push unit configured to fill the order information of the order to be processed into the called reminder message generation template to generate a reminder message, and push the reminder message to the courier.

[0017] Optionally, in the fifth implementation manner of the second aspect of the present invention, the settlement module includes: a second marking unit configured to, when the payment status of the order to be processed changes from unpaid to paid, mark the order to be processed with a payment status of paid as a completed order; a division unit configured to periodically collect the order numbers and corresponding collection information of the completed orders, and use a pre-trained convolutional neural network model to divide the completed orders into normal orders and abnormal orders; a settlement unit configured to summarize the normal orders and the abnormal orders respectively, and generate a settlement report.

[0018] The third aspect of the present invention provides an order payment management device, including: a memory and at least one processor, where computer-readable instructions are stored in the memory, and the memory and the at least one processor are interconnected by a line; the at least one processor calls the computer-readable instructions in the memory so that the order payment management device executes each step of the order payment management method as described above.

[0019] The fourth aspect of the present invention provides a computer-readable storage medium, in which computer-readable instructions are stored, and when it runs on a computer, it causes the computer to execute each step of the order payment management method as described above.

[0020] In the technical solution provided by the present invention, it can effectively identify and process orders with a logistics status of being delivered but not yet paid. By using a multi-layer perceptron model to intelligently judge the reminder priority and method, it timely notifies the courier to pay attention, which not only improves the automation and intelligence level of order processing, but also ensures the timeliness and accuracy of commodity distribution. At the same time, it regularly summarizes the completed order information to generate a settlement report, which is convenient for financial management and data analysis, and overall improves the operation efficiency and optimizes the customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is the first flowchart of the order payment management method provided by the embodiment of the present invention;

[0022] Figure 2 It is the second flowchart of the order payment management method provided by the embodiment of the present invention;

[0023] Figure 3 It is the third flowchart of the order payment management method provided by the embodiment of the present invention;

[0024] Figure 4 It is the fourth flowchart of the order payment management method provided by the embodiment of the present invention;

[0025] Figure 5 It is the fifth flowchart of the order payment management method provided by the embodiment of the present invention;

[0026] Figure 6 It is the sixth flowchart of the order payment management method provided by the embodiment of the present invention;

[0027] Figure 7 It is the structural schematic diagram of the order payment management device provided by the embodiment of the present invention;

[0028] Figure 8 It is the structural schematic diagram of the order payment management device provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Embodiments of the present invention provide an order payment management method, apparatus, device, and storage medium. The method is used to manage unpaid orders and generate settlement reports. The method includes: obtaining the order number of an unpaid order, and obtaining order information corresponding to the unpaid order based on the order number of the unpaid order; extracting the logistics status of the unpaid order based on the order information, screening out the unpaid orders with the logistics status of being delivered, and marking them as orders to be processed; regularly obtaining the payment status and courier status information of the orders to be processed. If the payment status of the order to be processed is unpaid, input the order information and courier status information of the order to be processed into a trained multi-layer perceptron model, and obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model; generating a reminder message based on the reminder priority level and reminder method corresponding to the order to be processed, and pushing the reminder message to the courier; when the payment status of the order to be processed changes from unpaid to paid, marking the order to be processed with the payment status of paid as a completed order, and regularly collecting the order numbers and corresponding collection information of the completed orders to summarize and generate a settlement report.

[0030] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and drawings of the present invention are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "including" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0031] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to Figure 1 , the first embodiment of an order payment management method in the embodiments of the present invention includes:

[0032] S101. Obtain the order number of an unpaid order, and obtain order information corresponding to the unpaid order based on the order number of the unpaid order.

[0033] It can be understood that the execution subject of the present invention can be an order payment management device, or a terminal or a server. Specifically, no limitation is made here. The embodiments of the present invention are described by taking the server as the execution subject as an example.

[0034] In this embodiment, through the order management system, all orders that have not completed payment are screened out, and the order numbers of these orders are obtained. Using the order numbers as indexes, the detailed order information corresponding to these orders that have not completed payment is queried and obtained from the database, including but not limited to product information, buyer information, logistics information, etc.

[0035] S102. Extract the logistics status of the orders that have not completed payment based on the order information, screen out the orders that have not completed payment with the logistics status of "being delivered", and mark them as orders to be processed.

[0036] In this embodiment, the order information covers a large number of dimensions of data, and the logistics status is one of the key pieces of information. The logistics status value of each order that has not completed payment is extracted and stored in a temporary data structure (such as a list, dictionary, or database table) together with its order information. Traverse the order information in the temporary data structure and check whether the logistics status of each order is "being delivered".

[0037] For the orders that have not completed payment with the logistics status of "being delivered", mark them as orders to be processed.

[0038] S103. Regularly obtain the payment status and courier status information of the orders to be processed. If the payment status of the order to be processed is unpaid, input the order information and courier status information of the order to be processed into the trained multi-layer perceptron model, and obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model.

[0039] In this embodiment, a timer is set to regularly (such as every 30 minutes or every hour) check the payment status of the orders to be processed and the status information of the couriers responsible for delivery (such as location, delivery progress, etc.).

[0040] In this embodiment, when the payment status of the order to be processed is unpaid, the order information (such as order amount, order type, etc.) and courier status information are used as inputs and input into the trained multi-layer perceptron model. The model outputs the reminder priority level (such as high, medium, low) and reminder method (such as sound reminder, pop-up reminder, etc.) corresponding to the order to be processed according to the patterns learned during training.

[0041] S104. Generate a reminder message based on the reminder priority level and reminder method corresponding to the order to be processed, and push the reminder message to the courier.

[0042] In this embodiment, reminder message templates corresponding to different reminder priority levels and reminder methods are predefined. For example, for high-priority sound reminders. According to the reminder method, select an appropriate push channel to push the reminder message to the courier. If it is a sound reminder, a voice message can be sent through the mobile application used by the courier; if it is a pop-up reminder, a reminder window will pop up on the application interface.

[0043] S105. When the payment status of the order to be processed changes from unpaid to paid, mark the order to be processed with a payment status of paid as a completed order, and regularly collect the order numbers of the completed orders and the corresponding collection information to summarize and generate a settlement statement.

[0044] In this embodiment, once it is monitored that the payment status of the order to be processed changes from "unpaid" to "paid", immediately update the payment status field of the order to be processed in the database and mark it as a completed order.

[0045] In this embodiment, at fixed intervals, such as every day, collect information such as the numbers, collection amounts, and collection times of the completed orders from the database. Summarize and statistically analyze the collected information to generate a settlement statement, which is convenient for the enterprise to conduct financial management and data analysis and understand the inflow of funds.

[0046] The order payment management method provided in this embodiment can effectively identify and process orders with a logistics status of being delivered but not yet paid, use a multi-layer perceptron model to intelligently judge the reminder priority level and method, and timely notify the courier to pay attention. This not only improves the automation and intelligence level of order processing, but also ensures the timeliness and accuracy of commodity distribution. At the same time, regularly summarize the information of completed orders to generate a settlement statement, which is convenient for financial management and data analysis, and overall improves the operation efficiency and optimizes the customer experience.

[0047] Please refer to Figure 2 , the second embodiment of the order payment management method in the embodiment of the present invention includes:

[0048] S201. Batch obtain the order numbers of the orders with unpaid payments, and construct an SQL query statement based on the order numbers of the orders with unpaid payments.

[0049] In this embodiment, generally speaking, there will be a dedicated field in the order table to record the payment status of the order, such as the "payment status" field, which may have different values such as "paid", "unpaid", and "paying". The database query language (such as SQL) can be used to batch obtain the order numbers of the orders with unpaid payments.

[0050] S202. Execute the SQL query statement to obtain the order information corresponding to the orders with unpaid payments.

[0051] In this embodiment, a query statement is executed using a database link.

[0052] S203. Convert the order information corresponding to the unpaid order into a dictionary form and store it in a pre-constructed order information list.

[0053] In this embodiment, after obtaining the order information corresponding to the unpaid order from the database, this information is usually presented in the form of a table, where each row represents an order and each column represents an order attribute. To facilitate subsequent processing and use, it is necessary to convert this tabular data into a dictionary form.

[0054] A dictionary is a very flexible data structure that can use the order attribute names as keys and the attribute values as values, and store the order information in the form of key-value pairs. For example, for an order, attribute names such as "order number", "order amount", "order type", etc. can be used as keys, and the corresponding specific values can be used as values to construct a dictionary object.

[0055] In this embodiment, traverse each row of order information returned from the database and convert each row of data into a dictionary.

[0056] In this embodiment, by batch obtaining the order numbers of unpaid orders and constructing an SQL query statement to directly obtain the detailed information of these orders, the data retrieval efficiency is effectively improved. At the same time, the order information is converted into a dictionary form and stored in a list, which is convenient for subsequent data processing and analysis, and overall improves the automation and intelligence level of unpaid order management.

[0057] Please refer to Figure 3 , the third embodiment of an order payment management method in the embodiments of the present invention includes:

[0058] S301. Traverse the order information of each unpaid order in the order information list and determine whether there is a logistics status field.

[0059] In this embodiment, access the order information list, traverse this list, and check the payment status of each order. If the payment status of the order shows "unpaid", then proceed to the next step of processing. For each unpaid order, further check whether its order information contains a logistics status field. The logistics status field is usually an identifier indicating the current logistics stage of the order (such as shipped, in transit, signed for, etc.).

[0060] If this field exists, then proceed to the next step; if it does not exist, skip this order and continue to process the next one.

[0061] S302. If so, add the value of the logistics status field and the corresponding unpaid order to the logistics status information list.

[0062] In this embodiment, after confirming that there is a logistics status field in the information of an unpaid order, the value of this field and the corresponding order information are extracted and added to a dedicated logistics status information list. This list serves the purpose of data integration and screening preparation, gathering all unpaid orders containing logistics status information together. For example, the value of the logistics status field in the order information may be "collected", "in transit", "being delivered", "signed for", etc. After adding these values and the corresponding order information to the logistics status information list, it is convenient to perform screening based on the specific logistics status later.

[0063] S303. Screen out the unpaid orders corresponding to the "being delivered" value in the logistics status field from the logistics status information list, and mark the screened unpaid orders as orders to be processed.

[0064] In this embodiment, for each screened order, a marking operation is performed to update its status to "to be processed", which involves updating the order status field in the database or moving the order information to another data structure or queue dedicated to processing such orders.

[0065] In this embodiment, by systematically traversing the order information list, checking the logistics status of each unpaid order, and screening out those orders with the logistics status of "being delivered" for special processing (marking as orders to be processed), precise management and timely response to the order status are achieved.

[0066] Please refer to Figure 4 , the fourth embodiment of an order payment management method in the embodiments of the present invention includes:

[0067] S401. Construct and train a multi-layer perceptron model to obtain a trained multi-layer perceptron model.

[0068] In this embodiment, constructing and training a multi-layer perceptron model to obtain a trained multi-layer perceptron model specifically includes: using a deep learning framework to construct a multi-layer perceptron model. The multi-layer perceptron model includes an input layer, a hidden layer, and an output layer. The input layer is used to receive the order information and courier status information of the order to be processed. The hidden layer is used to judge the reminder priority level and reminder method corresponding to the order to be processed according to the information output by the input layer, and output the judgment result through the output layer; collect historical order data, where the historical order data includes the order type, payment amount, courier busyness level, distance between the courier's current location and the recipient's location, as well as the reminder priority level and reminder method of the historical order; use the historical order data to train the multi-layer perceptron model, and optimize the parameters of the multi-layer perceptron model using forward propagation and backpropagation during the training process to obtain a trained multi-layer perceptron model.

[0069] In this embodiment, a multi-layer perceptron model is constructed using, for example, TensorFlow or PyTorch. The hidden layer utilizes multiple neurons and a complex connection structure to perform a non-linear transformation based on the input information, thereby intelligently determining the reminder priority level and reminder method corresponding to the order to be processed. The output layer is responsible for presenting the judgment result of the hidden layer in a clear and definite manner.

[0070] In this embodiment, the training process is divided into two stages: forward propagation and backward propagation. In the forward propagation stage, the model calculates the prediction result of the output layer through the non-linear transformation of the hidden layer based on the input historical order data. Then, the prediction result is compared with the actual reminder priority level and reminder method, and the loss function value is calculated. In the backward propagation stage, optimization algorithms such as gradient descent are used to update the parameters of the model according to the loss function value to reduce the gap between the prediction result and the actual result. This process is iterated continuously until the performance of the model reaches the preset standard or converges.

[0071] S402. Regularly obtain the payment status and courier status information of the order to be processed.

[0072] In this embodiment, the payment status of the order to be processed includes unpaid and paid, and the courier status information includes the courier's busy degree and the distance between the courier's current location and the recipient's location. The courier's busy degree includes slightly busy, moderately busy, and highly busy.

[0073] S403. If the payment status of the order to be processed is unpaid, input the order information and courier status information of the order to be processed into the trained multi-layer perceptron model to obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model.

[0074] In this embodiment, assume there is an order to be processed, and the order information includes:

[0075] Order type: Fresh food

[0076] Payment amount: 200 yuan

[0077] Estimated delivery time: Before 4 pm;

[0078] The courier status information includes:

[0079] Courier's busy degree: Moderate (currently 5 orders have been received and all can be delivered within the specified time)

[0080] Distance between the courier's current location and the recipient's location: 10 kilometers (estimated driving time 20 minutes);

[0081] Taking the above order information and courier status information as inputs, they are passed to the trained multi-layer perceptron model. The model first preprocesses this information, such as converting text descriptions into numerical features, converting distances and times into relative values or normalized values, etc. Then, based on the learned weights and bias values, it performs a non-linear transformation on the input information to extract features related to the reminder priority level and reminder method. After a series of calculations, the model will finally output one or more reminder priority levels and reminder methods corresponding to the orders to be processed. For example, in this case, the model outputs: the reminder priority level is high, and the reminder methods are text message reminder and APP push reminder simultaneously. The reminder priority level is high because the order type is fresh food and needs to be delivered as soon as possible to maintain freshness. The reminder methods are text message reminder and APP push reminder simultaneously because the current busyness level of the courier is medium and the distance from the recipient's location is far, and multiple reminder methods are needed to ensure that the courier will not forget or delay.

[0082] In this embodiment, a multi-layer perceptron model is constructed and trained using a deep learning framework. This model can receive the order information of the order to be processed and the courier status information as inputs, and through the processing of the hidden layer, it can intelligently judge and output the reminder priority level and reminder method corresponding to the order to be processed, ensuring the timeliness and effectiveness of the reminder and greatly optimizing the work process of the courier.

[0083] Please refer to Figure 5 , the fifth embodiment of an order payment management method in the embodiments of the present invention includes:

[0084] S501. Pre-build reminder message generation templates corresponding to the reminder priority level and reminder method, and classify and store the reminder message generation templates in a template library.

[0085] In this embodiment, each template should contain necessary placeholders for subsequent insertion of specific order information. For example, for the text message reminder template, it can be designed as follows: "

Order Reminder

[0086] In this embodiment, the templates are classified and stored according to the reminder priority level (such as high, medium, low) and reminder method (such as text message, APP push, phone call, etc.). In this way, the corresponding template can be quickly found according to the specific reminder priority level and reminder method during subsequent calls.

[0087] S502. Based on the reminder priority level and reminder method corresponding to the order to be processed, call the corresponding reminder message generation template from the template library.

[0088] In this embodiment, the reminder priority level and reminder method are extracted from the output of the model. Then, based on this information, the corresponding reminder message generation template is found in the template library.

[0089] S503. Fill the order information of the order to be processed into the called reminder message generation template to generate a reminder message, and push the reminder message to the courier.

[0090] In this embodiment, after the information filling is completed, the template will be converted into a complete reminder message. This message contains all necessary order information and reminder information to ensure that the courier can clearly understand the situation of the order to be processed. Finally, according to the reminder method (such as SMS, APP push, etc.), the generated reminder message is pushed to the courier.

[0091] In this embodiment, by intelligently judging the reminder priority level and reminder method of the order to be processed, pre-constructing the corresponding reminder message generation template, filling the order information accurately into the template and then pushing it to the courier, the timeliness and accuracy of order processing are effectively improved, ensuring that the courier can respond in a timely manner and give priority to processing important orders, thereby improving customer satisfaction and logistics operation efficiency.

[0092] Please refer to Figure 6 , the sixth embodiment of an order payment management method in the embodiments of the present invention includes:

[0093] S601. When the payment status of the order to be processed changes from unpaid to paid, mark the order to be processed with a payment status of paid as a completed order.

[0094] In this embodiment, the payment status of the order to be processed is continuously monitored. When it is detected that the payment status of a certain order to be processed changes from "unpaid" to "paid", it means that the payment process of this order has been completed, and then the order to be processed with a payment status of paid is marked as a completed order.

[0095] S602. Regularly collect the order numbers and corresponding collection information of the completed orders, and use a pre-trained convolutional neural network model to classify the completed orders into normal orders and abnormal orders.

[0096] In this embodiment, data of completed orders is collected according to a set time period, such as every day. The collected content mainly includes the order numbers of completed orders and the corresponding collection information, and the collection information covers key data such as the collection amount and collection time. After the collection is completed, a pre-trained convolutional neural network model is used to further analyze these completed orders. The convolutional neural network classifies the completed orders into normal orders and abnormal orders according to various features of the orders, such as the fluctuation of the order amount, the pattern of the payment time, and the historical payment behavior of the customers. Normal orders represent transactions that conform to the regular business process and expectations, while abnormal orders may contain some situations that require special attention, such as the abnormal payment time of large orders, frequent small payments in a short period, etc.

[0097] Suppose there is a customer who usually makes one or two small payments per month to buy some daily necessities. However, on a certain day, he suddenly made more than ten small payments, and the amount of each payment was slightly different. This behavior pattern is significantly different from his historical payment behavior.

[0098] When using the convolutional neural network to analyze the orders of this customer, the model will identify this abnormal payment behavior and classify the customer's orders as abnormal orders, thus triggering the risk warning mechanism.

[0099] S603. Summarize normal orders and abnormal orders respectively and generate a settlement statement.

[0100] In this embodiment, after the order classification is completed, normal orders and abnormal orders are summarized and processed respectively. For normal orders, data such as the order quantity, total collection amount, and average order amount will be counted; for abnormal orders, in addition to basic statistics, abnormal features and relevant information will be recorded in detail. Then, a settlement statement is generated according to the summarized data.

[0101] In this embodiment, through the timely marking of payment status change orders, regular data collection, scientific order classification, and finally generating a settlement statement, the integrity and systematicness of the order payment management process are realized. On the one hand, timely marking of completed orders ensures the accuracy of order status management and provides a reliable basis for subsequent data processing; on the other hand, using the convolutional neural network model to classify order types can quickly identify abnormal orders, helping enterprises to discover potential risks in a timely manner, such as fraudulent transactions, abnormal payment patterns, etc., to ensure the safety of enterprise funds. At the same time, summarizing and generating settlement statements respectively provides clear data support for enterprise financial analysis.

[0102] The order payment management method in the embodiment of the present invention has been described above. Next, the device in the embodiment of the present invention will be described. Please refer to Figure 7, the implementation manner of the order payment management device in the embodiments of the present invention includes:

[0103] An acquisition module 701, configured to acquire the order number of an unpaid order, and acquire the order information corresponding to the unpaid order based on the order number of the unpaid order;

[0104] A marking module 702, configured to extract the logistics status of the unpaid order based on the order information, screen out the unpaid orders with the logistics status of being dispatched, and mark them as orders to be processed;

[0105] A judgment module 703, configured to periodically acquire the payment status and the courier status information of the order to be processed. If the payment status of the order to be processed is unpaid, input the order information and the courier status information of the order to be processed into a trained multi-layer perceptron model, and acquire the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model;

[0106] A push module 704, configured to generate a reminder message based on the reminder priority level and reminder method corresponding to the order to be processed, and push the reminder message to the courier;

[0107] A settlement module 705, configured to, when the payment status of the order to be processed changes from unpaid to paid, mark the order to be processed with the payment status of paid as a completed order, and periodically collect the order numbers and corresponding collection information of the completed orders to summarize and generate a settlement statement.

[0108] In this embodiment, the acquisition module 701 includes: a first acquisition unit 7011, configured to batch acquire the order numbers of unpaid orders, and construct an SQL query statement based on the order numbers of the unpaid orders; a second acquisition unit 7012, configured to execute the SQL query statement to acquire the order information corresponding to the unpaid orders; a conversion unit 7013, configured to convert the order information corresponding to the unpaid orders into a dictionary form and store it in a pre-constructed order information list.

[0109] In this embodiment, the marking module 702 includes: a first judgment unit 7021, configured to traverse the order information of each unpaid order in the order information list to judge whether there is a logistics status field; an adding unit 7022, configured to, when there is a logistics status field, add the value of the logistics status field and the corresponding unpaid order to the logistics status information list; a first marking unit 7023, configured to screen out the unpaid orders corresponding to the value of the logistics status field in the logistics status information list being dispatched, and mark the screened unpaid orders as orders to be processed.

[0110] In this embodiment, the judgment module 703 includes: a training unit 7031, configured to construct and train a multi-layer perceptron model to obtain a trained multi-layer perceptron model; a third acquisition unit 7032, configured to periodically acquire the payment status and courier status information of the order to be processed, where the payment status of the order to be processed includes unpaid and paid, and the courier status information includes the courier's busyness level and the distance between the courier's current location and the recipient's location; a second judgment unit 7033, configured to, when the payment status of the order to be processed is unpaid, input the order information and courier status information of the order to be processed into the trained multi-layer perceptron model, and obtain the reminder priority level and reminder method corresponding to the order to be processed output by the trained multi-layer perceptron model.

[0111] In this embodiment, the push module 704 includes: a construction unit 7041, configured to pre-construct a reminder message generation template corresponding to the reminder priority level and reminder method, and classify and store the reminder message generation template in a template library; a generation unit 7042, configured to, based on the reminder priority level and reminder method corresponding to the order to be processed, call the corresponding reminder message generation template from the template library; a push unit 7043, configured to fill the order information of the order to be processed into the called reminder message generation template to generate a reminder message, and push the reminder message to the courier.

[0112] In this embodiment, the settlement module 705 includes: a second marking unit 7051, configured to, when the payment status of the order to be processed changes from unpaid to paid, mark the order to be processed with a payment status of paid as a completed order; a division unit 7052, configured to periodically collect the order numbers and corresponding collection information of the completed orders, and use a pre-trained convolutional neural network model to divide the completed orders into normal orders and abnormal orders; a settlement unit 7053, configured to summarize the normal orders and the abnormal orders respectively, and generate a settlement report.

[0113] In this embodiment, it can effectively identify and process orders with a logistics status of being dispatched but not yet paid, use a multi-layer perceptron model to intelligently judge the reminder priority level and method, and timely notify the courier to pay attention, which not only improves the automation and intelligence level of order processing, but also ensures the timeliness and accuracy of commodity distribution. At the same time, it periodically summarizes the completed order information to generate a settlement report, which is convenient for financial management and data analysis, and overall improves the operation efficiency and optimizes the customer experience.

[0114] Figure 7 The structure of the order payment management device shown does not limit the order payment management device, and can implement the steps of the order payment management method provided by the above method embodiments.

[0115] Above Figure 7The order payment management device in the embodiments of the present invention will be described in detail from the perspective of modular functional entities. Next, the order payment management device in the embodiments of the present invention will be described in detail from the perspective of hardware processing.

[0116] Figure 8 FIG. 4 is a schematic structural diagram of an order payment management device provided by an embodiment of the present invention. The device 800 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 810 (for example, one or more processors) and a memory 820, and one or more storage media 830 for storing application programs 833 or data 832 (for example, one or more mass storage devices). Among them, the memory 820 and the storage media 830 may be transient storage or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the device 800. Further, the processor 810 may be configured to communicate with the storage media 830 and execute a series of instruction operations in the storage media on the device 800.

[0117] The device 800 may further include one or more power supplies 840, one or more wired or wireless network interfaces 850, one or more input / output interfaces 860, and / or one or more operating systems 831, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on.

[0118] The embodiments of the present invention further provide a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium, or may also be a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the order payment management method.

[0119] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, or units can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0120] When 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, in essence, or the part that contributes to the prior art, or all 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 and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0121] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An order payment management method, characterized in that: The order payment management method comprises: Obtaining an order number of an unfinished payment order, and obtaining order information corresponding to the unfinished payment order based on the order number of the unfinished payment order; Extract the logistics status of unfinished payment orders based on order information, filter out unfinished payment orders with a logistics status of being delivered, and mark them as pending orders; Periodically obtain the payment status and courier status information of the pending order; if the payment status of the pending order is unpaid, input the order information and courier status information of the pending order into the trained multi-layer perceptron model, and obtain the reminder priority level and reminder method corresponding to the pending order output by the trained multi-layer perceptron model; Generate a reminder message based on the reminder priority level and reminder method corresponding to the pending order, and push the reminder message to the courier; When the payment status of the pending order changes from unpaid to paid, the pending order with the payment status of paid is marked as a completed order, and the order number and corresponding payment information of the completed order are regularly collected to generate a settlement report.

2. The order payment management method according to claim 1, characterized in that: The obtaining the order number of the unfinished payment order, and obtaining the order information corresponding to the unfinished payment order based on the order number of the unfinished payment order, includes: Batch obtain the order numbers of the unfinished payment orders, and construct an SQL query statement based on the order numbers of the unfinished payment orders; Execute an SQL query statement to obtain order information corresponding to the unfinished payment order; The order information corresponding to the unfinished payment order is converted into a dictionary form and stored in a pre-built order information list.

3. The order payment management method according to claim 2, characterized in that: The obtaining of the logistics status of the unfinished payment orders, and filtering out the unfinished payment orders whose logistics status is being delivered, and marking them as pending orders, includes: Traverse the order information of each uncompleted payment order in the order information list to determine whether there is a logistics status field; If yes, then add the value of the logistics status field and the corresponding unfinished payment order to the logistics status information list; The value of the logistics status field and the unfinished payment orders corresponding to the delivery are filtered out from the logistics status information list, and the filtered out unfinished payment orders are marked as pending orders.

4. The order payment management method according to claim 1, characterized in that: The step of periodically obtaining the payment status and courier status information of the pending order, and if the payment status of the pending order is unpaid, inputting the order information and courier status information of the pending order into a trained multi-layer perceptron model, and obtaining the reminder priority level and reminder method corresponding to the pending order output by the trained multi-layer perceptron model, includes: Construct and train a multi-layer perceptron model to obtain a trained multi-layer perceptron model; Obtaining the payment status and courier status information of the pending order at regular intervals, wherein the payment status of the pending order includes unpaid and paid, and the courier status information includes the courier's busyness and the distance between the courier's current location and the recipient's location; If the payment status of the pending order is unpaid, the order information and courier status information of the pending order are input into the trained multi-layer perceptron model to obtain the reminder priority level and reminder method corresponding to the pending order output by the trained multi-layer perceptron model.

5. The order payment management method according to claim 4, characterized in that: The step of constructing and training a multi-layer perceptron model to obtain a trained multi-layer perceptron model includes: A multi-layer perceptron model is constructed using a deep learning framework, wherein the multi-layer perceptron model includes an input layer, a hidden layer, and an output layer, wherein the input layer is used to receive order information and courier status information of the order to be processed, and the hidden layer is used to determine the reminder priority level and reminder method corresponding to the order to be processed according to the information output by the input layer, and output the judgment result through the output layer; Collect historical order data, including order type, payment amount, courier busyness, distance between the courier's current location and the recipient's location, and reminder priority and reminder method; The multi-layer perceptron model is trained using the historical order data, and the parameters of the multi-layer perceptron model are optimized using forward propagation and back propagation during the training process to obtain a trained multi-layer perceptron model.

6. The order payment management method according to claim 1, characterized in that: The generating a reminder message based on the reminder priority level and reminder method corresponding to the pending order, and pushing the reminder message to the courier, comprises: Pre-build reminder message generation templates corresponding to reminder priority levels and reminder methods, and classify and save the reminder message generation templates in a template library; Based on the reminder priority level and reminder method corresponding to the pending order, call a corresponding reminder message generation template from the template library; Fill the order information of the pending order into the called reminder message generation template to generate a reminder message, and push the reminder message to the courier.

7. The order payment management method according to claim 1, characterized in that: When the payment status of the pending order changes from unpaid to paid, the pending order with the payment status of paid is marked as a completed order, and the order number and corresponding payment information of the completed order are regularly collected to generate a settlement report, including: When the payment status of the pending order changes from unpaid to paid, the pending order with the payment status of paid is marked as a completed order; Regularly collect the order numbers and corresponding payment information of the completed orders, and use a pre-trained convolutional neural network model to classify the completed orders into normal orders and abnormal orders; The normal orders and the abnormal orders are summarized respectively to generate a settlement report.

8. An order payment management device, characterized in that: include: An acquisition module, used to acquire an order number of an unfinished payment order, and acquire order information corresponding to the unfinished payment order based on the order number of the unfinished payment order; A marking module is used to extract the logistics status of unfinished payment orders based on order information, filter out unfinished payment orders whose logistics status is being delivered, and mark them as pending orders; A judgment module, used for periodically obtaining the payment status and courier status information of the pending order; if the payment status of the pending order is unpaid, inputting the order information and courier status information of the pending order into the trained multi-layer perceptron model, and obtaining the reminder priority level and reminder method corresponding to the pending order output by the trained multi-layer perceptron model; A push module, configured to generate a reminder message based on the reminder priority level and reminder method corresponding to the pending order, and push the reminder message to the courier; The settlement module is used to mark the pending orders with the payment status of paid as completed orders when the payment status of the pending orders changes from unpaid to paid, and regularly collect the order numbers and corresponding payment information of the completed orders to summarize and generate settlement reports.

9. An order payment management device, characterized in that: comprising a memory and at least one processor, wherein the memory has computer-readable instructions stored therein; The at least one processor calls the computer-readable instructions in the memory to execute the various steps of the order payment management method as described in any one of claims 1-7.

10. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the steps of the order payment management method as described in any one of claims 1 to 7 are implemented.

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