A data settlement system, method and electronic device

Abstract

The application provides a data settlement system, method and electronic equipment, and relates to the technical field of communication data processing.The data settlement system comprises a data acquisition unit, a data standardization unit, a settlement rule matching unit, a business demand judgment unit, a rule engine editing unit and a settlement execution unit.The data acquisition unit is used for acquiring a plurality of business data.The data standardization unit is used for standardizing each business data to obtain a standard number field.The settlement rule matching unit is used for matching the standard number field corresponding to all business data with a preset rule library to determine the settlement rule corresponding to the business data.The business demand judgment unit is used for judging whether the settlement rule needs to be adjusted.The rule engine editing unit is used for editing and updating the settlement rule of the business data to obtain the latest settlement rule of the business data when adjustment is needed, and the settlement rule is taken as the latest settlement rule when adjustment is not needed.The settlement execution unit is used for generating a settlement detail list of a cooperation party according to the latest settlement rule of each business data.The application improves the accuracy and efficiency of data settlement.

Description

Technical Field

[0001] This invention relates to the field of communication data processing technology, and more specifically, to a data settlement system, method, and electronic device. Background Technology

[0002] In the field of data settlement within the telecommunications industry, the main focus is on the collection, processing, and settlement of various types of data between telecommunications service providers and their partners. Traditional settlement systems primarily rely on manual integration of heterogeneous data from multiple sources, such as call records, data traffic, and content usage data, followed by configuration of fixed settlement rules to complete the data settlement process.

[0003] In related technologies, the diverse sources and varying formats of heterogeneous data (such as common CSV, XML, and some partner-defined transmission protocols) lead to cumbersome and time-consuming data integration processes. Furthermore, since settlement rules are based on code logic, changes in business requirements necessitate manual code recompilation and deployment, a complex and time-consuming process prone to human error. These factors result in a significant amount of manual operation in the data settlement process, severely extending the completion time. Summary of the Invention

[0004] The problem addressed by this invention is how to improve the accuracy and efficiency of data settlement.

[0005] To address the above problems, the present invention provides a data settlement system, method, and electronic device.

[0006] In a first aspect, the present invention provides a data settlement system comprising:

[0007] The data acquisition unit is used to acquire multi-source heterogeneous data from partners, including multiple business data.

[0008] A data standardization unit is used to standardize each piece of business data through field mapping and semantic verification to obtain a standard number segment corresponding to each piece of business data.

[0009] The settlement rule matching unit is used to match the standard digital segments corresponding to all the business data with a preset rule library to determine the settlement rule corresponding to the business data.

[0010] The business requirement judgment unit is used to analyze the business requirement information corresponding to each business data and determine whether the settlement rules of the business data need to be adjusted.

[0011] The rule engine editing unit is used to edit and update the settlement rules of the business data according to the business requirement information of the business data when the settlement rules need to be adjusted, so as to obtain the latest settlement rules of the business data; when the settlement rules do not need to be adjusted, the settlement rules of the business data are used as the latest settlement rules.

[0012] The settlement execution unit is used to generate a settlement details list for the partner based on the latest settlement rules for each of the business data.

[0013] Optionally, the data acquisition unit is specifically used for:

[0014] Obtain the business type corresponding to each piece of business data required when settling data with the partner;

[0015] Generate a data request corresponding to each of the aforementioned business types;

[0016] The business data is obtained by sending the data request to the partner.

[0017] Optionally, the data standardization unit is specifically used for:

[0018] Each of the aforementioned business data is preprocessed to obtain processed business data with a unified encoding format;

[0019] By mapping fields, the original field names in each of the processed business data are converted into preset standard field names to obtain the standard fields of the business data;

[0020] The standard field is semantically validated, and the format of the standard field after semantic validation is completed is converted to obtain the standard number segment of the business data in the standard data format.

[0021] Optionally, the settlement rule matching unit is specifically used for:

[0022] Feature extraction is performed on the standard digital segment corresponding to each of the business data to obtain multiple key business features in the standard digital segment;

[0023] Each of the key business features is matched and compared with the preset settlement rules in the preset rule base to obtain a matching comparison result, wherein the matching comparison result includes multiple rules to be filtered corresponding to the business data;

[0024] All the rules to be filtered in the matching comparison results are filtered to obtain the settlement rules corresponding to the business data.

[0025] Optionally, the business requirement determination unit is specifically used for:

[0026] Obtain the business type, business scale, number of partner users, user usage status, and partner agreement terms corresponding to each business data, and use the business type, business scale, number of partner users, user usage status, and partner agreement terms as the business requirement information;

[0027] Feature extraction is performed on the business requirement information to obtain multiple key feature indicators of the business requirement information;

[0028] A comprehensive analysis of all the aforementioned key characteristic indicators is conducted, and the analysis results are quantified to determine the degree of change in the business needs of the partner.

[0029] The change level value is compared with the preset change threshold of the business data to determine whether the settlement rules of the business data need to be adjusted.

[0030] Wherein, when the degree of change is greater than or equal to the preset change threshold, the settlement rules of the business data need to be adjusted;

[0031] When the degree of change is less than the preset change threshold, there is no need to adjust the settlement rules of the business data.

[0032] Optionally, the rule engine editing unit is specifically used for:

[0033] When it is necessary to adjust the settlement rules, the business requirement information is parsed to determine the content of the business requirement change;

[0034] Based on the changes in the business requirements, determine the rule change template corresponding to the changes in the business requirements;

[0035] The rule change template is used to generate a rule adjustment plan, and the settlement rules of the business data are edited and updated according to the rule adjustment plan to obtain the latest settlement rules.

[0036] Optionally, the rule engine editing unit is further used for:

[0037] Natural language processing technology is used to perform semantic analysis on the changes in the business requirements and generate a structured semantic representation.

[0038] The rule change template is obtained by matching the structured semantic representation with a preset rule template library using a machine learning algorithm.

[0039] Optionally, the settlement execution unit is specifically used for:

[0040] Based on the latest settlement rule for each of the business data, generate the settlement content corresponding to the business data;

[0041] Based on the settlement details of each business data item, the settlement details list of the partner is obtained.

[0042] Secondly, a data settlement method of the present invention includes:

[0043] Acquire multi-source heterogeneous data from partners, including multiple business data;

[0044] Each piece of business data is standardized by field mapping and semantic validation to obtain a standard number segment corresponding to each piece of business data.

[0045] The settlement rules corresponding to the business data are determined by matching the standard digital segments corresponding to all the business data with a preset rule base.

[0046] Based on the analysis of the business requirement information corresponding to each of the business data, it is determined whether the settlement rules of the business data need to be adjusted;

[0047] When the settlement rules need to be adjusted, the settlement rules of the business data are edited and updated according to the business requirement information of the business data to obtain the latest settlement rules of the business data; when the settlement rules do not need to be adjusted, the settlement rules of the business data are used as the latest settlement rules.

[0048] Based on the latest settlement rules for each of the aforementioned business data, a detailed settlement list for the partner is generated.

[0049] Thirdly, an electronic device according to the present invention includes a memory and a processor;

[0050] The memory is used to store computer programs;

[0051] The processor is configured to implement the data settlement method as described above when executing the computer program.

[0052] The data settlement system, method, and electronic device of this invention enable the data acquisition unit to comprehensively acquire multi-source heterogeneous data from partners, providing a complete data foundation for subsequent processing. The data standardization unit standardizes business data through field mapping and semantic verification, ensuring all data adheres to unified specifications and eliminating errors caused by data format differences, thereby improving settlement accuracy. The settlement rule matching unit accurately matches a preset rule library, the business requirement judgment unit analyzes and judges settlement rules based on business needs, and the rule engine editing unit flexibly edits and updates settlement rules according to business requirement information, ensuring a high degree of consistency between settlement rules and actual conditions, further improving settlement accuracy. Finally, the settlement execution unit generates a detailed settlement list based on the latest settlement rules, guaranteeing the accuracy of the settlement results. Traditional settlement systems rely on manual integration of multi-source heterogeneous data, a cumbersome and time-consuming process. In contrast, the various units in this application work collaboratively to automate the entire process of data acquisition, standardization, rule matching and adjustment, and settlement execution, significantly reducing manual operations and avoiding the inefficiency of manual processing. Furthermore, when business requirements change, there is no need to recompile and deploy the code. The settlement rules can be quickly adjusted through the rule engine editing unit, shortening the adjustment cycle and improving the system's adaptability to business changes, thereby improving the overall data settlement efficiency.

[0053] Meanwhile, addressing the cumbersome data integration issues caused by the wide range of heterogeneous data sources and significant format differences in related technologies, this invention, through a data acquisition unit acquiring various types of data, and a data standardization unit using field mapping and semantic verification to standardize business data, converts data of different formats into standard numerical segments, solving the problem of inconsistent data formats, simplifying the data integration process, improving integration efficiency, and effectively addressing the challenges of integrating multi-source heterogeneous data. Regarding related technologies where settlement rules are based on code logic, requiring manual recompilation and deployment of code to adjust rules when business needs change, a complex process prone to human error leading to settlement errors, this invention employs a rule engine editing unit. When the business needs judgment unit determines that settlement rules need adjustment, the settlement rules can be directly edited and updated based on the business needs information without modifying the code logic, simplifying the adjustment process, reducing the risk of human error, ensuring settlement accuracy, and solving the problem of complex and error-prone settlement rule adjustments. In summary, the data settlement system of this application, through the close cooperation of various functional units, effectively improves the accuracy and efficiency of data settlement, solving the technical problems of cumbersome and time-consuming data integration and complex and error-prone settlement rule adjustments in traditional data settlement systems. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the structure of a data settlement system according to an embodiment of the present invention;

[0055] Figure 2 This is a flowchart illustrating a data settlement method according to another embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of the structure of an electronic device according to another embodiment of the present invention. Detailed Implementation

[0057] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0058] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0059] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0060] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0061] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0062] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties. The collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0063] Combination Figure 1 As shown, the present invention provides a data settlement system, comprising:

[0064] The data acquisition unit is used to acquire multi-source heterogeneous data from partners, including multiple business data.

[0065] Specifically, the data acquisition unit acquires multi-source heterogeneous data from partners by constructing a unified data access interface that adapts to various methods, including partners' API interfaces, FTP transfer protocols, and custom transfer protocols. This data includes call logs, data usage, and content service usage data. It can monitor data sources in real time and periodically extract data from various partners' data sources according to a preset data retrieval frequency, ensuring data timeliness and integrity. This guarantees that subsequent settlement processing is based on the latest data, thus providing rich and accurate raw data materials for the entire data settlement process.

[0066] The data standardization unit is used to standardize each piece of business data through field mapping and semantic verification to obtain a standard number segment corresponding to each piece of business data.

[0067] Specifically, after receiving business data from the data acquisition unit, the data standardization unit first establishes a standardized data dictionary, which includes standard field names, data types, and data formats for each business data item. For each business data item, field mapping is used to match and associate fields in the original data with standard fields; for example, mapping partner A's "call_time" field to the standard field "call duration". Simultaneously, semantic validation rules are used to verify the data content, such as checking if the call duration is non-negative and if traffic data conforms to unit specifications. Abnormal data that does not conform to semantics is cleaned, transformed, or discarded. Finally, each business data item is converted into a standard numerical segment conforming to standard specifications, ensuring that data from different sources has a unified format and semantics, laying the foundation for subsequent settlement rule matching.

[0068] The settlement rule matching unit is used to match the standard digital segments corresponding to all the business data with a preset rule library to determine the settlement rule corresponding to the business data.

[0069] Specifically, after obtaining the standard digital segments corresponding to all business data, the settlement rule matching unit loads the system's preset rule base. The rule base stores various settlement rules, each defining the applicable business scenario and corresponding calculation logic. The settlement rule matching unit compares key information in the standard digital segments, such as business type and service content, with the rule conditions in the rule base one by one. For example, for business data whose standard digital segment is identified as international roaming call, it matches the rule in the rule base that calculates charges based on call duration for international roaming calls, thereby determining the settlement rule corresponding to the business data. This achieves a precise association between the settlement rule and the business data, ensuring that subsequent settlement execution is based on the correct rule.

[0070] The business requirement judgment unit is used to analyze the business requirement information corresponding to each business data and determine whether the settlement rules of the business data need to be adjusted.

[0071] Specifically, when analyzing the business requirement information corresponding to each piece of business data, the business requirement judgment unit first retrieves historical business requirement records and current business requirement change notifications from the database. By analyzing key elements in the business requirement information, such as settlement cycle adjustments and changes in promotional activities, it uses pre-defined judgment logic to compare the fit between the current settlement rules and the business requirements. For example, if the business requirement information shows that a partner has launched a new promotional activity for a specific content service, but the current settlement rules do not cover the calculation method for the discount of this activity, it is determined that the settlement rules need to be adjusted; conversely, if the business requirements have not changed, or the settlement rules already meet the existing business requirements, it is determined that no adjustment is needed, thus providing an accurate decision-making basis for subsequent rule updates.

[0072] The rule engine editing unit is used to edit and update the settlement rules of the business data according to the business requirement information of the business data when the settlement rules need to be adjusted, so as to obtain the latest settlement rules of the business data; when the settlement rules do not need to be adjusted, the settlement rules of the business data are used as the latest settlement rules.

[0073] Specifically, after receiving an adjustment request from the business requirement judgment unit, the rule engine editing unit creates a rule editing instance corresponding to the business data based on the rule template engine. According to the specific content in the business requirement information, the parameters, calculation formulas, and business logic in the settlement rules are adjusted through a visual editing interface or script editing. For example, if the business requirement calls for increasing the revenue sharing ratio of a certain content service, the corresponding revenue sharing rule item is found in the rule editing interface, the corresponding ratio parameters are modified, and test cases are used to verify whether the updated rule meets expectations. After editing, the updated rule is stored in the rule library and marked as the latest settlement rule for that business data. If no adjustment is needed, the original rule is directly retrieved from the rule library as the latest settlement rule, ensuring the timely update and accuracy of the settlement rules to adapt to constantly changing business needs.

[0074] The settlement execution unit is used to generate a settlement details list for the partner based on the latest settlement rules for each of the business data.

[0075] Specifically, after obtaining the latest settlement rules for each business data item, the settlement execution unit initiates the settlement process engine. Based on the type of business data and the associated settlement rules, it calls the corresponding calculation module to perform item-by-item settlement processing. For example, for call record business data, the cost of the call record is calculated based on the call duration billing formula in the latest settlement rules, combined with the call duration data in the standard digital segment; for data traffic, the cost is calculated cumulatively according to the data traffic billing rules. After completing the settlement of all business data, the settlement results of each business data item are summarized and integrated, and a detailed settlement list containing information such as the partner's name, business type, and settlement amount is generated according to a preset list format. This provides partners with clear and accurate settlement basis, achieving efficient and precise data settlement services.

[0076] The data settlement system, method, and electronic device of this invention enable the data acquisition unit to comprehensively acquire multi-source heterogeneous data from partners, providing a complete data foundation for subsequent processing. The data standardization unit standardizes business data through field mapping and semantic verification, ensuring all data adheres to unified specifications and eliminating errors caused by data format differences, thereby improving settlement accuracy. The settlement rule matching unit accurately matches a preset rule library, the business requirement judgment unit analyzes and judges settlement rules based on business needs, and the rule engine editing unit flexibly edits and updates settlement rules according to business requirement information, ensuring a high degree of consistency between settlement rules and actual conditions, further improving settlement accuracy. Finally, the settlement execution unit generates a detailed settlement list based on the latest settlement rules, guaranteeing the accuracy of the settlement results. Traditional settlement systems rely on manual integration of multi-source heterogeneous data, a cumbersome and time-consuming process. In contrast, the various units in this application work collaboratively to automate the entire process of data acquisition, standardization, rule matching and adjustment, and settlement execution, significantly reducing manual operations and avoiding the inefficiency of manual processing. Furthermore, when business requirements change, there is no need to recompile and deploy the code. The settlement rules can be quickly adjusted through the rule engine editing unit, shortening the adjustment cycle and improving the system's adaptability to business changes, thereby improving the overall data settlement efficiency.

[0077] Meanwhile, addressing the cumbersome data integration issues caused by the wide range of heterogeneous data sources and significant format differences in related technologies, this invention, through a data acquisition unit acquiring various types of data, and a data standardization unit using field mapping and semantic verification to standardize business data, converts data of different formats into standard numerical segments, solving the problem of inconsistent data formats, simplifying the data integration process, improving integration efficiency, and effectively addressing the challenges of integrating multi-source heterogeneous data. Regarding related technologies where settlement rules are based on code logic, requiring manual recompilation and deployment of code to adjust rules when business needs change, a complex process prone to human error leading to settlement errors, this invention employs a rule engine editing unit. When the business needs judgment unit determines that settlement rules need adjustment, the settlement rules can be directly edited and updated based on the business needs information without modifying the code logic, simplifying the adjustment process, reducing the risk of human error, ensuring settlement accuracy, and solving the problem of complex and error-prone settlement rule adjustments. In summary, the data settlement system of this application, through the close cooperation of various functional units, effectively improves the accuracy and efficiency of data settlement, solving the technical problems of cumbersome and time-consuming data integration and complex and error-prone settlement rule adjustments in traditional data settlement systems.

[0078] Optionally, the data acquisition unit is specifically used for:

[0079] Obtain the business type corresponding to each piece of business data required when settling data with the partner;

[0080] Generate a data request corresponding to each of the aforementioned business types;

[0081] The business data is obtained by sending the data request to the partner.

[0082] Specifically, the data acquisition unit first extracts a list of business types required for data settlement from the system's internal business configuration database. This list is pre-generated based on the business agreements with partners and the system configuration. Then, relying on a predefined data request template library, which provides corresponding data request formats for each business type, including requested fields, data format requirements, and transmission protocols, the data acquisition unit uses these templates, combined with the specific data interface specifications of the partners, to generate data requests that precisely match the business types.

[0083] Subsequently, the data acquisition unit selects an appropriate data transmission method based on the data interface information provided by the partner, such as sending an API request via HTTP or downloading data files from the partner's server using FTP. During the data request process, the system also incorporates a built-in data integrity verification mechanism to ensure that the sent data requests are formatted correctly and are complete, enabling the partner to accurately identify and respond to the requests.

[0084] After receiving data from its partners, the data acquisition unit performs preliminary integrity checks and anomaly handling to verify that the data meets the expected format and content requirements. If any data is missing or abnormal, the system automatically triggers a retry mechanism to resend the data request until complete and valid business data is successfully obtained. This ensures the stability of the data acquisition process and the accuracy of the data, providing a solid foundation for subsequent data processing steps.

[0085] This invention, by clearly defining the business type corresponding to each business data required for data settlement, makes data collection targeted and purposeful. Precisely generating data requests based on business type ensures that the collected data highly matches business needs, avoids collecting irrelevant data, and improves data effectiveness. Simultaneously, obtaining business data by sending data requests to partners guarantees the reliability and accuracy of the data source, providing a high-quality data foundation for subsequent data settlement. It can generate corresponding data requests based on the business types of different partners, applicable to various business scenarios and data interface specifications. Whether it's an API interface, FTP transfer, or other custom protocols, the data acquisition unit can flexibly obtain data from partners by adapting to the corresponding data request format, enhancing the system's compatibility and integrability with different partners.

[0086] The system automatically generates and sends data requests based on business type, reducing manual intervention and avoiding errors and delays that may result from manual operations. It enables timely acquisition of the latest business data, improving the timeliness of data collection and accelerating the entire data settlement process, making settlement work more efficient. Integrity checks and anomaly handling are performed during data collection, and an automatic retry mechanism ensures data integrity. By verifying the format and content of the data to meet requirements, problems during data transmission can be identified and addressed promptly, improving data reliability and providing strong support for subsequent data processing and settlement.

[0087] Optionally, the data standardization unit is specifically used for:

[0088] Each of the aforementioned business data is preprocessed to obtain processed business data with a unified encoding format;

[0089] By mapping fields, the original field names in each of the processed business data are converted into preset standard field names to obtain the standard fields of the business data;

[0090] The standard field is semantically validated, and the format of the standard field after semantic validation is completed is converted to obtain the standard number segment of the business data in the standard data format.

[0091] Specifically, when standardizing business data, the data standardization unit first performs preprocessing on each piece of business data. The purpose of preprocessing is to unify the data encoding format. For example, data with different character encoding formats (such as GBK, UTF-8, etc.) is uniformly converted to UTF-8 encoding format to eliminate data parsing errors caused by encoding differences. Simultaneously, preprocessing removes invalid characters, redundant spaces, and other impurities from the data to ensure data cleanliness, resulting in processed business data with a unified encoding format. Next, using a pre-configured field mapping table, the original field names in each piece of processed business data are converted to preset standard field names. For example, suppose a field name in the original data is "call_time," and the standard field name is "call duration." The field mapping table stores this name correspondence. By looking up this mapping table, the "call_time" field name in all business data is converted to "call duration," and so on, completing the conversion of all original field names to standard field names, resulting in the standard fields of the business data.

[0092] Finally, the data standardization unit performs semantic validation on the standard fields. Semantic validation verifies the rationality and accuracy of the data based on predefined semantic rules. For example, for the "call duration" field, the semantic validation rule may require its value to be non-negative; for the "data traffic" field, it may require its value to conform to a certain data volume unit format (such as MB, GB). Data that does not conform to the semantic rules is marked or corrected. After semantic validation, the standard fields are also formatted to conform to the standard data format specified by the system. For example, numeric data is uniformly converted to string data, or date data is converted to a uniform "YYYY-MM-DD" format, ultimately obtaining standard numerical segments of business data in the standard data format, thereby providing unified and standardized data support for subsequent settlement rule matching and settlement execution.

[0093] This invention preprocesses business data and standardizes the encoding format, eliminating obstacles to data parsing and processing caused by different encoding formats, ensuring consistent identification and reading of data in subsequent processing. Field mapping standardizes original field names, ensuring consistency in field names across business data from different sources. Furthermore, format conversion further conforms the data to standard data format requirements, resulting in high standardization and uniformity throughout the system, providing a standardized data foundation for subsequent settlement processing. Removal of invalid characters and handling of redundant spaces during preprocessing reduces data impurities and improves data cleanliness, thereby lowering the probability of errors in subsequent processing stages. Semantic validation verifies the rationality and accuracy of the data, promptly identifying and correcting data that does not conform to semantic rules, ensuring data authenticity and usability. After these processing steps, the resulting standard numerical segments are more accurate in both semantics and format, providing a reliable basis for subsequent settlement rule matching and settlement execution, effectively avoiding settlement errors caused by data quality issues.

[0094] Data standardization enables data from different partners and business types to be processed according to a unified standard, enhancing the system's compatibility with multi-source heterogeneous data. Regardless of the partner's origin, data processed by the data standardization unit can be converted into standard digital segments that meet system requirements, facilitating integration with other components and modules within the system. This facilitates seamless data flow between different systems and business processes, improving the overall integration and collaborative capabilities of the data settlement system. Standardized data possesses a unified format and semantics, enabling more efficient subsequent steps such as settlement rule matching, business requirement assessment, and settlement execution. The system no longer needs to expend additional time and resources to handle data format differences and data quality issues, thereby reducing the complexity and time cost of data processing, accelerating the entire data settlement process, and improving the overall operational efficiency of the system.

[0095] Optionally, the settlement rule matching unit is specifically used for:

[0096] Feature extraction is performed on the standard digital segment corresponding to each of the business data to obtain multiple key business features in the standard digital segment;

[0097] Each of the key business features is matched and compared with the preset settlement rules in the preset rule base to obtain a matching comparison result, wherein the matching comparison result includes multiple rules to be filtered corresponding to the business data;

[0098] All the rules to be filtered in the matching comparison results are filtered to obtain the settlement rules corresponding to the business data.

[0099] Specifically, the settlement rule matching unit first performs feature extraction on the standard digital segment corresponding to each business data. This step mainly relies on a preset feature extraction algorithm, which can identify and extract multiple key business features from the standard digital segment. For example, for a call record business data, key business features may include information such as "call duration," "call type" (local, long-distance, international, etc.), and "call time"; for data traffic business data, key business features may include "data traffic usage" and "data traffic type" (2G, 3G, 4G, 5G, etc.). In this way, the settlement rule matching unit can accurately extract the core features of each business data, providing a crucial basis for subsequent rule matching. After completing feature extraction, the settlement rule matching unit will match and compare the extracted key business features with preset settlement rules in the preset rule base. Each settlement rule in the preset rule base defines a series of conditions that correspond to the key business features of the business data. For example, a settlement rule may stipulate a billing standard of "call type is long-distance and call duration exceeds 30 minutes". The settlement rule matching unit compares the key business characteristics of the business data with the conditions in the preset settlement rules, identifying all rules that partially or completely match the business data characteristics. These rules are then included in the matching comparison results as rules to be filtered. Finally, the settlement rule matching unit filters all the rules to be filtered in the matching comparison results, based on factors such as rule priority, scope of application, and specific attributes of the business data. For example, some settlement rules may have higher priority and be applicable to specific business scenarios or partners. By comprehensively considering these factors, the settlement rule matching unit can select the settlement rule that best suits the current business data from the rules to be filtered, thereby achieving precise matching.

[0100] This invention, through extracting key business features and comparing them with a preset rule base, accurately determines settlement rules applicable to different business data. This precise matching avoids the problems of rule misuse or inaccurate matching that may occur in traditional systems, improving settlement accuracy. The extraction of key business features allows the system to focus on core elements related to settlement, quickly finding the most suitable settlement rules in complex business scenarios and ensuring that settlement results conform to actual business conditions. The feature extraction process simplifies the complexity of data matching. Compared to directly matching all raw data, matching only key business features significantly reduces computation and improves matching efficiency. Simultaneously, further filtering of the rules to be selected in the matching comparison results quickly eliminates inapplicable rules, further optimizing the rule matching process and improving system processing speed. This efficient processing capability enables the system to handle large-scale business data, meeting the high-frequency, real-time requirements of data settlement in the communications industry and effectively shortening the settlement cycle. This implementation allows the preset rule base to be flexibly updated and adjusted according to changes in business needs. When business rules change, only the corresponding rules in the preset rule base need to be updated, without requiring large-scale modifications to the entire system. The system can quickly adapt to new business scenarios based on new rules, meeting the rapidly changing business needs of the communications industry. Furthermore, the method for extracting key business features can be adjusted and optimized according to the characteristics of the business data, making the system more adaptable and scalable, and suitable for different types of business data and the requirements of partners.

[0101] Optionally, the business requirement determination unit is specifically used for:

[0102] Obtain the business type, business scale, number of partner users, user usage status, and partner agreement terms corresponding to each business data, and use the business type, business scale, number of partner users, user usage status, and partner agreement terms as the business requirement information;

[0103] Feature extraction is performed on the business requirement information to obtain multiple key feature indicators of the business requirement information;

[0104] A comprehensive analysis of all the aforementioned key characteristic indicators is conducted, and the analysis results are quantified to determine the degree of change in the business needs of the partner.

[0105] The change level value is compared with the preset change threshold of the business data to determine whether the settlement rules of the business data need to be adjusted.

[0106] Wherein, when the degree of change is greater than or equal to the preset change threshold, the settlement rules of the business data need to be adjusted;

[0107] When the degree of change is less than the preset change threshold, there is no need to adjust the settlement rules of the business data.

[0108] Specifically, the business requirement assessment unit obtains information such as business type, business scale, number of partner users, user usage, and agreement terms for each business data item through the system's internal business data interface and partner agreement database, and integrates this information into business requirement information. For example, the business type might be voice calls, data traffic, or SMS services; the business scale can be measured by the number of users, service area, or transaction volume; the number of partner users and their usage directly reflect the actual operational status of the business; and the partner agreement terms may contain key information such as settlement methods, preferential conditions, or special requirements. After obtaining this business requirement information, the business requirement assessment unit uses a pre-designed feature extraction algorithm to process this information and extract multiple key feature indicators. For example, it extracts service type codes from business types, user scale and regional coverage codes from business scale, and average usage and frequency of use from partner user usage. These feature indicators form the basis for subsequent analysis of changes in business requirements. Next, the business requirement assessment unit conducts a comprehensive analysis of all extracted key feature indicators. This step typically involves building a multi-dimensional analytical model that considers the interrelationships between various key performance indicators (KPIs) and their potential impact on changes in business needs. For example, if business scale expands and the number of users increases significantly, while new promotional activities appear in partner agreements, these factors combined may indicate a substantial change in business needs. To more objectively assess the degree of change in business needs, the system quantifies the analysis results, calculating a degree of change value. This quantification process may involve assigning weights to each key performance indicator and calculating a weighted total score based on its magnitude of change. For example, changes in business scale may be assigned a higher weight, while some minor changes in partner agreement terms may be assigned a lower weight. The formula for calculating the degree of change value may be the sum of the weighted feature change scores, or other mathematical models designed based on business logic.

[0109] Finally, the system compares the calculated degree of change with a preset threshold, which is set based on historical data and business experience to determine whether the change in business requirements is significant enough to necessitate adjustments to the settlement rules. If the degree of change is greater than or equal to the preset threshold, the system determines that the current settlement rules may not accurately reflect the new business requirements, thus requiring adjustment. Conversely, if the degree of change is less than the preset threshold, the current settlement rules are considered still applicable and no adjustment is needed. This mechanism ensures that the settlement rules can respond promptly to significant changes in business requirements while avoiding unnecessary frequent adjustments due to minor fluctuations, thereby improving system stability and efficiency.

[0110] This invention enables comprehensive collection of business requirement information, including business type, business scale, and other aspects. Key feature indicators are obtained through feature extraction, and comprehensive analysis and quantitative processing are performed to accurately calculate the degree of change in business requirements. This precise judgment allows the system to respond promptly to significant changes in business requirements, avoiding settlement errors or unreasonable settlement rule applications caused by undetected changes in business requirements, thus improving the accuracy and adaptability of settlement. Through quantitative processing and comparison with preset change thresholds, the system can quickly determine whether settlement rules need to be adjusted. This avoids the tedious process of manually analyzing changes in business requirements one by one, reduces unnecessary settlement rule adjustment operations, and thus improves the overall efficiency of the system. When the degree of change does not reach the threshold, the system can stably use the existing settlement rules without frequent adjustments, saving system resources and processing time. The preset change threshold is set based on historical data and business experience, providing an objective basis for judging changes in business requirements. Only when the degree of change reaches or exceeds the threshold will the system trigger the settlement rule adjustment process. This mechanism avoids frequent rule adjustments caused by minor, occasional fluctuations in business demand, enhances the stability and reliability of the system in the face of complex and ever-changing business environments, and ensures the continuity and accuracy of the settlement process.

[0111] Optionally, the rule engine editing unit is specifically used for:

[0112] When it is necessary to adjust the settlement rules, the business requirement information is parsed to determine the content of the business requirement change;

[0113] Based on the changes in the business requirements, determine the rule change template corresponding to the changes in the business requirements;

[0114] The rule change template is used to generate a rule adjustment plan, and the settlement rules of the business data are edited and updated according to the rule adjustment plan to obtain the latest settlement rules.

[0115] Specifically, upon receiving an instruction to adjust settlement rules, the rules engine editing unit first performs in-depth analysis of the business requirement information using natural language processing technology and predefined business requirement parsing rules. For example, if the business requirement information mentions "the number of partner users has increased by 20%", the system will recognize this information and determine that the business requirement change is "increased user numbers". Next, based on the business requirement change, the system searches for a matching template from a pre-set rule change template library. This library stores rule change templates corresponding to various business requirement change scenarios. For example, for the business requirement change of "increased user numbers", the system will match a rule change template for "changes in user numbers", which defines how to adjust settlement rules based on changes in user numbers. After matching the corresponding rule change template, the rules engine editing unit generates a rule adjustment plan based on the guidance in the template and the specific business requirement change. For example, the rule adjustment plan might adjust the fee calculation formula in the settlement rules based on the percentage increase in user numbers. Finally, the system edits and updates the settlement rules of the business data according to the rule adjustment plan, obtaining the latest settlement rules, and stores them in the rule library for use by the subsequent settlement execution unit.

[0116] This invention, through parsing changes in business requirements and matching them with rule change templates, enables the system to quickly generate rule adjustment plans and update settlement rules. This efficiency ensures the system can respond promptly to changes in business needs without manual intervention, significantly shortening the rule adjustment cycle and improving the system's flexibility and adaptability. In traditional systems, rule adjustments typically require manual code modification and deployment, which is prone to errors due to human error. This system, however, automates rule adjustments, reducing human intervention and thus lowering the risk of settlement errors caused by human error, improving the accuracy and reliability of settlement. The use of rule change templates makes the rule adjustment process more standardized and regulated. All rule changes are based on preset templates, making the system's rule base easy to maintain and update. Furthermore, rule change templates can be expanded and optimized according to changes in business needs, further enhancing the system's maintainability and scalability. For partners, the system's ability to adjust settlement rules promptly according to changes in business needs ensures the fairness and reasonableness of settlement results. Partners do not need to worry about settlement issues caused by business changes, thereby improving partner satisfaction and user experience.

[0117] Optionally, the rule engine editing unit is further used for:

[0118] Natural language processing technology is used to perform semantic analysis on the changes in the business requirements and generate a structured semantic representation.

[0119] The rule change template is obtained by matching the structured semantic representation with a preset rule template library using a machine learning algorithm.

[0120] Specifically, the rule engine editing unit first processes the changes in business requirements, using natural language processing (NLP) to perform semantic analysis and generate a structured semantic representation. For example, if the change requires increasing the discount of a service during a specific time period, the system can identify key information such as the service type, time range, and discount changes, and convert this information into structured data. Next, the rule engine editing unit uses machine learning algorithms to match the generated structured semantic representation against a pre-defined rule template library. This library contains various templates suitable for different business requirement change scenarios. The machine learning algorithm compares and analyzes the key features in the structured semantic representation against the templates in the library to find the most suitable rule change template. Once this template is found, the system generates a corresponding rule adjustment plan according to the template's guidance and updates the settlement rules accordingly to obtain the latest settlement rules to adapt to changes in business requirements.

[0121] For example, the machine learning algorithm used in this embodiment is based on the decision tree algorithm. The decision tree algorithm constructs a tree-structured model by extracting and analyzing features from the data. Each node represents a feature judgment, and the leaf nodes represent the final classification result. In rule change template matching, the algorithm judges each feature (such as business type, change attribute, etc.) in the structured semantic representation, gradually finding the most matching template. After receiving the business requirement change content and generating a structured semantic representation, the system provides these semantic features as input data to the machine learning algorithm. The algorithm judges and classifies the input data according to the previously trained decision tree model, and finally outputs the corresponding rule change template. For example, features contained in the structured semantic representation, such as "business type" being voice call service and "change attribute" being discount, are used by the algorithm to judge at each node of the decision tree, ultimately locating the corresponding rule change template.

[0122] The preset rule template library contains rule change templates that provide guidance for rule changes under different business requirement scenarios. For example, one template might address an increase in the number of partner users, specifying how to adjust the fee calculation formula in the settlement rules under such circumstances, such as adjusting the settlement amount calculation method according to the proportion of the increase in the number of users. Once the business requirement change is parsed into a structured semantic representation, the machine learning algorithm searches and matches this structured semantic representation in the preset rule template library. The preset rule template library provides the system with a rich selection of rule change templates, enabling the system to find suitable templates and generate corresponding rule adjustment plans based on different business requirement changes. For example, when the business requirement change concerns an adjustment to the price of a specific service, the system uses a machine learning algorithm to find the corresponding price adjustment template in the preset rule template library, then generates a rule adjustment plan based on that template, and edits and updates the settlement rules.

[0123] The natural language processing technology in this invention can deeply understand the semantics of changes in business requirements, accurately extract key information, and transform it into a structured semantic representation. Machine learning algorithms perform efficient matching based on these structured features, significantly improving matching accuracy. Compared to traditional keyword-based simple matching, this method can more accurately find the rule change template that best matches the changes in business requirements, ensuring that the generated rule adjustment plan meets actual business needs, thereby improving the accuracy of settlement rule adjustments. The entire process is automated, from semantic analysis of changes in business requirements to matching rule change templates, without manual intervention. This greatly shortens the rule adjustment cycle, enabling settlement rules to quickly adapt to changes in business requirements. Especially in the context of frequent changes in business requirements in the communications industry, it can update settlement rules in a timely manner, ensuring the timeliness and effectiveness of settlement and improving the overall efficiency of the system. With the help of natural language processing and machine learning technologies, the system can continuously learn and adapt to new ways of expressing business requirements. As the number of business requirement change cases processed increases, the machine learning model can be continuously optimized, further improving the accuracy and efficiency of matching. This makes the system more intelligent and adaptable when facing complex and ever-changing business environments, enabling it to better cope with various business demand change scenarios and provide strong support for enterprise data settlement.

[0124] Optionally, the settlement execution unit is specifically used for:

[0125] Based on the latest settlement rule for each of the business data, generate the settlement content corresponding to the business data;

[0126] Based on the settlement details of each business data item, the settlement details list of the partner is obtained.

[0127] Specifically, in the data settlement process, after receiving the latest settlement rules from the rule engine editing unit, the settlement execution unit performs a detailed analysis. For example, the latest settlement rules may include specific billing formulas, preferential conditions, and settlement cycles for different business data. For voice call business data, the settlement rules may stipulate segmented billing based on call duration, with different fee standards corresponding to different call durations in different time periods. Next, the settlement execution unit iterates through each business data point and calculates the cost for each item according to the corresponding latest settlement rules. Taking call records as an example, the system applies the billing formula in the settlement rules based on information such as call duration, call type, and call time to calculate the specific cost for each call record. The cost calculation results for these individual business data points constitute the settlement content corresponding to that business data.

[0128] Finally, the settlement execution unit will summarize and integrate the settlement details of all business data. It will categorize and summarize the data according to dimensions such as partners and business types, generating a complete settlement details list. This list details the settlement status of each business under the partner's name, including specific fees for each business, discount deductions, and the final settlement amount.

[0129] This invention generates settlement content through settlement rules, ensuring the accuracy of settlement results for each business data item. The systematic processing avoids calculation errors or omissions that may occur during manual settlement, improving settlement accuracy. The generated settlement details list records the settlement status of each business data item in detail, making the settlement process transparent. Partners can clearly understand how each business data item is billed and how the final settlement amount is derived, facilitating verification and traceability of settlement results and enhancing system credibility. The system automates the settlement process, enabling rapid processing of large amounts of business data and significantly shortening the settlement cycle. The system can complete the settlement processing of large amounts of business data in a short time, improving settlement efficiency and meeting the telecommunications industry's demand for rapid settlement.

[0130] Combination Figure 2 As shown, a data settlement method of the present invention includes:

[0131] Acquire multi-source heterogeneous data from partners, including multiple business data;

[0132] Each piece of business data is standardized by field mapping and semantic validation to obtain a standard number segment corresponding to each piece of business data.

[0133] The settlement rules corresponding to the business data are determined by matching the standard digital segments corresponding to all the business data with a preset rule base.

[0134] Based on the analysis of the business requirement information corresponding to each of the business data, it is determined whether the settlement rules of the business data need to be adjusted;

[0135] When the settlement rules need to be adjusted, the settlement rules of the business data are edited and updated according to the business requirement information of the business data to obtain the latest settlement rules of the business data; when the settlement rules do not need to be adjusted, the settlement rules of the business data are used as the latest settlement rules.

[0136] Based on the latest settlement rules for each of the aforementioned business data, a detailed settlement list for the partner is generated.

[0137] The data settlement method of the present invention has the same advantages over the prior art as the aforementioned data settlement system over the prior art, and will not be repeated here.

[0138] like Figure 3 As shown, an electronic device 300 provided in this embodiment of the invention includes a memory 310 and a processor 320; the memory 310 is used to store a computer program; the processor 320 is used to implement the data settlement method described above when the computer program is executed.

[0139] The electronic device of the present invention has the same advantages over the prior art as the data settlement method described above, and will not be repeated here.

[0140] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A data settlement system, characterized by, include: The data acquisition unit is used to acquire multi-source heterogeneous data from partners, including multiple business data. A data standardization unit is used to standardize each piece of business data through field mapping and semantic verification to obtain a standard number segment corresponding to each piece of business data. The settlement rule matching unit is used to match the standard digital segments corresponding to all the business data with a preset rule library to determine the settlement rule corresponding to the business data. A business requirement judgment unit is used to analyze the business requirement information corresponding to each piece of business data to determine whether the settlement rules of the business data need to be adjusted. Specifically, this includes: acquiring the business type, business scale, number of partner users, partner user usage, and partner agreement terms corresponding to each piece of business data, and using the business type, business scale, number of partner users, partner user usage, and partner agreement terms as the business requirement information; extracting features from the business requirement information to obtain multiple key feature indicators; comprehensively analyzing all the key feature indicators and quantifying the analysis results to determine the degree of change in the partner's business requirements; comparing the degree of change value with a preset change threshold for the business data to determine whether the settlement rules of the business data need to be adjusted; wherein, when the degree of change value is greater than or equal to the preset change threshold, the settlement rules of the business data need to be adjusted; when the degree of change value is less than the preset change threshold, the settlement rules of the business data do not need to be adjusted. The rule engine editing unit is used to edit and update the settlement rules of the business data according to the business requirement information of the business data when the settlement rules need to be adjusted, to obtain the latest settlement rules of the business data; when the settlement rules do not need to be adjusted, the settlement rules of the business data are used as the latest settlement rules; specifically, it includes: when the settlement rules need to be adjusted, parsing the business requirement information to determine the business requirement change content; determining the rule change template corresponding to the business requirement change content according to the business requirement change content; generating a rule adjustment plan through the rule change template, and editing and updating the settlement rules of the business data according to the rule adjustment plan to obtain the latest settlement rules; The settlement execution unit is used to generate a settlement details list for the partner based on the latest settlement rules for each of the business data.

2. The data settlement system of claim 1, wherein, The data acquisition unit is specifically used for: Obtain the business type corresponding to each piece of business data required when settling data with the partner; Generate a data request corresponding to each of the aforementioned business types; The business data is obtained by sending the data request to the partner.

3. The data settlement system of claim 1, wherein The data standardization unit is specifically used for: Preprocess each of the business data to obtain processed business data with a unified coding format; Convert an original field name in each of the processed business data into a preset standard field name through field mapping to obtain a standard field of the business data; Perform semantic checking on the standard field, and perform format conversion on the standard field after the semantic checking to obtain the standard field of the business data in a standard data format.

4. The data settlement system of claim 1, wherein The settlement rule matching unit is specifically configured to: Extract features from the standard field corresponding to each of the business data to obtain a plurality of key business features in the standard field; Match and compare each of the key business features with a preset settlement rule in a preset rule library to obtain a matching and comparing result, wherein the matching and comparing result includes a to-be-screened rule corresponding to the plurality of business data; Screen all the to-be-screened rules in the matching and comparing result to obtain the settlement rule corresponding to the business data.

5. The data settlement system of claim 1, wherein The rule engine editing unit is specifically further configured to: Perform semantic analysis on the business demand change content through a natural language processing technology to generate a structured semantic representation; Match the structured semantic representation in a preset rule template library through a machine learning algorithm to obtain the rule change template.

6. The data settlement system of claim 1, wherein, The settlement execution unit is specifically configured to: Generate settlement content corresponding to each of the business data according to the latest settlement rule of each of the business data; Obtain the settlement detail list of the cooperation party according to the settlement content of each of the business data.

7. A data settlement method, characterized by, The method comprises: Obtain multi-source heterogeneous data of a cooperation party, wherein the multi-source heterogeneous data includes a plurality of business data; Perform standardized processing on each of the business data through field mapping and semantic checking to obtain a standard field corresponding to each of the business data; Match the standard field corresponding to all the business data with a preset rule library to determine a settlement rule corresponding to the business data; and Perform settlement on the cooperation party according to the settlement rule corresponding to the business data. According to the business demand information corresponding to each business data, it is judged whether the settlement rule of the business data needs to be adjusted; wherein, it specifically includes: obtaining the business type, business scale, number of partner users, partner user usage and partner agreement terms corresponding to each business data, and taking the business type, business scale, number of partner users, partner user usage and partner agreement terms as the business demand information; performing feature extraction on the business demand information to obtain a plurality of key feature indexes of the business demand information; comprehensively analyzing all the key feature indexes and quantitatively processing the analysis result to determine the change degree value of the business demand of the partner; comparing the change degree value with a preset change threshold value of the business data to judge whether the settlement rule of the business data needs to be adjusted; wherein, when the change degree value is greater than or equal to the preset change threshold value, the settlement rule of the business data needs to be adjusted; when the change degree value is less than the preset change threshold value, the settlement rule of the business data does not need to be adjusted; When the settlement rule needs to be adjusted, the settlement rule of the business data is edited and updated according to the business demand information of the business data to obtain the latest settlement rule of the business data; when the settlement rule does not need to be adjusted, the settlement rule of the business data is taken as the latest settlement rule; wherein, it specifically includes: when the settlement rule needs to be adjusted, the business demand information is parsed to determine the business demand change content; according to the business demand change content, a rule change template corresponding to the business demand change content is determined; a rule adjustment scheme is generated through the rule change template, and the settlement rule of the business data is edited and updated according to the rule adjustment scheme to obtain the latest settlement rule; According to the latest settlement rule of each business data, a settlement detail list of the partner is generated.

8. An electronic device, comprising: It includes a memory and a processor; The memory is used to store a computer program; The processor is used to implement the data settlement method of claim 7 when executing the computer program.

Images