Method, device and equipment for customizing billing and delivery in logistics industry and storage medium

Abstract

The invention relates to the technical field of logistics, in particular to a logistics industry customized billing and delivery method, device and equipment and a storage medium. The logistics industry customized billing and delivery method comprises the following steps of obtaining a bar code image, and adopting an identification model to identify the bar code image so as to obtain order base information; selecting a billing template according to the order issuing information, and filling key information in the order issuing information into the billing template to generate customized billing; and auditing the customized billing, and generating a task according to the customized billing passing the auditing. According to the invention, through automatic customized billing, billing efficiency can be effectively improved, and increase of labor cost is reduced, so that customized billing service can be popularized to all customer groups, and customer satisfaction is improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a method, device, equipment and storage medium for customized billing and shipment in the logistics industry. Background Art

[0002] With the intensification of market competition and the diversification of consumer preferences, the logistics industry has gradually shifted from traditional standardized services to personalized and customized services. Customers are no longer satisfied with single, standardized logistics services, but hope to obtain more accurate and efficient logistics solutions based on their specific needs.

[0003] However, it is difficult for current logistics and express delivery companies to provide customized services that customers want. If customized services are required for each customer, a large amount of human resources will be required to cooperate, which will greatly increase labor costs and reduce delivery efficiency. Therefore, existing logistics and express delivery companies can only provide customized services for a small number of customers. It can be seen that the existing technology still needs to be improved and enhanced. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a customized order issuance and shipment method, device, equipment and storage medium for the logistics industry, aiming to solve the technical problems of high labor costs and low delivery efficiency in the prior art of logistics express companies providing customized services.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides a customized invoice and shipment method for the logistics industry, comprising the following steps: obtaining a barcode image, using a recognition model to identify the barcode image to obtain order form information; selecting an invoice template based on the order form information, and filling the key information in the order form information into the invoice template to generate a customized invoice; reviewing the customized invoice, and generating a task based on the customized invoice that has passed the review.

[0007] Optionally, in a first implementation method of the first aspect of the present invention, the acquiring of a barcode image and the use of a recognition model to recognize the barcode image to obtain order receipt information specifically include: acquiring historical image data, using a convolutional neural network as an initial model, and training the initial model with historical image data to obtain a recognition model; acquiring a barcode image, recognizing the barcode image with a recognition model to obtain barcode data, and acquiring order receipt information based on the barcode data; and using natural language processing technology to parse the barcode image to obtain supplementary text information, and adding the supplementary text information to the order receipt information.

[0008] Optionally, in a second implementation method of the first aspect of the present invention, selecting an invoice template based on order form information, and filling key information in the order form information into the invoice template to generate a customized invoice, specifically includes: obtaining order form information, extracting key information in the order form information, wherein the key information includes goods information and customer information; using a preference prediction model to predict customer preferences based on the key information, obtaining preference data, and selecting an invoice template based on the goods information and the preference data; filling key information in the order form information into the invoice template to generate a customized invoice.

[0009] Optionally, in a third implementation of the first aspect of the present invention, the preference prediction model is used to predict the customer's preference based on key information to obtain preference data, and the invoice template is selected based on the goods information and the preference data, specifically including: using a preference prediction model to predict the customer's preference based on key information to obtain preference data; obtaining user opinions, the user opinions including user information, reference modules and module types, and constructing multiple invoice templates based on user opinions; using a decision tree or rule engine to select an invoice template based on goods attributes and preference data.

[0010] Optionally, in a fourth implementation method of the first aspect of the present invention, the step of filling the key information in the order form information into the invoice template to generate a customized invoice specifically includes: using natural language generation technology to create descriptive text based on the key information in the order form information; matching the descriptive text with the fields in the invoice template to fill the descriptive text into the invoice template to generate a customized invoice; classifying the customized invoice, using a type prediction model to predict the particularity of the customized invoice, and marking the customized invoice with particularity.

[0011] Optionally, in a fifth implementation method of the first aspect of the present invention, the customized invoice is reviewed and a task is generated based on the reviewed customized invoice, specifically including: obtaining historical order data, and establishing an anomaly detection algorithm based on the historical order data; using the anomaly detection algorithm to review the customized invoice, marking and outputting abnormal data points in the customized invoice to obtain a customized invoice that meets the review requirements; obtaining deployable resource data, using a task allocation model, and generating tasks based on the deployable resource data and the customized invoice.

[0012] Optionally, in a sixth implementation method of the first aspect of the present invention, the acquiring of deployable resource data, the adoption of a task allocation model, and the generation of tasks based on the deployable resource data and customized order opening specifically include: acquiring historical transportation data, the historical transportation data including transportation routes and transportation times, using a reinforcement learning model as an initial model, and training with historical transportation data to obtain a task allocation model; acquiring deployable resource data, the deployable resource data including deployable transportation capacity data of the affiliated outlets and deployable transportation capacity data of nearby outlets, and the adoption of a task allocation model to generate tasks based on the deployable resource data and customized order opening; acquiring the estimated delivery time and transportation time in the task, acquiring the required delivery time for customized order opening, setting a priority for the task based on the estimated delivery time, transportation time, and required delivery time, and sorting all tasks according to the priority.

[0013] The second aspect of the present invention provides a customized invoice and delivery device for the logistics industry, including: an identification module, used to obtain a barcode image, and use a recognition model to identify the barcode image to obtain order form information; a generation module, used to select an invoice template according to the order form information, and fill the key information in the order form information into the invoice template to generate a customized invoice; an audit module, used to audit the customized invoice, and generate a task based on the customized invoice that has passed the audit.

[0014] Optionally, in a first implementation method of the second aspect of the present invention, the recognition module includes: a first construction unit, used to acquire historical image data, using a convolutional neural network as an initial model, and using historical image data to train the initial model to obtain a recognition model; a recognition unit, used to acquire a barcode image, using the recognition model to recognize the barcode image to obtain barcode data, and obtaining order form information based on the barcode data; and a supplement unit, used to parse the barcode image using natural language processing technology to obtain supplementary text information, and add the supplementary text information to the order form information.

[0015] Optionally, in a second implementation method of the second aspect of the present invention, the generation module includes: an acquisition sub-module, used to acquire order form information and extract key information in the order form information, wherein the key information includes goods information and customer information; a prediction sub-module, used to adopt a preference prediction model to predict customer preferences based on key information, obtain preference data, and select an invoice template based on the goods information and preference data; and a filling sub-module, used to fill the key information in the order form information into the invoice template to generate a customized invoice.

[0016] Optionally, in a third implementation of the second aspect of the present invention, the prediction submodule includes: a prediction unit, used to adopt a preference prediction model to predict customer preferences based on key information and obtain preference data; a second construction unit, used to obtain user opinions, wherein the user opinions include user information, reference modules and module types, and a plurality of invoice templates are constructed according to user opinions; a selection unit, used to adopt a decision tree or a rule engine to select an invoice template according to goods attributes and preference data.

[0017] Optionally, in a fourth implementation method of the second aspect of the present invention, the filling submodule includes: a creation unit, used to use natural language generation technology to create descriptive text according to key information in the order form information; a matching unit, used to match the descriptive text with the fields in the invoice template to fill the descriptive text into the invoice template to generate a customized invoice; a classification unit, used to classify the customized invoice, use a type prediction model to predict the particularity of the customized invoice, and mark the customized invoice with particularity.

[0018] Optionally, in a fifth implementation of the second aspect of the present invention, the audit module includes: an establishment sub-module for obtaining historical order data and establishing an anomaly detection algorithm based on the historical order data; an audit sub-module for using an anomaly detection algorithm to audit customized invoices, marking and outputting abnormal data points in customized invoices to obtain customized invoices that meet the audit requirements; and a generation sub-module for obtaining deployable resource data and using a task allocation model to generate tasks based on the deployable resource data and customized invoices.

[0019] Optionally, in a sixth implementation of the second aspect of the present invention, the generation submodule includes: a third construction unit, used to obtain historical transportation data, the historical transportation data including transportation routes and transportation time, using a reinforcement learning model as the initial model, and training with historical transportation data to obtain a task allocation model; a generation unit, used to obtain deployable resource data, the deployable resource data including deployable capacity data of the affiliated outlets and deployable capacity data of nearby outlets, using a task allocation model to generate tasks based on the deployable resource data and customized ordering; a sorting unit, used to obtain the estimated delivery time and transportation time in the task, obtain the required delivery time for customized ordering, set the priority of the task based on the estimated delivery time, transportation time and required delivery time, and sort all tasks according to the priority.

[0020] The third aspect of the present invention provides a customized billing and shipping device for the logistics industry, comprising a memory and at least one processor, wherein the memory stores computer-readable instructions; the at least one processor calls the computer-readable instructions in the memory to execute the various steps of the customized billing and shipping method for the logistics industry as described above.

[0021] A fourth aspect of the present invention provides a computer-readable storage medium having computer-readable instructions stored thereon, which, when executed by a processor, implement the various steps of the customized billing and shipping method for the logistics industry as described above.

[0022] Beneficial effects: The present invention provides a customized billing and shipping method for the logistics industry, which first obtains a barcode image, uses a recognition model to identify the barcode image, and obtains order form information, and quickly and automatically obtains the barcode information from the barcode image to obtain the order form information, thereby avoiding order entry errors and accelerating information acquisition efficiency, and then automatically selects a billing template according to the order form information to automatically select a billing template suitable for the customer, thereby carrying out customized services, and then fills the key information in the order form information into the billing template to generate a customized bill, reducing manual information entry, and finally auditing the customized bill, and generating tasks based on the audited customized bill. The present invention can effectively reduce labor costs and improve delivery efficiency by automatically generating customized bills that meet customer needs, and can be extended to serve all customers and increase customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A first flow chart of a customized billing and shipping method for the logistics industry provided by an embodiment of the present invention;

[0024] Figure 2 A second flow chart of a customized billing and shipping method for the logistics industry provided by an embodiment of the present invention;

[0025] Figure 3 A third flow chart of a customized billing and shipping method for the logistics industry provided by an embodiment of the present invention;

[0026] Figure 4 A fourth flow chart of a customized billing and shipping method for the logistics industry provided by an embodiment of the present invention;

[0027] Figure 5 A fifth flow chart of a customized billing and shipping method for the logistics industry provided by an embodiment of the present invention;

[0028] Figure 6 A sixth flow chart of a customized billing and shipping method for the logistics industry provided by an embodiment of the present invention;

[0029] Figure 7 A seventh flow chart of a customized billing and shipping method for the logistics industry provided by an embodiment of the present invention;

[0030] Figure 8 A schematic diagram of the structure of a customized billing and dispatching device for the logistics industry provided by an embodiment of the present invention;

[0031] Fig. 9 Another structural schematic diagram of a customized billing and dispatching device for the logistics industry provided by an embodiment of the present invention;

[0032] Fig.10 A schematic structural diagram of a customized billing and dispatching device for the logistics industry provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention provides a method, device, equipment and storage medium for customized billing and delivery in the logistics industry. The present invention first obtains a barcode image, and uses a recognition model to identify the barcode image to obtain order form information; then selects a billing template according to the order form information, and fills the key information in the order form information into the billing template to generate a customized bill; finally, the customized bill is reviewed, and based on the customized bill generation task that has passed the review, the present invention provides logistics companies with a customer customized service model with high delivery efficiency and high degree of automation, which can help companies achieve differentiated competition and enhance brand image and market share. At the same time, by providing personalized services, logistics companies can also better meet customer needs, enhance customer stickiness, and lay a solid foundation for the long-term development of the company.

[0034] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] For ease of understanding, the specific process of the embodiment of the present invention is described below. It should be noted that the relevant steps of obtaining user personal information in the present invention are all performed with the user's authorization.

[0036] See also Figure 1The first embodiment of the customized billing and shipping method for the logistics industry in the embodiment of the present invention includes:

[0037] S101. Obtain a barcode image and use a recognition model to recognize the barcode image to obtain order form information;

[0038] By automatically identifying and parsing the barcode information in the barcode image through the recognition model, the time and errors of manual input can be reduced, and the order information can be quickly entered and processed. When manually entering order information, errors or omissions are inevitable, and barcode recognition technology can significantly reduce the probability of these human errors through automated processing. In addition, the recognition model has a high recognition accuracy and can accurately recognize various types of barcodes, including one-dimensional barcodes and two-dimensional barcodes.

[0039] S102. Select an invoice template based on the order form information, and fill the key information in the order form information into the invoice template to generate a customized invoice;

[0040] Different orders may have different billing requirements. Customized billing templates can be adjusted and optimized according to the specific order form information to meet the personalized needs of customers. By selecting a suitable billing template and automatically filling in the key information in the order form, the time for manual entry and verification can be greatly reduced, and work efficiency can be improved. Automatic filling reduces the possibility of human input errors and ensures the accuracy of billing information. Specifically, billing templates can be set in advance. Using billing templates can ensure that all billing documents have a relatively uniform format and style, improve the professionalism and readability of the documents, and the template design usually follows industry standards and specifications to help ensure that the billing process complies with relevant regulations.

[0041] S103. Review the customized invoice and generate a task based on the customized invoice that has passed the review.

[0042] Auditing the generated customized billing can ensure the accuracy and completeness of the information. Specifically, anomaly detection algorithms can be introduced to automatically identify any potential errors or omissions, establish normal patterns based on historical data, and mark data points that do not meet expectations.

[0043] In specific scenarios, when the system reviews customized orders, it can include the following contents: 1. Verify whether the cargo volume and weight are required and meet the requirements, otherwise it will prompt "Please enter the weight / volume first"; 2. Verify whether the number of digits in the order number meets the number of digits in the logistics company's order number, otherwise it will prompt "Not our company's express delivery number"; 3. The interface verifies whether the order number exists, if not, it returns "Order number does not exist"; 4. The interface verifies whether the order number is for an ordered order; 5. The interface verifies whether the order number is for the current outlet, if not, it returns "Not the current outlet order".

[0044] The above-mentioned customized billing and shipping method for the logistics industry provided by the present invention can be implemented through customized scanning equipment and software systems, so as to realize fast and accurate scanning and identification of goods, reduce manual operations, and improve delivery efficiency. The application of automation and intelligent technology can also greatly reduce human errors, such as order errors, lost or damaged goods, and improve overall logistics efficiency. The customized customer billing and shipping service can also allow customers to customize the billing and shipping processes according to their own needs, thereby improving overall logistics efficiency. This customer-centric service concept helps to improve customer satisfaction and loyalty. In addition, the present invention can reduce labor costs and error costs, so that logistics companies can optimize their cost structure and improve profitability.

[0045] See also Figure 2 The second embodiment of the customized billing and shipping method for the logistics industry in the embodiment of the present invention includes:

[0046] S201. Obtain historical image data, use a convolutional neural network as an initial model, and train the initial model using the historical image data to obtain a recognition model;

[0047] The recognition model obtained by training the convolutional neural network (CNN) with a large number of barcode images can effectively deal with various changes in barcodes, such as tilt, blur, etc. In order to improve the accuracy of the recognition model, when acquiring historical image data, multiple different types of barcode images can be added to the training set, so that the recognition model can deal with barcode images in various complex conditions.

[0048] S202. Obtain a barcode image, use a recognition model to recognize the barcode image to obtain barcode data, and obtain order form information based on the barcode data;

[0049] Specifically, after the recognition model recognizes the barcode data from the barcode image, the barcode data will be further decoded. After successful decoding, the system can send a request to the backend server through the RESTful API interface to obtain the order receipt information.

[0050] In order to improve the security of logistics information, some order numbers will be encrypted; different types of business may also use order numbers with different digits for easy distinction. When obtaining the barcode data of the barcode image, some order numbers need to be decoded to obtain data that meets the requirements of the backend server.

[0051] S203. Analyze the barcode image using natural language processing technology to obtain supplementary text information, and add the supplementary text information to the order form information.

[0052] In some special cases, the captured barcode image may contain accompanying handwritten notes or unstructured text information, which may be important information that affects logistics services. In this embodiment, natural language processing technology is used to parse the barcode image to obtain supplementary text information, which can effectively avoid the omission of important information and can be automatically added to the order form information without the need for additional manual operations, thereby effectively improving operational efficiency.

[0053] See also Figure 3 The third embodiment of the customized billing and shipping method for the logistics industry in the embodiment of the present invention includes:

[0054] S301. Obtain order form information and extract key information from the order form information, wherein the key information includes goods information and customer information;

[0055] Specifically, the cargo information includes but is not limited to cargo weight, total number of cargo pieces, cargo type, cargo volume, cargo packaging, etc.; customer information includes but is not limited to customer name, customer industry, customer scale, etc.;

[0056] S302. Use a preference prediction model to predict customer preferences based on key information, obtain preference data, and select an order template based on the cargo information and preference data;

[0057] Through the preference prediction model, logistics companies can more accurately grasp customer preferences and needs. Based on these preference data, the system can quickly select the billing template that best meets customer expectations, thereby providing a more personalized experience during the service process. This personalized service can significantly improve customer satisfaction and loyalty. In addition, with the continuous accumulation of data and continuous optimization of the model, the ability of the preference prediction model will continue to improve, which will enable companies to more accurately grasp market dynamics and changes in customer needs, providing a strong guarantee for the company's continuous improvement and innovation.

[0058] S303. Fill the key information in the order form into the invoice template to generate a customized invoice.

[0059] Predefined billing templates can avoid tedious formatting and information entry every time a bill is issued. Employees can focus more on checking and confirming key information, further improving work efficiency and accuracy.

[0060] See also Figure 4 The fourth embodiment of the customized billing and shipping method for the logistics industry in the embodiments of the present invention includes:

[0061] S401. Use a preference prediction model to predict customer preferences based on key information and obtain preference data;

[0062] S402. Obtain user opinions, including user information, reference modules and module types, and construct multiple billing templates based on user opinions;

[0063] By establishing a billing template library based on preference data, enterprises can automate the billing process. Employees only need to select the appropriate template based on the customer's preference information and fill in the necessary key information to quickly generate billing documents that meet customer expectations. This greatly shortens billing time and improves work efficiency.

[0064] In addition, since the billing template is provided based on user opinions, the billing template built in advance is more in line with customer expectations. In particular, customers in different industries may have different needs and expectations. For example, they may choose a template with additional packaging instructions for fragile items. By obtaining user opinions and then building billing templates based on user opinions, the billing templates formed will be more targeted, greatly improve customer satisfaction, and reduce the number of subsequent billing template modifications.

[0065] S403. Use a decision tree or rule engine to select an invoice template based on cargo attributes and preference data.

[0066] Both the decision tree and the rule engine can accurately match the specific attributes of the goods with the customer's preference data, thereby selecting the billing template that best meets the customer's needs. This personalized service can significantly improve customer satisfaction and loyalty. The decision tree and rule engine can flexibly adjust the selection strategy of the billing template according to the real-time changes in the goods attributes and preference data. This helps companies better adapt to changes in market demand, optimize resource allocation, and improve market competitiveness. The traditional billing method may lead to errors due to human negligence or misunderstanding. The decision tree and rule engine effectively reduce the occurrence of human errors and improve the accuracy of billing through strict logical judgment and data processing.

[0067] See also Figure 5 The fifth embodiment of the customized billing and shipping method for the logistics industry in the embodiment of the present invention includes:

[0068] S501. Use natural language generation technology to create descriptive text based on key information in the order form information;

[0069] In the process of filling in the template, using natural language generation (NLG) technology to automatically create descriptive text can ensure that the billing content is both professional and clear. Among the key information obtained, there may be some grammatical errors, incorrect grammatical structures or incorrect vocabulary choices. Natural language generation technology can avoid the above errors and reduce manual modification of incorrect text.

[0070] S502. Match the descriptive text with the fields in the billing template to fill the descriptive text into the billing template to generate a customized bill;

[0071] S503. Classify the customized invoices, use a type prediction model to predict the particularity of the customized invoices, and mark the customized invoices with particularity.

[0072] The classification algorithm in machine learning can predict which orders are more likely to require special handling based on historical data, so that preparations can be made in advance. By marking customized orders with special features, delivery personnel and outlets can be reminded to make corresponding preparations and effectively avoid omissions. For example, the transportation of fresh goods needs to maintain a low temperature environment, or a transportation vehicle with a constant temperature function needs to be used according to customer requirements. These types of customized orders are special. After marking, the system can assign these customized orders to outlets with service experience or service capabilities, and the outlets can also allocate transportation resources in advance according to the marking to avoid delivery delays.

[0073] See also Figure 6 The sixth embodiment of the customized billing and shipping method for the logistics industry in the embodiment of the present invention includes:

[0074] S601. Obtain historical order data, and establish an anomaly detection algorithm based on the historical order data;

[0075] S602. Use anomaly detection algorithms to review customized billing, mark and output abnormal data points in customized billing, and obtain customized billing that meets the review requirements;

[0076] After opening an order, multiple pieces of information need to be verified, such as whether the volume and weight are entered, whether they meet the input requirements, whether the order number is correct, whether necessary information is missing, etc. If manual inspection is used one by one, it will require a lot of manpower costs, and the efficiency of manual inspection is low and the error rate is high. Especially for customized orders with different templates, the error rate of manual review will be higher. The anomaly detection algorithm allows the system to automatically complete the review, mark abnormal data points, and remind the salesperson to make modifications, thereby improving the review efficiency.

[0077] S603. Acquire the deployable resource data, adopt the task allocation model, and generate tasks according to the deployable resource data and customized billing.

[0078] For example, after a customized order is generated, the system obtains data on available resources. For example, based on current resource availability and past performance data, the task allocation model can recommend the best delivery route or priority sorting, thereby improving overall operational efficiency.

[0079] See also Figure 7 The seventh embodiment of the customized billing and shipping method for the logistics industry in the embodiments of the present invention includes:

[0080] S701. Obtain historical transportation data, the historical transportation data including transportation routes and transportation time, use the reinforcement learning model as the initial model, and use the historical transportation data for training to obtain a task allocation model;

[0081] S702. Acquire the deployable resource data, which includes the deployable capacity data of the affiliated outlets and the deployable capacity data of nearby outlets, and use the task allocation model to generate tasks based on the deployable resource data and customized order opening;

[0082] By collecting and analyzing the data on available resources, including the type, quantity, and status of current transportation capacity, the task allocation model can more accurately match task requirements with resource supply, ensuring that each task can receive the most appropriate transportation capacity support. In addition, the task allocation model can also avoid idle or overused transportation capacity due to uneven resource allocation or poor information flow, thereby improving the overall resource utilization efficiency. When a new task is generated, the system can quickly generate a task allocation plan based on the available resource data and notify relevant outlets to execute it, shortening the task response and execution time. Through the task allocation model, possible resource conflicts can also be foreseen, and scheduling and adjustments can be made in advance to ensure the smooth execution of tasks.

[0083] S703. Obtain the estimated delivery time and transportation time in the task, obtain the required delivery time for customized ordering, set the priority of the task according to the estimated delivery time, transportation time and required delivery time, and sort all tasks according to the priority.

[0084] By establishing task priorities, logistics companies can flexibly adjust transportation resources based on task priority sorting, give priority to tasks that are time-sensitive or have higher requirements, and ensure priority processing of important customers or urgent orders, thereby improving on-time delivery rates and meeting customer expectations. In addition, by setting task priorities and sorting, the entire transportation process can be optimized, waiting time and unnecessary delays can be reduced, and overall operational efficiency can be improved.

[0085] The above describes the customized billing and sending method for the logistics industry in the embodiment of the present invention. The following describes the customized billing and sending device for the logistics industry in the embodiment of the present invention. Figure 8 In one embodiment of the present invention, a customized billing and sending device for the logistics industry includes:

[0086] The recognition module 10 is used to obtain a barcode image and recognize the barcode image using a recognition model to obtain order form information;

[0087] The generating module 20 is used to select an invoice template according to the order form information, and fill the key information in the order form information into the invoice template to generate a customized invoice;

[0088] The review module 30 is used to review the customized billing and generate tasks according to the customized billing that has passed the review.

[0089] See also Fig. 9 In one embodiment of the present invention, a customized billing and sending device for the logistics industry includes:

[0090] The recognition module 10 is used to obtain a barcode image and recognize the barcode image using a recognition model to obtain order form information;

[0091] The generating module 20 is used to select an invoice template according to the order form information, and fill the key information in the order form information into the invoice template to generate a customized invoice;

[0092] An audit module 30 is used to audit the customized billing and generate tasks according to the customized billing that has passed the audit;

[0093] In this embodiment, the identification module 10 includes:

[0094] A first construction unit 11 is used to obtain historical image data, use a convolutional neural network as an initial model, and use the historical image data to train the initial model to obtain a recognition model;

[0095] The recognition unit 12 is used to obtain a barcode image, recognize the barcode image using a recognition model to obtain barcode data, and obtain order form information according to the barcode data;

[0096] A supplementing unit 13 is used to parse the barcode image using natural language processing technology to obtain supplementary text information, and add the supplementary text information to the order form information;

[0097] In this embodiment, the generating module 20 includes:

[0098] The acquisition submodule 21 is used to acquire order form information and extract key information from the order form information, wherein the key information includes goods information and customer information;

[0099] The prediction submodule 22 is used to use a preference prediction model to predict the customer's preference based on key information, obtain preference data, and select an order template based on the goods information and the preference data;

[0100] The filling submodule 23 is used to fill the key information in the order form information into the billing template to generate a customized billing;

[0101] In this embodiment, the prediction submodule 22 includes:

[0102] The prediction unit 221 is used to use a preference prediction model to predict the customer's preference according to the key information and obtain preference data;

[0103] The second construction unit 222 is used to obtain user opinions, wherein the user opinions include user information, reference modules and module types, and construct multiple billing templates according to the user opinions;

[0104] A selection unit 223, for selecting a billing template according to the cargo attributes and preference data by using a decision tree or a rule engine;

[0105] In this embodiment, the filling submodule 23 includes:

[0106] A creation unit 231 is used to create a descriptive text according to key information in the order form information by using natural language generation technology;

[0107] A matching unit 232, configured to match the descriptive text with a field in the billing template, so as to fill the descriptive text into the billing template and generate a customized billing;

[0108] A classification unit 233 is used to classify the customized billing, predict the particularity of the customized billing using a type prediction model, and mark the customized billing with particularity;

[0109] In this embodiment, the audit module 30 includes:

[0110] Establishing a submodule 31 for acquiring historical order data and establishing an anomaly detection algorithm based on the historical order data;

[0111] The review submodule 32 is used to review the customized billing using an anomaly detection algorithm, mark and output abnormal data points in the customized billing to obtain a customized billing that meets the review requirements;

[0112] A generation submodule 33 is used to obtain the deployable resource data, adopt the task allocation model, and generate tasks according to the deployable resource data and customized billing;

[0113] In this embodiment, the generating submodule 33 includes:

[0114] The third construction unit 331 is used to obtain historical transportation data, wherein the historical transportation data includes transportation routes and transportation time, and use the reinforcement learning model as the initial model and the historical transportation data for training to obtain a task allocation model;

[0115] A generating unit 332 is used to obtain the deployable resource data, wherein the deployable resource data includes the deployable transport capacity data of the affiliated outlet and the deployable transport capacity data of nearby outlets, and to generate tasks according to the deployable resource data and customized billing by using a task allocation model;

[0116] The sorting unit 333 is used to obtain the estimated delivery time and transportation time in the task, obtain the required delivery time for customized orders, set the priority of the task according to the estimated delivery time, transportation time and required delivery time, and sort all tasks according to the priority.

[0117] The customized billing and dispatching device for the logistics industry of the present invention provides customers with personalized, efficient and precise logistics services, helping logistics companies to better meet customer needs, improve customer satisfaction and profitability. In addition, the customized billing and dispatching device for the logistics industry has a high degree of automation, can generate corresponding billing templates and automatically fill in according to customer preferences, can effectively reduce manual operations and human errors, has high efficiency during operation, not only does it not increase delivery time, but also can optimize distribution services, improve distribution efficiency, and can be fully promoted to serve all customers.

[0118] The above is a detailed description of the customized billing and sending device for the logistics industry in an embodiment of the present invention from the perspective of modular functional entities. The following is a detailed description of the customized billing and sending device for the logistics industry in an embodiment of the present invention from the perspective of hardware processing.

[0119] Fig.10 A schematic diagram of the structure of a customized billing and sending device for the logistics industry provided in an embodiment of the present invention. The customized billing and sending device 900 for the logistics industry may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 910 (for example, one or more processors) and a memory 920, and one or more storage media 930 (for example, one or more mass storage devices) storing application programs 933 or data 932. Among them, the memory 920 and the storage medium 930 can be temporary storage or permanent storage. The program stored in the storage medium 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the customized billing and sending device 900 for the logistics industry. Furthermore, the processor 910 can be configured to communicate with the storage medium 930, and execute a series of instruction operations in the storage medium 930 on the customized billing and sending device 900 for the logistics industry, so as to implement the steps of the customized billing and sending method for the logistics industry provided in the above-mentioned method embodiments.

[0120] The customized billing and dispatching device 900 for the logistics industry may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input and output interfaces 960, and / or one or more operating systems 931, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Fig.10 The structure of the customized billing and dispatching equipment for the logistics industry shown does not constitute a limitation on the customized billing and dispatching equipment for the logistics industry, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0121] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of a customized order issuance and shipment method for the logistics industry.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the equipment or device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0123] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or 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, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0124] It is understandable that those skilled in the art can make equivalent substitutions or changes based on the technical solution and inventive concept of the present invention, and all these changes or substitutions should fall within the protection scope of the claims attached to the present invention.

Claims

1. A customized billing and shipping method for the logistics industry, characterized in that: The steps include: Obtain a barcode image and use a recognition model to recognize the barcode image to obtain order form information; Select an invoice template based on the order form information, and fill in the key information in the order form information into the invoice template to generate a customized invoice; The customized invoice is reviewed, and a task is generated according to the customized invoice that has passed the review.

2. The customized billing and delivery method for the logistics industry according to claim 1 is characterized in that: The obtaining of the barcode image and the use of the recognition model to recognize the barcode image to obtain the order form information specifically include: Obtain historical image data, use a convolutional neural network as an initial model, and use the historical image data to train the initial model to obtain a recognition model; Obtain a barcode image, use a recognition model to recognize the barcode image to obtain barcode data, and obtain order form information based on the barcode data; The barcode image is parsed using natural language processing technology to obtain supplementary text information, and the supplementary text information is added to the order form information.

3. The customized billing and delivery method for the logistics industry according to claim 1 is characterized in that: The process of selecting an invoice template based on the order form information and filling the key information in the order form information into the invoice template to generate a customized invoice specifically includes: Obtain order form information and extract key information from the order form information, wherein the key information includes goods information and customer information; Use the preference prediction model to predict customer preferences based on key information, obtain preference data, and select the billing template based on the cargo information and preference data; Fill the key information in the order form into the invoice template to generate a customized invoice.

4. The customized billing and delivery method for the logistics industry according to claim 3 is characterized in that: The preference prediction model is used to predict the customer's preference based on key information, obtain preference data, and select an order template based on the goods information and preference data, specifically including: Use the preference prediction model to predict customer preferences based on key information and obtain preference data; Obtaining user opinions, including user information, reference modules and module types, and constructing multiple billing templates based on the user opinions; Use decision trees or rule engines to select billing templates based on cargo attributes and preference data.

5. The customized billing and delivery method for the logistics industry according to claim 3 is characterized in that: Filling the key information in the order form information into the billing template to generate a customized billing specifically includes: Use natural language generation technology to create descriptive text based on key information in the order form; Matching the descriptive text with a field in the billing template to fill the descriptive text into the billing template and generate a customized bill; The customized invoices are classified, the particularity of the customized invoices is predicted using a type prediction model, and the customized invoices with particularity are marked.

6. The customized billing and delivery method for the logistics industry according to claim 1 is characterized in that: The review of the customized billing and the generation of tasks according to the reviewed customized billing specifically include: Acquire historical order data, and establish an anomaly detection algorithm based on the historical order data; Adopt anomaly detection algorithms to review customized billing, mark and output abnormal data points in customized billing, and obtain customized billing that meets the review requirements; The deployable resource data is obtained, and a task allocation model is adopted to generate tasks according to the deployable resource data and customized billing.

7. The customized billing and delivery method for the logistics industry according to claim 6 is characterized in that: The step of obtaining the deployable resource data and using the task allocation model to generate tasks according to the deployable resource data and customized billing specifically includes: Acquire historical transportation data, wherein the historical transportation data includes transportation routes and transportation time, use a reinforcement learning model as an initial model, and use the historical transportation data for training to obtain a task allocation model; Acquire the deployable resource data, which includes the deployable transport capacity data of the affiliated outlets and the deployable transport capacity data of nearby outlets, and use the task allocation model to generate tasks according to the deployable resource data and customized billing; Get the estimated delivery time and transportation time in the task, get the required delivery time for customized orders, set the priority of the task based on the estimated delivery time, transportation time and required delivery time, and sort all tasks according to the priority.

8. A customized billing and delivery device for the logistics industry, characterized in that: include: The recognition module is used to obtain the barcode image and use the recognition model to recognize the barcode image to obtain the order form information; The generation module is used to select an invoice template based on the order form information, and fill the key information in the order form information into the invoice template to generate a customized invoice; The review module is used to review the customized invoice and generate tasks according to the customized invoice that has passed the review.

9. A customized billing and delivery device for the logistics industry, 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 each step of the customized billing and shipping method for the logistics industry 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 the processor, the various steps of the customized billing and shipping method for the logistics industry as described in any one of claims 1-7 are implemented.

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