Multi-source bill dynamic and static classification management method and system

Abstract

The invention provides a multi-source bill dynamic and static classification management method and system, and relates to the technical field of network bill intelligent management. Multi-network-source bills are obtained, integration processing and relevance matching analysis are carried out on the multi-network-source bills, and a matched bill group is obtained; acquiring a bill transaction node, performing dynamic and static classification packaging on the bill information according to the bill transaction node information, and updating the bill transaction node information according to the acquired newly-added bill information; classifying and packaging a plurality of matched bill groups and unmatched bill groups of each type of bills, classifying and extracting bill feature information, calculating an integration coefficient, and managing and ranking the type of bills; according to the invention, the efficiency and quality of financial management are improved through advanced technical means, and more comprehensive and accurate financial analysis and decision support are provided for enterprises and individuals.

Description

Technical Field

[0001] The invention proposes a method and system for dynamic and static classification management of multi-source bills, and relates to the technical field of network bill intelligent management. Background Art

[0002] In the current financial management field, with the rise of e-commerce, mobile payments and various financial service networks, enterprises and individuals are facing the challenge of managing bills from multiple different networks. Traditional bill management methods mostly rely on manual operations, such as manual entry, classification and comparison of bill information. This method is not only inefficient, but also prone to errors and difficult to meet the needs of modern financial management. In addition, traditional bill management systems often lack cross-network integration capabilities, resulting in isolated bill data between different networks, making it difficult to achieve comprehensive and accurate financial analysis and decision support. Traditional bill management is difficult to handle information such as refunds and settlements in a targeted manner, making bill management chaotic and difficult for users to sort out. Summary of the invention

[0003] The present invention provides a method and system for dynamic and static classification management of multi-source bills, which is used to solve the problems that traditional bill management is difficult to carry out targeted processing of information such as refunds and settlements, making bill management confusing and difficult for users to sort out. The present invention proposes a method and system for dynamic and static classification management of multi-source bills, the method comprising: S1. Obtain bills from multiple networks, integrate the bills from multiple networks, obtain a historical bill database, perform correlation matching analysis on the historical bills and bill information, and obtain a matching bill group; S2. Store the matched bill groups and the unmatched bill groups, obtain the bill transaction nodes, classify and package the bill information dynamically and statically according to the bill transaction node information, and update the bill transaction node information according to the acquired new bill information; S3, classifying and packaging multiple matching bill groups and unmatched bill groups of each type of bill, classifying and extracting bill feature information, calculating integration coefficients, and managing and ranking the bills of the type; S4. Calculate the distributed bill difference coefficient, and set different bill labels according to the calculated bill difference coefficient.

[0004] Further, the S1 includes: Obtaining historical billing information of each network, preprocessing the historical billing information, and obtaining preprocessed historical billing information; Sort the pre-processed historical bill information in chronological order to obtain bill integration information; Classifying the bill integration information according to preset bill categories to obtain bill information of multiple categories; Storing the multiple types of bill information to obtain a historical bill database; Obtaining income information and expenditure information of historical bill data, extracting associated bills from the historical bill data according to the income information and expenditure information, and obtaining multiple associated bill groups; Extracting each bill information of each associated bill group, determining the relevance between the bill information and other bill information, and obtaining relevance determination information; The associated bills are matched according to the association determination information to obtain a matching bill group.

[0005] Further, the S2 includes: Each matched bill group and unmatched bill group is stored to obtain a matched bill storage location and an unmatched bill storage location, wherein the matched bill storage location and the unmatched bill storage location are bill transaction nodes; Determine the bill information of the matching bill transaction node as a closed-loop node, and statically package and store the bill information of the closed-loop node; The billing information of the unmatched billing transaction node is determined as an open-loop node, and the billing information of the open-loop node is dynamically packaged and stored; Acquire new bill information in real time, and match the new bill information with each bill transaction node information of the unmatched bill group; When a matching bill group is obtained, the matching bill group is stored, a new bill transaction node is obtained, and the original unmatched bill group is updated.

[0006] Further, the S3 includes: Obtain multiple matching bill groups and unmatching bill groups for each type of bill; Pack multiple matching bill groups to obtain a category matching package group; Pack multiple unmatched bill groups to obtain a type-unmatched packaged group; Extract bill feature information of the type matching packaging group to obtain matching feature information; Extract bill feature information of the type mismatching packaging group to obtain mismatching feature information; Acquire characteristic information of each matching bill group and each unmatching bill group in the matching characteristic information and the unmatching characteristic information; Corresponding storage of all feature information Calculate the integration coefficient of each type of bill according to the characteristic information combined with preset weight information; The bills of the types are managed and ranked according to the integration coefficient to obtain management ranking information.

[0007] Further, the S4 includes: Calculate a distributed bill difference coefficient for each matching bill group and non-matching bill group of the category matching package group and the category non-matching package group; When the bill difference coefficient is a positive number, a bill expenditure label is set for the corresponding matching bill group or the unmatching bill group; When the bill difference coefficient is zero, a bill balance label is set for the corresponding matching bill group or the unmatching bill group; When the bill difference coefficient is a negative number, a bill income label is set for the corresponding matching bill group or unmatching bill group.

[0008] Furthermore, the system comprises: An integration and matching module, used to obtain bills from multiple networks, integrate the bills from multiple networks, obtain a historical bill database, perform correlation matching analysis on the historical bills and bill information, and obtain a matching bill group; The packaging and updating module is used to store the matched bill groups and the unmatched bill groups, obtain the bill transaction nodes, classify and package the bill information dynamically and statically according to the bill transaction node information, and update the bill transaction node information according to the acquired new bill information; A feature management module, used to classify and package multiple matching bill groups and unmatched bill groups of each type of bill, classify and extract bill feature information, calculate integration coefficients, and manage and rank the bills of the type; The label setting module is used to calculate the distributed bill difference coefficient and set different bill labels according to the calculated bill difference coefficient.

[0009] Beneficial effects of the present invention: By acquiring and integrating multi-network bills, centralized management and unified analysis of data are achieved, improving data integration capabilities and utilization efficiency. The application of correlation matching algorithms makes bill analysis more accurate, and can effectively identify duplicate or related bill information, reducing redundancy and errors. The system can update bill information in real time, and perform dynamic and static classification and packaging processing based on bill characteristics, thereby improving the flexibility and timeliness of data management. Through the calculation of integration coefficients and the formulation of management rankings, managers are provided with a scientific basis for decision-making, which helps to optimize resource allocation and the formulation of management strategies. The calculation of distributed bill difference coefficients and the label setting function enable the system to promptly detect abnormal bill information, and issue early warnings and processing, thereby improving the efficiency and accuracy of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a dynamic and static classification management method for multi-source bills; Figure 2 This is a diagram of the network bill management process. DETAILED DESCRIPTION

[0011] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0012] In one embodiment of the present invention, a method and system for dynamic and static classification management of multi-source bills is proposed, and the method includes: S1. Obtain bills from multiple networks, integrate the bills from multiple networks, obtain a historical bill database, perform correlation matching analysis on the historical bills and bill information, and obtain a matching bill group; S2. Store the matched bill groups and the unmatched bill groups, obtain the bill transaction nodes, classify and package the bill information dynamically and statically according to the bill transaction node information, and update the bill transaction node information according to the acquired new bill information; S3, classifying and packaging multiple matching bill groups and unmatched bill groups of each type of bill, classifying and extracting bill feature information, calculating integration coefficients, and managing and ranking the bills of the type; S4. Calculate the distributed bill difference coefficient, and set different bill labels according to the calculated bill difference coefficient, such as Figure 1 shown.

[0013] The working principle of the above technical solution is as follows: the system automatically or manually obtains bill data from multiple different networks (such as banks, e-commerce networks, payment networks, etc.). Clean, convert and standardize bill data from different networks and in different formats to ensure data consistency and comparability, thereby building a unified historical bill database. Compare the bills in the database in pairs, identify bills with the same content as matching bill groups, and the rest as unmatched bill groups. Store the matching and unmatched bill groups separately, and extract key node information of bill transactions. Dynamic and static classification: According to the frequency of change of bill transaction nodes, bill information is divided into dynamic and static categories. Dynamic bills need to be updated frequently, while static bills (such as one-time purchases) are relatively stable. The classified bills are packaged for easy management and analysis. The system continuously monitors the newly added bill information and updates the bill transaction nodes based on this new information to maintain the timeliness and accuracy of the data. The matching and unmatched bill groups of each type of bill (such as shopping, catering, transportation, etc.) are further classified and packaged. Extract key feature information from the classified bills, such as consumption frequency, average amount, consumption location, etc., for subsequent analysis. Integration coefficient: By calculating the integration coefficient of each type of bill, evaluate its importance and management priority. Rank bill types based on the integration coefficient to facilitate resource allocation and the formulation of management strategies. Use distributed computing technology to improve the efficiency of bill difference coefficient calculation to ensure that results can be obtained quickly even with large data sets. By calculating the difference coefficient between bills, evaluate their degree of difference and importance. According to the size of the difference coefficient and the characteristics of the bill, set different labels for the bill (such as abnormal, normal, focus, etc.) to facilitate subsequent monitoring and analysis, such as Figure 2 shown.

[0014] The technical effects of the above technical solution are: through the acquisition and integration of multi-network bills, centralized management and unified analysis of data are realized, and the data integration capability and utilization efficiency are improved. The application of correlation matching algorithm makes bill analysis more accurate, and can effectively identify repeated or related bill information, reducing redundancy and errors. The system can update bill information in real time, and perform dynamic and static classification and packaging processing according to bill characteristics, which improves the flexibility and timeliness of data management. Through the calculation of integration coefficients and the formulation of management rankings, it provides managers with a scientific decision-making basis, which helps to optimize resource allocation and the formulation of management strategies. The calculation of distributed bill difference coefficients and label setting functions enable the system to detect abnormal bill information in a timely manner, and issue early warnings and processing, which improves the efficiency and accuracy of monitoring.

[0015] In one embodiment of the present invention, the S1 includes: Obtaining historical billing information of each network, preprocessing the historical billing information, and obtaining preprocessed historical billing information; Sort the pre-processed historical bill information in chronological order to obtain bill integration information; The bill integration information is classified according to preset bill categories to obtain bill information of multiple categories; the multiple categories include shopping, transportation, transfer, etc.

[0016] Storing the multiple types of bill information to obtain a historical bill database; The income information and expenditure information of the historical bill data are obtained, and the historical bill data is associated with bills according to the income information and expenditure information to obtain multiple associated bill groups; the associated bill group is a bill with equal income and expenditure. The associated bill group includes multiple bills with equal income and expenditure.

[0017] Extract each bill information of each associated bill group, perform correlation determination on the bill information and other bill information, and obtain correlation determination information; the correlation determination information includes bill information of which the bill information in multiple associated bill groups is equal.

[0018] According to the association determination information, the associated bills are matched to obtain a matching bill group. The association determination of the bill information is that the bill information with the same income information from the other party's account and the same expenditure information (income and expenditure of the account, etc.) on the other party's account is determined as the associated bill information; the above steps are to obtain the income and expenditure of the same account. The matching bill group includes bill groups with equal bill information in multiple associated bill groups.

[0019] The working principle of the above technical solution is as follows: historical bill information is obtained from each network. The obtained bill information is preprocessed, including data cleaning, format conversion, data standardization, etc., to ensure data consistency and accuracy. The preprocessed historical bill information is sorted in chronological order, which helps to clearly see the time context of the bill in subsequent analysis. The sorted bill information is integrated, that is, bills from different networks but belonging to the same user or the same account are integrated to form a complete bill record. The integrated bill information is classified according to preset bill types (such as shopping, transportation, transfer, etc.). This helps to conduct in-depth analysis of specific types of bills in the future. The classified bill information of multiple categories is stored in the historical bill database. This database is the basic data source for subsequent analysis. Income information and expenditure information are extracted from the historical bill database, and associated bills are extracted based on this information. Associated bills refer to bills with equal income and expenditure, which usually means that these bills are records of the same transaction (such as transfer). The association between each bill information in each associated bill group and other bill information is determined. The main focus here is on the corresponding relationship between income and expenditure between accounts, that is, whether the income of one account is equal to the expenditure of another account, and whether these transactions are consistent in key information such as amount and time. Through correlation determination, billing information with equal billing information in multiple associated billing groups can be obtained, and these billing information is marked as correlation determination information. According to the correlation determination information, the associated bills are matched to form a matching billing group. The billing information in the matching billing group is equal in multiple associated billing groups, which further confirms that these bills are records of the same transaction.

[0020] The technical effects of the above technical solution are as follows: through preprocessing and integration processing, the integration and standardization of historical bill information of multiple networks are realized, providing a high-quality data source for subsequent analysis. The classification and storage of bill information improves the efficiency of data retrieval, allowing users to quickly locate specific types of bill information. The extraction of related bills based on the correspondence between income and expenditure effectively identifies the records of the same transaction between different networks or different accounts, and improves the accuracy of bill analysis. Through correlation determination and matching algorithms, intelligent matching and determination of bill information are realized, reducing manual intervention and improving work efficiency. The constructed historical bill database and matching bill group provide strong support for subsequent in-depth analysis and decision-making, such as consumption habit analysis, financial planning, etc. Through automated bill processing and analysis processes, the user's operating burden is reduced and the user experience is improved.

[0021] In one embodiment of the present invention, S2 includes: Each matched bill group and unmatched bill group is stored to obtain a matched bill storage location and an unmatched bill storage location, wherein the matched bill storage location and the unmatched bill storage location are bill transaction nodes; The bill information of the matching bill transaction node is determined as a closed-loop node, and the bill information of the closed-loop node is packaged and stored statically; it is packaged and stored separately as a stable state, and is not easily updated.

[0022] The billing information of the unmatched billing transaction node is determined to be an open-loop node, and the billing information of the open-loop node is dynamically packaged and stored; the dynamic packaged storage is in an unstable state, and is updated as appropriate.

[0023] Acquire the newly added bill information in real time, and match the newly added bill information with each bill transaction node information of the unmatched bill group; the matching operation is the same as step S2.

[0024] When a matching bill group is obtained, the matching bill group is stored, a new bill transaction node is obtained, and the original unmatched bill group is updated.

[0025] The working principle of the above technical solution is: store each matching bill group and unmatched bill group separately and record the storage location. These storage locations are marked as bill transaction nodes in the system, representing the key intersection of bill information. According to whether the bill group matches, the bill transaction nodes are divided into closed-loop nodes and open-loop nodes. Closed-loop nodes represent matching bill groups, whose bill information has been verified and confirmed to form a closed loop; while open-loop nodes represent unmatched bill groups, whose bill information may still be changing or need further verification. The bill information of closed-loop nodes is statically packaged and stored. This storage method means that these bill information is regarded as a stable state and is not easily updated or modified to ensure the accuracy and consistency of the data. The bill information of open-loop nodes is dynamically packaged and stored. This storage method allows the system to update the bill in real time according to the newly added bill information or other changes to adapt to the instability of the data. The system obtains the newly added bill information in real time and matches it with the information of each bill transaction node of the unmatched bill group. If the newly added bill information successfully matches the bill information of a certain unmatched bill group to form a new matching bill group, the matching bill group is stored and a new bill transaction node (closed-loop node) is generated. At the same time, the original unmatched bill group is updated, and part of the bill information may be transferred to the new matched bill group, or it may be completely converted to a matched state.

[0026] The technical effect of the above technical solution is: by distinguishing closed-loop nodes and open-loop nodes and adopting different packaging storage methods, the system can manage bill information more flexibly. Static packaging storage ensures the reliability of stable data, while dynamic packaging storage can reflect data changes in a timely manner. Static packaging storage reduces unnecessary update operations, thereby saving storage resources and computing resources. At the same time, dynamic packaging storage can ensure the real-time and accuracy of data, meeting application scenarios that are sensitive to data changes. Real-time acquisition and processing of new bill information can timely discover and process new matching bill groups, thereby improving the efficiency and accuracy of data processing. In addition, by updating unmatched bill groups, the system can continuously optimize the matching degree and integrity of bill information. Classified storage and packaging of bill information help prevent data leakage and tampering. In particular, the stable state of static packaging storage can ensure the long-term security and reliability of key data. By building a complete bill transaction node network, the system can provide users with more comprehensive and accurate financial data analysis support. Both the stable data of closed-loop nodes and the dynamic data of open-loop nodes can provide a strong basis for users' decision-making.

[0027] In one embodiment of the present invention, S3 includes: Obtain multiple matching bill groups and unmatching bill groups for each type of bill; Pack multiple matching bill groups to obtain a category matching package group; Pack multiple unmatched bill groups to obtain a type-unmatched packaged group; Extract bill feature information of the type matching packaging group to obtain matching feature information; Extract bill feature information of the type mismatching packaging group to obtain mismatching feature information; Acquire characteristic information of each matching bill group and each unmatching bill group in the matching characteristic information and the unmatching characteristic information; All feature information is stored accordingly for quick retrieval of overall bill information and related information. Matching feature information and non-matching feature information are overall feature information, which are obtained through natural language processing technology. The feature information of each matching bill group and non-matching bill group is detailed feature information. The feature information includes income and expenditure information, type of bill information, and transaction purpose.

[0028] Calculate the integration coefficient of each type of bill according to the characteristic information combined with preset weight information; The calculation formula of the integration coefficient is: Among them, JC xsis the integration coefficient of the bill type, t is the total number of matching bill groups of the calculated bill type, b is the total number of unmatched bill groups of the calculated bill type, T pa To calculate the feature data of the ath matching bill of the category bill, T z is the feature data of all matching bill groups of all types of bills, B ec is the feature data of the cth unmatched bill of the calculated type of bill, B z is the characteristic data of all unmatched bill groups of all types of bills, q is the weight coefficient of the calculated type of bill, ranging from 0 to 1. When the value is 0, there is no integration coefficient; The bills of the types are managed and ranked according to the integration coefficient to obtain management ranking information.

[0029] The working principle of the above technical solution is as follows: the system obtains multiple matching bill groups and non-matching bill groups for each type of bill (such as shopping, transportation, transfer, etc.) from the historical bill database. These bill groups are packaged. The matching bill groups are packaged into category matching package groups, while the non-matching bill groups are packaged into category non-matching package groups. This packaging process helps to simplify the subsequent feature extraction and analysis process. The system extracts bill feature information from the category matching package group and the category non-matching package group respectively. These feature information include income and expenditure information, category bill information and transaction purposes, etc., which are automatically obtained from the bill text through natural language processing technology (such as text analysis, keyword extraction, etc.). During the extraction process, the system will further refine and obtain detailed feature information of each matching bill group and non-matching bill group for more in-depth analysis. All extracted feature information (including overall feature information and detailed feature information) will be stored accordingly. This storage method enables the system to quickly retrieve all information related to a specific bill or bill group, thereby improving the efficiency of data processing. The system calculates the integration coefficient of each type of bill based on the extracted feature information and preset weight information. According to the size of the integration coefficient, the system manages and ranks the bill types. The bill types with higher rankings usually occupy a more important position in the overall bill information and require more attention and management resources.

[0030] The technical effect of the above technical solution is: through the classification and packaging of bill groups, automatic extraction of feature information and corresponding storage, the system can efficiently process a large amount of bill data and quickly retrieve relevant information. The application of natural language processing technology enables the system to extract rich feature information from the bill text, providing strong support for the subsequent calculation of integration coefficients and management rankings. The calculation of integration coefficients and the formulation of management rankings provide managers with a scientific basis for decision-making. By understanding the importance and characteristics of different types of bills, managers can allocate resources and management energy more reasonably. The system can update bill information in real time and perform dynamic analysis, so that users can grasp their financial status at any time and make corresponding adjustments. At the same time, by optimizing the bill management process and providing personalized service suggestions, the system can also improve user satisfaction and loyalty. During the data processing and analysis process, the system takes strict data protection measures to ensure the security and privacy of data. At the same time, through regular backup and recovery mechanisms, the system can also effectively prevent the risk of data loss and damage.

[0031] In one embodiment of the present invention, the S4 includes: Calculate a distributed bill difference coefficient for each matching bill group and non-matching bill group of the category matching package group and the category non-matching package group; The calculation formula of the bill difference coefficient is: Among them, ZD xs is the bill variance coefficient, Z c For the expenditure data of matched bill group or unmatched bill group, S r Y is the income data that matches the billing group or the expenditure data that does not match the billing group. z The difference impact factor (for example, the difference caused by service fees, etc., the value range is 1-10, and the value is 1 when the fee has no impact). Usually, the matching bill group The result is 0. This calculation is used to calculate the difference in special cases. When calculating the bill difference coefficient of the matching bill group, the data of the matching bill group is brought in, otherwise, the data of the unmatched bill group is brought in.

[0032] When the bill difference coefficient is a positive number, a bill expenditure label is set for the corresponding matching bill group or the unmatching bill group; When the bill difference coefficient is zero, a bill balance label is set for the corresponding matching bill group or the unmatching bill group; When the bill difference coefficient is a negative number, a bill income label is set for the corresponding matching bill group or unmatching bill group.

[0033] The working principle of the above technical solution is: for each matching bill group and unmatched bill group in the category matching package group and the category unmatched package group, the system calculates its bill difference coefficient in a distributed computing manner. Distributed computing allows computing tasks to be distributed to multiple computing nodes for parallel processing, thereby speeding up the computing speed and improving processing efficiency. The calculation formula of the bill difference coefficient takes into account the difference between income and expenditure within the bill group. The specific formula may involve multiple factors such as total income, total expenditure, income change rate in a specific time period, and expenditure change rate, but the core is to compare the relative size of income and expenditure. According to the calculation result of the bill difference coefficient, the system sets a corresponding label for each matching bill group or unmatched bill group. These labels are used to quickly identify the characteristics of the bill group, which is convenient for subsequent management and analysis. When the bill difference coefficient is a positive number, it means that the expenditure of the bill group is greater than the income, and the system sets a "bill expenditure label" for it. When the bill difference coefficient is zero, it means that the income and expenditure of the bill group are equal, and the system sets a "bill balance label" for it. When the bill difference coefficient is a negative number, it means that the income of the bill group is greater than the expenditure, and the system sets a "bill income label" for it.

[0034] The technical effect of the above technical solution is: through distributed computing, the system can process the difference coefficient calculation tasks of multiple bill groups in parallel, significantly improving the computing efficiency. This is particularly important for processing large-scale bill data. Setting labels according to the calculation results of the bill difference coefficient enables the system to accurately identify the characteristics of each bill group. This classification method is helpful for subsequent data analysis and decision making. By setting bill expenditure, balance and income labels, the system can provide financial managers with intuitive bill status information. This helps them to discover potential financial problems in a timely manner and take corresponding measures to adjust and optimize. The setting of labels makes the bill data more intuitive and easy to understand when it is visualized. Financial managers can quickly understand the status of each bill group by viewing the labels, so as to make more accurate judgments. In the distributed computing process, the system needs to ensure the security and privacy of the data. By adopting security measures such as encrypted transmission and access control, the system can protect the bill data from being illegally obtained or tampered with.

[0035] In one embodiment of the present invention, the system comprises: An integration and matching module, used to obtain bills from multiple networks, integrate the bills from multiple networks, obtain a historical bill database, perform correlation matching analysis on the historical bills and bill information, and obtain a matching bill group; The packaging and updating module is used to store the matched bill groups and the unmatched bill groups, obtain the bill transaction nodes, classify and package the bill information dynamically and statically according to the bill transaction node information, and update the bill transaction node information according to the acquired new bill information; A feature management module, used to classify and package multiple matching bill groups and unmatched bill groups of each type of bill, classify and extract bill feature information, calculate integration coefficients, and manage and rank the bills of the type; The label setting module is used to calculate the distributed bill difference coefficient and set different bill labels according to the calculated bill difference coefficient.

[0036] The working principle of the above technical solution is as follows: the system automatically or manually obtains bill data from multiple different networks (such as banks, e-commerce networks, payment networks, etc.). Clean, convert and standardize bill data from different networks and in different formats to ensure data consistency and comparability, thereby building a unified historical bill database. Compare the bills in the database in pairs, identify bills with the same content as matching bill groups, and the rest as unmatched bill groups. Store the matching and unmatched bill groups separately, and extract the key node information of the bill transaction for subsequent classification and processing. Dynamic and static classification: According to the frequency of change of bill transaction nodes, bill information is divided into dynamic and static categories. Dynamic bills need to be updated frequently, while static bills (such as one-time purchases) are relatively stable. The classified bills are packaged for easy management and analysis. The system continuously monitors the newly added bill information and updates the bill transaction nodes based on this new information to maintain the timeliness and accuracy of the data. The matching and unmatched bill groups of each type of bill (such as shopping, catering, transportation, etc.) are further classified and packaged. Extract key feature information from the classified bills, such as consumption frequency, average amount, consumption location, etc., for subsequent analysis. Integration coefficient: Evaluate the importance and management priority of each type of bill by calculating the integration coefficient. Rank bill types based on the integration coefficient to facilitate resource allocation and the formulation of management strategies. Use distributed computing technology to improve the calculation efficiency of the bill difference coefficient to ensure that results can be obtained quickly even with large-scale data sets. Evaluate the degree of difference and importance by calculating the difference coefficient between bills. Set different labels for bills (such as abnormal, normal, focus, etc.) according to the size of the difference coefficient and the characteristics of the bill to facilitate subsequent monitoring and analysis.

[0037] The technical effects of the above technical solution are: through the acquisition and integration of multi-network bills, centralized management and unified analysis of data are realized, and the data integration capability and utilization efficiency are improved. The application of correlation matching algorithm makes bill analysis more accurate, and can effectively identify repeated or related bill information, reducing redundancy and errors. The system can update bill information in real time, and perform dynamic and static classification and packaging processing according to bill characteristics, which improves the flexibility and timeliness of data management. Through the calculation of integration coefficients and the formulation of management rankings, it provides managers with a scientific decision-making basis, which helps to optimize resource allocation and the formulation of management strategies. The calculation of distributed bill difference coefficients and label setting functions enable the system to detect abnormal bill information in a timely manner, and issue early warnings and processing, which improves the efficiency and accuracy of monitoring.

[0038] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for dynamic and static classification management of multi-source bills, characterized in that: The method comprises: S1. Obtain bills from multiple networks, integrate the bills from multiple networks, obtain a historical bill database, perform correlation matching analysis on the historical bills and bill information, and obtain a matching bill group; S2. Store the matched bill groups and the unmatched bill groups, obtain the bill transaction nodes, classify and package the bill information dynamically and statically according to the bill transaction node information, and update the bill transaction node information according to the acquired new bill information; S3, classifying and packaging multiple matching bill groups and unmatched bill groups of each type of bill, classifying and extracting bill feature information, calculating integration coefficients, and managing and ranking the bills of the type; S4. Calculate the distributed bill difference coefficient, and set different bill labels according to the calculated bill difference coefficient.

2. According to claim 1, a method for dynamic and static classification management of multi-source bills is characterized by: The S1 includes: Obtaining historical billing information of each network, preprocessing the historical billing information, and obtaining preprocessed historical billing information; Sort the pre-processed historical bill information in chronological order to obtain bill integration information; Classifying the bill integration information according to preset bill categories to obtain bill information of multiple categories; Storing the multiple types of bill information to obtain a historical bill database; Obtaining income information and expenditure information of historical bill data, extracting associated bills from the historical bill data according to the income information and expenditure information, and obtaining multiple associated bill groups; Extracting each bill information of each associated bill group, determining the relevance between the bill information and other bill information, and obtaining relevance determination information; The associated bills are matched according to the association determination information to obtain a matching bill group.

3. According to claim 1, a method for dynamic and static classification management of multi-source bills is characterized by: The S2 includes: Each matched bill group and unmatched bill group is stored to obtain a matched bill storage location and an unmatched bill storage location, wherein the matched bill storage location and the unmatched bill storage location are bill transaction nodes; Determine the bill information of the matching bill transaction node as a closed-loop node, and statically package and store the bill information of the closed-loop node; The billing information of the unmatched billing transaction node is determined as an open-loop node, and the billing information of the open-loop node is dynamically packaged and stored; Acquire new bill information in real time, and match the new bill information with each bill transaction node information of the unmatched bill group; When a matching bill group is obtained, the matching bill group is stored, a new bill transaction node is obtained, and the original unmatched bill group is updated.

4. According to claim 1, a method for dynamic and static classification management of multi-source bills is characterized in that: The S3 includes: Obtain multiple matching bill groups and unmatching bill groups for each type of bill; Pack multiple matching bill groups to obtain a category matching package group; Pack multiple unmatched bill groups to obtain a type-unmatched packaged group; Extract bill feature information of the type matching packaging group to obtain matching feature information; Extract bill feature information of the type mismatching packaging group to obtain mismatching feature information; Acquire characteristic information of each matching bill group and each unmatching bill group in the matching characteristic information and the unmatching characteristic information; All feature information is stored accordingly; Calculate the integration coefficient of each type of bill according to the characteristic information combined with preset weight information; The bills of the types are managed and ranked according to the integration coefficient to obtain management ranking information.

5. According to claim 1, a method for dynamic and static classification management of multi-source bills is characterized in that: The S4 includes: Calculate a distributed bill difference coefficient for each matching bill group and non-matching bill group of the category matching package group and the category non-matching package group; When the bill difference coefficient is a positive number, a bill expenditure label is set for the corresponding matching bill group or the unmatching bill group; When the bill difference coefficient is zero, a bill balance label is set for the corresponding matching bill group or the unmatching bill group; When the bill difference coefficient is a negative number, a bill income label is set for the corresponding matching bill group or unmatching bill group.

6. According to claim 4, a method for dynamic and static classification management of multi-source bills is characterized in that: The calculation formula for the integration coefficient of each type of bill includes: The calculation formula of the integration coefficient is: Among them, JC xs is the integration coefficient of the bill type, t is the total number of matching bill groups of the calculated bill type, b is the total number of unmatched bill groups of the calculated bill type, T pa To calculate the feature data of the ath matching bill of the category bill, T z is the feature data of all matching bill groups of all types of bills, B ec is the feature data of the cth unmatched bill of the calculated type of bill, B z is the characteristic data of all unmatched bill groups of all types of bills, q is the calculated weight coefficient of the type of bill, and the value is 0-1.

7. According to claim 5, a method for dynamic and static classification management of multi-source bills is characterized in that: The calculation formula for the bill variance coefficient includes: The calculation formula of the bill difference coefficient is: Among them, ZD xs is the bill variance coefficient, Z c For the expenditure data of matched bill group or unmatched bill group, S r Y is the income data that matches the billing group or the expenditure data that does not match the billing group. z is the difference influencing factor.

8. A dynamic and static classification management system for multi-source bills, characterized in that: The system comprises: An integration and matching module, used to obtain bills from multiple networks, integrate the bills from multiple networks, obtain a historical bill database, perform correlation matching analysis on the historical bills and bill information, and obtain a matching bill group; The packaging and updating module is used to store the matched bill groups and the unmatched bill groups, obtain the bill transaction nodes, classify and package the bill information dynamically and statically according to the bill transaction node information, and update the bill transaction node information according to the acquired new bill information; A feature management module, used to classify and package multiple matching bill groups and unmatched bill groups of each type of bill, classify and extract bill feature information, calculate integration coefficients, and manage and rank the bills of the type; The label setting module is used to calculate the distributed bill difference coefficient and set different bill labels according to the calculated bill difference coefficient.

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