Delivery service business form generation method and system

Abstract

The invention relates to the technical field of data processing and management, in particular to a delivery service business form generation method and system. By introducing the steps of demand analysis, data extraction and processing, machine learning classification and the like, automatic generation of the delivery service form is realized. According to the automatic process, manual intervention is remarkably reduced, the data processing speed and accuracy are improved, it is ensured that the form meeting the requirement can be rapidly and efficiently generated, and the timeliness requirement of service operation is met; through the automatic form generation and data processing flow, the dependence on manual operation is remarkably reduced, and the problem that the efficiency of the traditional manual form filling and auditing process is low is solved. According to the method, through the steps of demand analysis, data source configuration, data extraction, data preprocessing, automatic form generation and the like, delivery service business demands can be quickly responded, the response time of delivery service is shortened, and the overall working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and management, and in particular to a method and system for generating a delivery service business form. Background Art

[0002] In the modern financial and logistics industries, the accuracy and efficiency of delivery services are crucial. Delivery services involve multiple links, including the actual delivery of goods, warehouse management, and the conversion of futures and spot goods. In order to support this complex business process, companies usually need to generate and manage a large number of delivery service business forms. These forms not only contain key delivery information, such as delivery number, delivery date, stock code, transaction price, and delivery quantity, but also need to ensure the accuracy and completeness of the data.

[0003] However, existing technologies face multiple challenges in the generation and management of delivery service business forms:

[0004] First, the generation of delivery service business forms under existing technologies often relies on manual labor, and the management process is inefficient: manual form filling and long review processes limit the rapid response capabilities of the business.

[0005] Secondly, the data in the delivery service business forms under existing technologies are complex and difficult to integrate: the existing management system does not provide sufficient support for diverse data sources, resulting in inefficient data extraction and processing.

[0006] In view of the above problems, it is necessary to propose a method and system for generating a delivery service business form. Summary of the invention

[0007] The purpose of the present invention is to solve the problems existing in the background technology and to propose a method and system for generating a delivery service business form.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] In a first aspect, the present invention provides a method for generating a delivery service business form, comprising the following steps:

[0010] A method for generating a delivery service business form comprises the following steps:

[0011] Step 1: Demand analysis and form template design;

[0012] Conduct demand analysis on the delivery service business and confirm the field requirements of various delivery service business forms required in the business process.

[0013] Get the delivery service type corresponding to the delivery service business form, including physical delivery, warehouse delivery, factory delivery and EFP.

[0014] As a preferred embodiment of the present invention, the field requirements in the delivery service business form are determined according to the acquired delivery service type.

[0015] The field requirements include field name, data type, field length, and whether it is required.

[0016] The field names include delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method.

[0017] Each field name corresponds to a preset importance factor, which provides a quantitative basis for the subsequent delivery service business form layout.

[0018] Each field name corresponds to a preset display space, ensuring that the field name, input box, and prompt information can be displayed clearly and avoid crowding or excessive blank space.

[0019] The data types include character string String, numerical value Number and date Date.

[0020] The field length described is the maximum number of characters for a string type field.

[0021] The mandatory or not refers to whether a certain field is a mandatory item in the delivery service business form.

[0022] Determine the field requirements in the delivery service business form based on the acquired delivery service type.

[0023] Subsequently, a form template is designed according to the results of the demand analysis, and the form fields are arranged according to the importance factor of each field name. The arrangement rule is that the field name with the highest importance factor is arranged in the first column of the delivery service business form, the field name with the second highest importance factor is arranged in the second column of the delivery service business form, and so on.

[0024] Configure the width of each column according to the preset display space.

[0025] Step 2: Data source configuration and data extraction;

[0026] Determine the data source and data source parameters of the delivery service business form.

[0027] The data source parameters include database type and link information.

[0028] The connection information includes database name DatabaseName, service name ServiceName, host name Hostname, port number PortNumber, user name Username and password Password.

[0029] According to the field requirements in the form template, call the preset data extraction script to extract the required data from the data source.

[0030] Step 3: Data preprocessing and field matching;

[0031] Perform data cleaning and field matching on the extracted data, remove duplicate parts in the captured data, and match the required data extracted from the data source with the preset field names.

[0032] The data cleaning refers to deduplication processing of the extracted data.

[0033] The field matching is implemented based on a support vector machine model.

[0034] The training and application process of the support vector machine is as follows:

[0035] S301, data preparation;

[0036] Define the data set: The data set extracted from the data source is recorded as D={d1, d2, ..., dn}. Each sample di contains multiple feature values, representing the contents of different fields;

[0037] S302: Text feature extraction: for each field sample di, extract text features, including string length L(di) and TF-IDF vectorized feature value. ; where i=1, 2, ..., n.

[0038] Construct a character vector for each field: .in is the field name to which field di belongs, including delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; is the attribution label, indicating whether the field di belongs to the current field name. If so, The value of is 1; otherwise it is 0.

[0039] S303, establishing a support vector machine model;

[0040] The extracted features are trained using a support vector machine model. The goal is to find an optimal hyperplane that maximizes the interval between samples.

[0041] The specific support vector machine model is: , where w is the weight vector; b is the bias term; C is the penalty parameter, which controls the tolerance for incorrect classification; is a slack variable used to handle nonlinear separable situations.

[0042] Set constraints: ;in is a feature mapping function, which is used to transform the input feature vector Mapping to a high-dimensional space.

[0043] S304, model training;

[0044] Assume a set of predefined fields, F = {f1, f2, ..., fn} as training samples, where each field fi has a feature vector: Among them are the field names that field fi actually belongs to and The value of is 1; F = {f1, f2, ..., fn} is used as training samples and input into the support vector machine model for training.

[0045] S305, application of training results;

[0046] After the support vector machine model is trained, the trained support vector machine model is used to predict new data. For each new sample, it is input into the trained support vector machine model to obtain its matching field name.

[0047] Step 4: Form generation and rendering;

[0048] Generate a specific form instance based on the form template and data. Use front-end technology to render the form.

[0049] Step 5: Form validation and error handling;

[0050] Process duplicate values ​​in the generated delivery service business form and merge duplicate rows. Define the unique identifier for each delivery as unique_id=TradeDate+SecurityCode+Buyername+SellerName. Among them, TradeDate is the hash value of the delivery date field; SecurityCode is the hash value of the delivery number field; Buyername is the hash value of the seller name field; SellerName is the hash value of the seller name field.

[0051] Subsequently, duplicate records are detected based on uniqueness and rows with the same unique identifier are merged in the delivery service business form.

[0052] Verify the generated delivery service business form to ensure the integrity and accuracy of the data. For data that fails verification, perform error processing and prompt the user to make corrections.

[0053] The specific process is: Check each field to see if there is an empty string or null. If so, highlight the field and output an error.

[0054] Step 6: Form storage and distribution;

[0055] The generated delivery service business form is stored in the database and file system for subsequent business processing. At the same time, the delivery service business form is distributed to relevant personnel or systems to ensure the smooth progress of the business process.

[0056] The form distribution is implemented through mail and message queue.

[0057] In a second aspect, the present invention provides a delivery service business form generation system, including a template design module, a database, a machine learning module, a field matching module, a verification and error handling module, and a storage and distribution module.

[0058] The template design module conducts demand analysis on the delivery service business and confirms the field requirements of various delivery service business forms required in the business process.

[0059] Get the delivery service type corresponding to the delivery service business form, including physical delivery, warehouse delivery, factory delivery and EFP.

[0060] As a preferred embodiment of the present invention, the field requirements in the delivery service business form are determined according to the acquired delivery service type.

[0061] The field requirements include field name, data type, field length, and whether it is required.

[0062] The field names include delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method.

[0063] Each field name corresponds to a preset importance factor, which provides a quantitative basis for the subsequent delivery service business form layout.

[0064] Each field name corresponds to a preset display space, ensuring that the field name, input box, and prompt information can be displayed clearly and avoid crowding or excessive blank space.

[0065] The data types include character string String, numerical value Number and date Date.

[0066] The field length described is the maximum number of characters for a string type field.

[0067] The mandatory or not refers to whether a certain field is a mandatory item in the delivery service business form.

[0068] Determine the field requirements in the delivery service business form based on the acquired delivery service type.

[0069] Subsequently, a form template is designed according to the results of the demand analysis, and the form fields are arranged according to the importance factor of each field name. The arrangement rule is that the field name with the highest importance factor is arranged in the first column of the delivery service business form, the field name with the second highest importance factor is arranged in the second column of the delivery service business form, and so on.

[0070] The database is the storage center for delivery business information and the data source for delivery service business forms.

[0071] The database includes several sub-databases, each of which includes MySQL, Oracle, RDBMS and NoSQL

[0072] Each sub-database has a unique matching database name DatabaseName, service name ServiceName, host name Hostname, port number PortNumber, user name Username and password Password.

[0073] According to the field requirements in the form template, call the preset data extraction script to extract the required data from the data source.

[0074] The machine learning module builds a support vector machine model for field name matching and performs model training.

[0075] Define the data set, and record the data set extracted from the data source as D={d1, d2, ..., dn}. Each sample di contains multiple feature values, representing the contents of different fields;

[0076] For each field sample di, extract text features, including string length L(di) and TF-IDF vectorized feature value ; where i=1, 2, ..., n.

[0077] Construct a character vector for each field: .in is the field name to which field di belongs, including delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; is the attribution label, indicating whether the field di belongs to the current field name. If so, The value of is 1; otherwise it is 0.

[0078] The extracted features are trained using a support vector machine model. The goal is to find an optimal hyperplane that maximizes the interval between samples.

[0079] The specific support vector machine model is: , where w is the weight vector; b is the bias term; C is the penalty parameter, which controls the tolerance for incorrect classification; is a slack variable used to handle nonlinear separable situations.

[0080] Set constraints: ;in is a feature mapping function, which is used to transform the input feature vector Mapping to a high-dimensional space.

[0081] Assume a set of predefined fields, F = {f1, f2, ..., fn} as training samples, where each field fi has a feature vector: Among them are the field names that field fi actually belongs to and The value of is 1; F = {f1, f2, ..., fn} is used as training samples and input into the support vector machine model for training.

[0082] The field matching module retrieves the training results of the machine learning module, matches the fields extracted from the database to the preset field names one by one, and outputs the field names corresponding to each field. .

[0083] The validation and error handling module processes duplicate values ​​in the generated delivery service business form and merges duplicate rows. Define the unique identifier unique_id = TradeDate + SecurityCode + Buyername + SellerName for each delivery. Among them, TradeDate is the hash value of the delivery date field; SecurityCode is the hash value of the delivery number field; Buyername is the hash value of the seller name field; SellerName is the hash value of the seller name field.

[0084] Subsequently, duplicate records are detected based on uniqueness and rows with the same unique identifier are merged in the delivery service business form.

[0085] Verify the generated delivery service business form to ensure the integrity and accuracy of the data. For data that fails verification, perform error processing and prompt the user to make corrections.

[0086] The specific process is: Check each field to see if there is an empty string or null. If so, highlight the field and output an error.

[0087] The storage and distribution module stores the generated delivery service business form in the database and file system for subsequent business processing. At the same time, the delivery service business form is distributed to relevant personnel or systems to ensure the smooth progress of the business process.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] 1. The present invention realizes the automatic generation of delivery service business forms by introducing steps such as demand analysis, data extraction and processing, and machine learning classification. This automated process significantly reduces manual intervention, improves the speed and accuracy of data processing, and ensures that forms that meet the needs can be generated quickly and efficiently to meet the timeliness requirements of business operations;

[0090] 2. The present invention clarifies the field requirements, data source configuration and field matching, ensuring the standardization and consistency of the generated form. The setting of field name, data type and whether it is required makes the form clear and comprehensive in structure, reduces the risk of errors caused by non-standard data, and enhances the integrity and accuracy of the data;

[0091] 3. The present invention significantly reduces the reliance on manual operations through automated form generation and data processing processes, and solves the problem of low efficiency in traditional manual form filling and review processes. The method can quickly respond to delivery service business needs, shorten the response time of delivery services, and improve overall work efficiency through steps such as demand analysis, data source configuration, data extraction, data preprocessing, and automatic form generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0093] Figure 1 is a flow chart of the method of the present invention;

[0094] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0095] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0096] See also Figure 1 As shown, a method for generating a delivery service business form includes the following steps:

[0097] Step 1: Demand analysis and form template design;

[0098] Conduct demand analysis on the delivery service business and confirm the field requirements of various delivery service business forms required in the business process.

[0099] Get the delivery service type corresponding to the delivery service business form, including physical delivery, warehouse delivery, factory delivery and EFP.

[0100] Furthermore, the field requirements in the delivery service business form are determined according to the acquired delivery service type.

[0101] The field requirements include field name, data type, field length, and whether it is required.

[0102] The field names include delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method.

[0103] Each field name corresponds to a preset importance factor, which provides a quantitative basis for the subsequent delivery service business form layout.

[0104] Each field name corresponds to a preset display space, ensuring that the field name, input box, and prompt information can be displayed clearly and avoid crowding or excessive blank space.

[0105] The data types include character string String, numerical value Number and date Date.

[0106] The field length described is the maximum number of characters for a string type field.

[0107] The mandatory or not refers to whether a certain field is a mandatory item in the delivery service business form.

[0108] It should be noted that the field name is the unique identifier of each field in the delivery service business form and is used to distinguish different fields.

[0109] Determine the field requirements in the delivery service business form based on the acquired delivery service type.

[0110] Subsequently, a form template is designed according to the results of the demand analysis, and the form fields are arranged according to the importance factor of each field name. The arrangement rule is that the field name with the highest importance factor is arranged in the first column of the delivery service business form, the field name with the second highest importance factor is arranged in the second column of the delivery service business form, and so on.

[0111] Configure the width of each column according to the preset display space.

[0112] Step 2: Data source configuration and data extraction;

[0113] Determine the data source and data source parameters of the delivery service business form.

[0114] The data source parameters include database type and link information.

[0115] The database types include MySQL, Oracle, RDBMS and NoSQL.

[0116] The connection information includes database name DatabaseName, service name ServiceName, host name Hostname, port number PortNumber, user name Username and password Password.

[0117] According to the field requirements in the form template, call the preset data extraction script to extract the required data from the data source.

[0118] Step 3: Data preprocessing and field matching;

[0119] Perform data cleaning and field matching on the extracted data, remove duplicate parts in the captured data, and match the required data extracted from the data source with the preset field names.

[0120] The specific process of data cleaning is as follows:

[0121] By formula The extracted data is deduplicated, where unique is the deduplication function. D is the set of all extracted data; D' is the set of duplicate data, d is a field element in the data set; d' is a duplicate field element.

[0122] The specific process of field matching is as follows:

[0123] S301, data preparation;

[0124] Define the data set: The data set extracted from the data source is recorded as D={d1, d2, ..., dn}. Each sample di contains multiple feature values, representing the contents of different fields;

[0125] S302: Text feature extraction: for each field sample di, extract text features, including string length L(di) and TF-IDF vectorized feature value. ; where i=1, 2, ..., n.

[0126] Construct a character vector for each field: .in is the field name to which field di belongs, including delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; is the attribution label, indicating whether the field di belongs to the current field name. If so, The value of is 1; otherwise it is 0.

[0127] S303, establishing a support vector machine model;

[0128] The extracted features are trained using a support vector machine model. The goal is to find an optimal hyperplane that maximizes the interval between samples.

[0129] The specific support vector machine model is: , where w is the weight vector; b is the bias term; C is the penalty parameter, which controls the tolerance for incorrect classification; is a slack variable used to handle nonlinear separable situations.

[0130] Set constraints: ;in is a feature mapping function, which is used to transform the input feature vector Mapping to a high-dimensional space.

[0131] S304, model training;

[0132] Assume a set of predefined fields, F = {f1, f2, ..., fn} as training samples, where each field fi has a feature vector: Among them are the field names that field fi actually belongs to and The value of is 1; F = {f1, f2, ..., fn} is used as training samples and input into the support vector machine model for training.

[0133] S305, application of training results;

[0134] After the support vector machine model is trained, the trained support vector machine model is used to predict new data. For each new sample, it is input into the trained support vector machine model to obtain its matching field name.

[0135] It should be noted that the basic goal of the support vector machine model is to find a hyperplane that can maximize the interval and classify the fields to identify which field name they belong to. The purpose of this process is to use machine learning to identify which type of information a field belongs to, including delivery number, delivery date, stock code, transaction price, delivery quantity, buyer name, seller name, and delivery method, to achieve automated field matching and classification, reduce manual intervention, and improve the speed and efficiency of data processing.

[0136] Step 4: Form generation and rendering;

[0137] Generate a specific form instance based on the form template and data. Use front-end technology to render the form.

[0138] The template engine is used to combine the form template and data to generate a form instance in HTML format. The Bootstrap front-end framework is used for style design to improve the aesthetics and usability of the form.

[0139] Step 5: Form validation and error handling;

[0140] Process duplicate values ​​in the generated delivery service business form and merge duplicate rows. Define the unique identifier for each delivery as unique_id=TradeDate+SecurityCode+Buyername+SellerName. Among them, TradeDate is the hash value of the delivery date field; SecurityCode is the hash value of the delivery number field; Buyername is the hash value of the seller name field; SellerName is the hash value of the seller name field.

[0141] Subsequently, duplicate records are detected based on uniqueness and rows with the same unique identifier are merged in the delivery service business form.

[0142] Verify the generated delivery service business form to ensure the integrity and accuracy of the data. For data that fails verification, perform error processing and prompt the user to make corrections.

[0143] The specific process is: Check each field to see if there is an empty string or null. If so, highlight the field and output an error.

[0144] Step 6: Form storage and distribution;

[0145] The generated delivery service business form is stored in the database and file system for subsequent business processing. At the same time, the delivery service business form is distributed to relevant personnel or systems to ensure the smooth progress of the business process.

[0146] The form distribution is implemented through mail and message queue.

[0147] See also Figure 2 As shown, a delivery service business form generation system includes a template design module, a database, a machine learning module, a field matching module, a verification and error handling module, and a storage distribution module.

[0148] The template design module conducts demand analysis on the delivery service business and confirms the field requirements of various delivery service business forms required in the business process.

[0149] Get the delivery service type corresponding to the delivery service business form, including physical delivery, warehouse delivery, factory delivery and EFP.

[0150] Furthermore, the field requirements in the delivery service business form are determined according to the acquired delivery service type.

[0151] The field requirements include field name, data type, field length, and whether it is required.

[0152] The field names include delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method.

[0153] Each field name corresponds to a preset importance factor, which provides a quantitative basis for the subsequent delivery service business form layout.

[0154] Each field name corresponds to a preset display space, ensuring that the field name, input box, and prompt information can be displayed clearly and avoid crowding or excessive blank space.

[0155] The data types include character string String, numerical value Number and date Date.

[0156] The field length described is the maximum number of characters for a string type field.

[0157] The mandatory or not refers to whether a certain field is a mandatory item in the delivery service business form.

[0158] Determine the field requirements in the delivery service business form based on the acquired delivery service type.

[0159] Subsequently, a form template is designed according to the results of the demand analysis, and the form fields are arranged according to the importance factor of each field name. The arrangement rule is that the field name with the highest importance factor is arranged in the first column of the delivery service business form, the field name with the second highest importance factor is arranged in the second column of the delivery service business form, and so on.

[0160] The database is the storage center for delivery business information and the data source for delivery service business forms.

[0161] The database includes several sub-databases, each of which includes MySQL, Oracle, RDBMS and NoSQL

[0162] Each sub-database has a unique matching database name DatabaseName, service name ServiceName, host name Hostname, port number PortNumber, user name Username and password Password.

[0163] According to the field requirements in the form template, call the preset data extraction script to extract the required data from the data source.

[0164] The machine learning module builds a support vector machine model for field name matching and performs model training.

[0165] Define the data set, and record the data set extracted from the data source as D={d1, d2, ..., dn}. Each sample di contains multiple feature values, representing the contents of different fields;

[0166] For each field sample di, extract text features, including string length L(di) and TF-IDF vectorized feature value ; where i=1, 2, ..., n.

[0167] Construct a character vector for each field: .in is the field name to which field di belongs, including delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; is the attribution label, indicating whether the field di belongs to the current field name. If so, The value of is 1; otherwise it is 0.

[0168] The extracted features are trained using a support vector machine model. The goal is to find an optimal hyperplane that maximizes the interval between samples.

[0169] The specific support vector machine model is: , where w is the weight vector; b is the bias term; C is the penalty parameter, which controls the tolerance for incorrect classification; is a slack variable used to handle nonlinear separable situations.

[0170] Set constraints: ;in is a feature mapping function, which is used to transform the input feature vector Mapping to a high-dimensional space.

[0171] Assume a set of predefined fields, F = {f1, f2, ..., fn} as training samples, where each field fi has a feature vector: Among them are the field names that field fi actually belongs to and The value of is 1; F = {f1, f2, ..., fn} is used as training samples and input into the support vector machine model for training.

[0172] The field matching module retrieves the training results of the machine learning module, matches the fields extracted from the database to the preset field names one by one, and outputs the field names corresponding to each field. .

[0173] The validation and error handling module processes duplicate values ​​in the generated delivery service business form and merges duplicate rows. Define the unique identifier unique_id = TradeDate + SecurityCode + Buyername + SellerName for each delivery. Among them, TradeDate is the hash value of the delivery date field; SecurityCode is the hash value of the delivery number field; Buyername is the hash value of the seller name field; SellerName is the hash value of the seller name field.

[0174] Subsequently, duplicate records are detected based on uniqueness and rows with the same unique identifier are merged in the delivery service business form.

[0175] Verify the generated delivery service business form to ensure the integrity and accuracy of the data. For data that fails verification, perform error processing and prompt the user to make corrections.

[0176] The specific process is: Check each field to see if there is an empty string or null. If so, highlight the field and output an error.

[0177] The storage and distribution module stores the generated delivery service business form in the database and file system for subsequent business processing. At the same time, the delivery service business form is distributed to relevant personnel or systems to ensure the smooth progress of the business process.

[0178] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0179] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations;

[0180] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for generating a delivery service business form, characterized in that: The following steps are involved: Step 1: Demand analysis and form template design: Conduct demand analysis on the delivery service business and confirm the field requirements of various delivery service business forms required in the business process; Step 2: Data source configuration and data extraction; Determine the data source and data source parameters of the delivery service business form, call the preset data extraction script according to the field requirements in the form template, and extract the required data from the data source; Step 3: Data preprocessing and field matching: Perform data cleaning and field matching on the extracted data, remove duplicate parts in the captured data, and match the required data extracted from the data source with the preset field names; Step 4: Form generation and rendering: Generate a specific form instance based on the form template and data; use front-end technology to render the form; Step 5: Form validation and error handling; Process duplicate values ​​in the generated delivery service business form; Step 6: Form storage and distribution: Store the generated delivery service business form in the database and file system, and distribute the delivery service business form to relevant personnel or systems.

2. A method for generating a delivery service business form according to claim 1, characterized in that: The specific process of confirming the field requirements of various delivery service business forms required in the business process is as follows: Get the delivery service type corresponding to the delivery service business form, including physical delivery, warehouse delivery, factory delivery and EFP; Determine the field requirements in the delivery service business form based on the acquired delivery service type; The field requirements include field name, data type, field length and whether it is required; The field names include delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; Each field name corresponds to a preset importance factor, which provides a quantitative basis for the subsequent delivery service business form layout; Each field name corresponds to a preset display space, ensuring that the field name, input box, and prompt information can be displayed clearly without crowding or excessive blank space; The data types include string, number and date; The field length mentioned is the maximum character limit for string type fields; The "required" refers to whether a field is a required item in the delivery service business form; Subsequently, a form template is designed according to the results of the demand analysis, and the form fields are arranged according to the importance factor of each field name. The arrangement rule is that the field name with the highest importance factor is arranged in the first column of the delivery service business form, and the field name with the second highest importance factor is arranged in the second column of the delivery service business form, and so on; the column width is configured for each column according to the preset display space.

3. A method for generating a delivery service business form according to claim 1, characterized in that: The data sources and their data source parameters are as follows: Database types include MySQL, Oracle, RDBMS, and NoSQL; The connection information includes database name DatabaseName, service name ServiceName, host name Hostname, port number PortNumber, user name Username and password Password.

4. A method for generating a delivery service business form according to claim 1, characterized in that: The specific process of data cleaning for the extracted data is as follows: By formula The extracted data is deduplicated, where unique is the deduplication function; D is the set of all extracted data; D' is the set of duplicate data, d is a field element in the data set; d' is a duplicate field element.

5. A method for generating a delivery service business form according to claim 1, characterized in that: Field matching of the extracted data is achieved through the training and application of support vector machines; After the support vector machine model is trained, the trained support vector machine model is used to predict new data; for each new sample, it is input into the trained support vector machine model to obtain its matching field name.

6. A method for generating a delivery service business form according to claim 5, characterized in that: The specific process of support vector machine training is: Data preparation; define the data set: the data set extracted from the data source is recorded as D={d1, d2, ..., dn}, each sample di contains multiple feature values, representing the content of different fields; Text feature extraction: for each field sample di, extract text features, including string length L(di) and TF-IDF vectorized feature value ; where i=1, 2, ..., n; Construct a character vector for each field: ;in is the field name to which field di belongs, including delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; is the attribution label, indicating whether the field di belongs to the current field name; if so, The value of is 1; otherwise it is 0; Build a support vector machine model; The extracted features are trained using a support vector machine model; The goal is to find an optimal hyperplane that maximizes the interval between samples; The specific support vector machine model is: , where w is the weight vector; b is the bias term; C is the penalty parameter, which controls the tolerance for incorrect classification; is a slack variable used to handle nonlinear separable situations; Set constraints: ;in is a feature mapping function, which is used to transform the input feature vector Mapping to high-dimensional space; Model training; Assume a set of predefined fields, F = {f1, f2, ..., fn} as training samples, where each field fi has a feature vector: ; are the field names to which field fi actually belongs and The value of is 1; F = {f1, f2, ..., fn} is used as training samples and input into the support vector machine model for training.

7. A method for generating a delivery service business form according to claim 1, characterized in that: The specific process of handling duplicate values ​​in the generated delivery service business form is as follows: Define the unique identifier of each delivery unique_id=TradeDate+SecurityCode+Buyername+SellerName; where TradeDate is the hash value of the delivery date field; SecurityCode is the hash value of the delivery number field; Buyername is the hash value of the seller name field; SellerName is the hash value of the seller name field; Subsequently, duplicate records are detected based on uniqueness, and rows with the same unique identifier are merged in the delivery service business form; Verify the generated delivery service business form to ensure the integrity and accuracy of the data; perform error processing on data that fails verification and prompt the user to make corrections; The specific process is: check each field to confirm whether there is an empty string or null; if so, highlight the field and output error.

8. A delivery service business form generation system, comprising a template design module, a database, a machine learning module, a field matching module, a verification and error handling module and a storage distribution module, characterized in that ； The template design module conducts demand analysis on the delivery service business and confirms the field requirements of various delivery service business forms required in the business process; Get the delivery service type corresponding to the delivery service business form, including physical delivery, warehouse delivery, factory delivery and EFP; Determine the field requirements in the delivery service business form based on the acquired delivery service type; The field requirements include field name, data type, field length and whether it is required; The field names include delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; Each field name corresponds to a preset importance factor, which provides a quantitative basis for the subsequent delivery service business form layout; Each field name corresponds to a preset display space, ensuring that the field name, input box, and prompt information can be displayed clearly without crowding or excessive blank space; The data types include string, number and date; The field length mentioned is the maximum character limit for string type fields; The "required" refers to whether a field is a required item in the delivery service business form; Subsequently, a form template is designed according to the demand analysis results, and the form fields are arranged according to the importance factor of each field name. The arrangement rule is that the field name with the highest importance factor is arranged in the first column of the delivery service business form, the field name with the second highest importance factor is arranged in the second column of the delivery service business form, and so on; The database is the storage center for delivery business information and the data source for delivery service business forms; The database includes several sub-databases, each of which includes MySQL, Oracle, RDBMS and NoSQL Each sub-database has a unique matching database name DatabaseName, service name ServiceName, host name Hostname, port number PortNumber, user name Username and password Password; According to the field requirements in the form template, call the preset data extraction script to extract the required data from the data source; The machine learning module builds a support vector machine model for field name matching and performs model training; Define the data set, and record the data set extracted from the data source as D={d1, d2, ..., dn}. Each sample di contains multiple feature values, representing the contents of different fields; For each field sample di, extract text features, including string length L(di) and TF-IDF vectorized feature value ; where i=1, 2, ..., n; Construct a character vector for each field: ;in is the field name to which field di belongs, including delivery number, delivery date, security code, transaction price, delivery quantity, buyer name, seller name and delivery method; is the attribution label, indicating whether the field di belongs to the current field name; if so, The value of is 1; otherwise it is 0; The extracted features are trained using a support vector machine model; The goal is to find an optimal hyperplane that maximizes the interval between samples; The specific support vector machine model is: , where w is the weight vector; b is the bias term; C is the penalty parameter, which controls the tolerance for incorrect classification; is a slack variable used to handle nonlinear separable situations; Set constraints: ;in is a feature mapping function, which is used to transform the input feature vector Mapping to high-dimensional space; Assume a set of predefined fields, F = {f1, f2, ..., fn} as training samples, where each field fi has a feature vector: ; are the field names to which field fi actually belongs and The values ​​of are all 1; F={f1, f2, ..., fn} is used as training samples to input into the support vector machine model for training; The field matching module retrieves the training results of the machine learning module, matches the fields extracted from the database to the preset field names one by one, and outputs the field names corresponding to each field. ; The validation and error handling module processes duplicate values ​​in the generated delivery service business form and merges duplicate rows; defines the unique identifier of each delivery, unique_id=TradeDate+SecurityCode+Buyername+SellerName; where TradeDate is the hash value of the delivery date field; SecurityCode is the hash value of the delivery number field; Buyername is the hash value of the seller name field; SellerName is the hash value of the seller name field; Subsequently, duplicate records are detected based on uniqueness, and rows with the same unique identifier are merged in the delivery service business form; Verify the generated delivery service business form to ensure the integrity and accuracy of the data; perform error processing on data that fails verification and prompt the user to make corrections; The specific process is: check each field to confirm whether there is an empty string or null; if so, highlight the field and output error; The storage and distribution module stores the generated delivery service business form in the database and file system to facilitate subsequent business processing; at the same time, the delivery service business form is distributed to relevant personnel or systems to ensure the smooth progress of the business process.

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