Data processing method and device, computer device, and storage medium

Abstract

The embodiment of the application belongs to the field of big data, and relates to a data processing method, comprising: judging whether a data reconciliation request input by a user is received; if yes, obtaining first transaction data of a first business channel system and second transaction data of a second business channel system; obtaining a first total transaction amount corresponding to a data identifier from the first transaction data and a second total transaction amount corresponding to the data identifier from the second transaction data; if the first total transaction amount is the same as the second total transaction amount; determining a target rule engine from a rule engine set; and performing data reconciliation on the first transaction data and the second transaction data based on the target rule engine to obtain a reconciliation result. The application also provides a data processing device, a computer device and a storage medium. In addition, the application also relates to blockchain technology, and the reconciliation result can be stored in the blockchain. The application improves the reconciliation processing efficiency of transaction data and ensures the accuracy of the reconciliation result.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to data processing methods, apparatus, computer equipment and storage media. Background Technology

[0002] Data reconciliation is a business process for monitoring the consistency of data across different business systems, ensuring data accuracy across upstream and downstream systems. It is applied in various industries, including the financial sector. Because the financial industry has stringent requirements for data consistency across its various business systems, data reconciliation is an essential part of its business processes and a powerful tool for detecting inconsistencies between different systems.

[0003] Most existing data reconciliation methods still rely on manual reconciliation. However, manual reconciliation can only perform general ledger checks for daily transactions, failing to reconcile details at the individual transaction level. Furthermore, manual calculations are prone to errors and omissions in data verification. The entire reconciliation process is time-consuming and labor-intensive, resulting in low efficiency. Summary of the Invention

[0004] The purpose of this application is to provide a data processing method, apparatus, computer equipment, and storage medium to solve the problems of existing manual reconciliation methods, which can only perform general ledger verification for daily transaction scenarios and cannot achieve detailed-level reconciliation. Furthermore, manual calculations are prone to errors in data verification and omissions in scenarios. The entire reconciliation process is time-consuming and labor-intensive, resulting in low efficiency.

[0005] To address the aforementioned technical problems, this application provides a data processing method, employing the following technical solution:

[0006] Determine whether a data reconciliation request input by the user has been received; wherein the data reconciliation request carries a first system identifier, a second system identifier, and time information;

[0007] If so, obtain the first transaction data of the first business channel system corresponding to the first system identifier and the time information, and obtain the second transaction data of the second business channel system corresponding to the second system identifier and the time information;

[0008] Based on a preset data identifier, a first total transaction amount corresponding to the data identifier is obtained from the first transaction data, and a second total transaction amount corresponding to the data identifier is obtained from the second transaction data;

[0009] Determine whether the total amount of the first transaction is the same as the total amount of the second transaction.

[0010] If they are the same, the target rule engine is determined from the preset set of rule engines;

[0011] Based on the target rule engine, the first transaction data and the second transaction data are reconciled to obtain the corresponding reconciliation results.

[0012] Furthermore, the step of determining the target rule engine from the preset set of rule engines specifically includes:

[0013] Call the preset engine priority data table;

[0014] Obtain the priority value of each rule engine contained in the rule engine set from the engine priority data table;

[0015] The target rule engine is determined from all the rule engines included in the rule engine set based on the priority value of each rule engine.

[0016] Furthermore, the step of determining the target rule engine from all the rule engines included in the rule engine set based on the priority values ​​of each rule engine specifically includes:

[0017] All the rule engines are sorted in descending order of their priority values ​​to obtain the corresponding sorting results;

[0018] The first rule engine that retrieves the first position in the sorted results;

[0019] Obtain the current first usage data of the first rule engine;

[0020] Determine whether the first usage data exceeds a preset usage data threshold;

[0021] If the data usage threshold is not exceeded, then the first rule engine will be used as the target rule engine.

[0022] If the data usage threshold is exceeded, the second rule engine in the sorting result is obtained according to the sorting order within the sorting result;

[0023] If it is determined that the second usage data of the second rule engine does not exceed the usage data threshold, then the second rule engine is used as the target rule engine.

[0024] Furthermore, prior to the step of calling the preset engine priority data table, the following steps are also included:

[0025] Obtain the processing efficiency value, processing accuracy value, and evaluation value of each rule engine included in the rule engine set within a preset time period;

[0026] Obtain the first weight, the second weight, and the third weight corresponding to the processing efficiency value, the processing accuracy value, and the evaluation value, respectively;

[0027] Based on the first weight, the second weight, and the third weight, a preset calculation formula is called to calculate the processing efficiency value, processing accuracy, and evaluation value of each rule engine within the preset time period, and to generate the priority value of each rule engine.

[0028] All the aforementioned priority values ​​are stored in the engine priority data table.

[0029] Furthermore, the step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result specifically includes:

[0030] Establish a communication connection with the target rule engine;

[0031] Obtain the preset reconciliation algorithm;

[0032] The target rule engine is invoked to reconcile the first transaction data and the second transaction data according to the reconciliation algorithm, thereby obtaining the reconciliation result.

[0033] Furthermore, before the step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result, the method further includes:

[0034] Obtain the preset standard data structure format;

[0035] The first transaction data is standardized based on the standard data structure format to obtain the processed first transaction data.

[0036] The second transaction data is standardized based on the standard data structure format to obtain the processed second transaction data.

[0037] The step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result includes:

[0038] Based on the target rule engine, the processed first transaction data and the processed second transaction data are reconciled to obtain the reconciliation result.

[0039] Furthermore, after the step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result, the method further includes:

[0040] Generate reminder information corresponding to the content of the reconciliation results;

[0041] Obtain the target user's communication address information;

[0042] Based on the communication address information, the reconciliation results and the reminder information are pushed to the target user's user terminal.

[0043] To address the aforementioned technical problems, this application also provides a data processing apparatus, which employs the following technical solution:

[0044] The first judgment module is used to determine whether a data reconciliation request input by the user has been received; wherein, the data reconciliation request carries a first system identifier, a second system identifier, and time information;

[0045] The first acquisition module is configured to, if so, acquire first transaction data of a first business channel system corresponding to the first system identifier and the time information, and acquire second transaction data of a second business channel system corresponding to the second system identifier and the time information;

[0046] The second acquisition module is used to acquire a first total transaction amount corresponding to the data identifier from the first transaction data based on a preset data identifier, and to acquire a second total transaction amount corresponding to the data identifier from the second transaction data.

[0047] The second judgment module is used to determine whether the first transaction total amount is the same as the second transaction total amount;

[0048] The determination module is used to determine the target rule engine from a preset set of rule engines if they are the same.

[0049] The first processing module is used to perform data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine, and obtain the corresponding reconciliation result.

[0050] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0051] Determine whether a data reconciliation request input by the user has been received; wherein the data reconciliation request carries a first system identifier, a second system identifier, and time information;

[0052] If so, obtain the first transaction data of the first business channel system corresponding to the first system identifier and the time information, and obtain the second transaction data of the second business channel system corresponding to the second system identifier and the time information;

[0053] Based on a preset data identifier, a first total transaction amount corresponding to the data identifier is obtained from the first transaction data, and a second total transaction amount corresponding to the data identifier is obtained from the second transaction data;

[0054] Determine whether the total amount of the first transaction is the same as the total amount of the second transaction.

[0055] If they are the same, the target rule engine is determined from the preset set of rule engines;

[0056] Based on the target rule engine, the first transaction data and the second transaction data are reconciled to obtain the corresponding reconciliation results.

[0057] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0058] Determine whether a data reconciliation request input by the user has been received; wherein the data reconciliation request carries a first system identifier, a second system identifier, and time information;

[0059] If so, obtain the first transaction data of the first business channel system corresponding to the first system identifier and the time information, and obtain the second transaction data of the second business channel system corresponding to the second system identifier and the time information;

[0060] Based on a preset data identifier, a first total transaction amount corresponding to the data identifier is obtained from the first transaction data, and a second total transaction amount corresponding to the data identifier is obtained from the second transaction data;

[0061] Determine whether the total amount of the first transaction is the same as the total amount of the second transaction.

[0062] If they are the same, the target rule engine is determined from the preset set of rule engines;

[0063] Based on the target rule engine, the first transaction data and the second transaction data are reconciled to obtain the corresponding reconciliation results.

[0064] Compared with the prior art, the embodiments of this application have the following main advantages:

[0065] When a user-input data reconciliation request is received, the system first acquires the first transaction data of the first business channel system corresponding to the first system identifier and the time information, and acquires the second transaction data of the second business channel system corresponding to the second system identifier and the time information. Then, based on a preset data identifier, it acquires the first total transaction amount corresponding to the data identifier from the first transaction data, and the second total transaction amount corresponding to the data identifier from the second transaction data. Next, it determines whether the first total transaction amount and the second total transaction amount are the same. If they are the same, a target rule engine is determined from a preset set of rule engines. Finally, data reconciliation processing is performed on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result. This embodiment of the application determines the target rule engine from a preset set of rule engines, thereby enabling fast and accurate data reconciliation processing between the first and second transaction data, improving the efficiency of transaction data reconciliation processing and ensuring the accuracy of the generated reconciliation results. Attached Figure Description

[0066] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0068] Figure 2 A flowchart of an embodiment of the data processing method according to this application;

[0069] Figure 3 This is a schematic diagram of the structure of an embodiment of the data processing apparatus according to this application;

[0070] Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0072] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0073] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0074] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0075] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0076] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0077] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0078] It should be noted that the data processing method provided in the embodiments of this application is generally executed by a server / terminal device, and correspondingly, the data processing device is generally located in the server / terminal device.

[0079] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0080] Continue to refer to Figure 2 A flowchart of an embodiment of the data processing method according to this application is shown. The data processing method includes the following steps:

[0081] Step S201: Determine whether a data reconciliation request input by the user has been received.

[0082] In this embodiment, the data processing method runs on an electronic device (e.g., Figure 1 The server / terminal device shown can obtain data reconciliation requests via wired or wireless connections. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future-developed wireless connection methods. The data reconciliation request carries a first system identifier, a second system identifier, and time information. The first and second system identifiers refer to the system identifiers of the business channel system for which data reconciliation processing is required. The system identifier can refer to the system name or system ID of the business channel system. The time information refers to the time period of the transaction data to be obtained for data reconciliation processing. This embodiment can support data reconciliation processing of transaction data from different business channel systems within a specific time period. The aforementioned transaction data may include commission data.

[0083] Step S202: If yes, obtain the first transaction data of the first business channel system corresponding to the first system identifier and the time information, and obtain the second transaction data of the second business channel system corresponding to the second system identifier and the time information.

[0084] In this embodiment, the corresponding first business channel system can be determined first based on the first system identifier, and then the first transaction data within the time period corresponding to the time information can be extracted from the underwriting data of the first business channel system. Similarly, the corresponding second business channel system can be determined first based on the second system identifier, and then the second transaction data within the time period corresponding to the time information can be extracted from the underwriting data of the second business channel system. The underwriting data may include at least underwriting data for different insurance products and services such as part-time work, commissions, and allowances. The first and second business channel systems may refer to the EasyPay system and the financial certificate system, respectively.

[0085] Step S203: Based on a preset data identifier, obtain the first total transaction amount corresponding to the data identifier from the first transaction data, and obtain the second total transaction amount corresponding to the data identifier from the second transaction data.

[0086] In this embodiment, the aforementioned data identifier may refer to the total commission field. The aforementioned first transaction total may refer to the total commission in the first transaction data, and the aforementioned second transaction total may refer to the total commission in the second transaction data.

[0087] Step S204: Determine whether the first transaction total amount is the same as the second transaction total amount.

[0088] In this embodiment, if the first transaction amount is different from the second transaction amount, a reconciliation result indicating a reconciliation failure between the first transaction amount and the second transaction amount can be directly generated.

[0089] Step S205: If they are the same, determine the target rule engine from the preset set of rule engines.

[0090] In this embodiment, the specific implementation process of determining the target rule engine from the preset set of rule engines is described in more detail in the following specific embodiments, and will not be elaborated on here.

[0091] Step S206: Based on the target rule engine, perform data reconciliation processing on the first transaction data and the second transaction data to obtain the corresponding reconciliation results.

[0092] In this embodiment, the specific implementation process of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation results will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0093] When this application receives a data reconciliation request input by a user, it first obtains the first transaction data of the first business channel system corresponding to the first system identifier and the time information, and the second transaction data of the second business channel system corresponding to the second system identifier and the time information. Then, based on a preset data identifier, it obtains the first total transaction amount corresponding to the data identifier from the first transaction data, and the second total transaction amount corresponding to the data identifier from the second transaction data. Next, it determines whether the first total transaction amount and the second total transaction amount are the same. If they are the same, it determines a target rule engine from a preset set of rule engines. Finally, it performs data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result. This application, by determining a target rule engine from a preset set of rule engines, can utilize this target rule engine to quickly and accurately complete the data reconciliation processing between the first and second transaction data, improving the efficiency of transaction data reconciliation processing and ensuring the accuracy of the generated reconciliation results.

[0094] In some alternative implementations, step S205 includes the following steps:

[0095] Call the preset engine priority data table.

[0096] In this embodiment, the engine priority data table is a pre-built table that stores the priority value of each rule engine contained in each rule engine set.

[0097] Obtain the priority value of each rule engine contained in the rule engine set from the engine priority data table.

[0098] The target rule engine is determined from all the rule engines included in the rule engine set based on the priority value of each rule engine.

[0099] In this embodiment, the specific implementation process of determining the target rule engine from all the rule engines included in the rule engine set based on the priority value of each rule engine will be further described in detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0100] This application calls a preset engine priority data table, then obtains the priority value of each rule engine included in the rule engine set from the engine priority data table, and then determines the target rule engine from all the rule engines included in the rule engine set based on the priority values ​​of each rule engine. This is beneficial for using the target rule engine to quickly and accurately complete the data reconciliation processing between the first transaction data and the second transaction data, improving the efficiency of transaction data reconciliation processing and ensuring the accuracy of the generated reconciliation results.

[0101] In some optional implementations of this embodiment, determining the target rule engine from all the rule engines included in the rule engine set based on the priority values ​​of each rule engine includes the following steps:

[0102] All the rule engines are sorted in descending order of their priority values ​​to obtain the corresponding sorting results.

[0103] Obtain the first rule engine that ranks first in the sorting results.

[0104] Obtain the current first usage data of the first rule engine.

[0105] In this embodiment, the aforementioned first usage data may specifically refer to the current load data of the first rule engine.

[0106] Determine whether the first usage data exceeds a preset usage data threshold.

[0107] In this embodiment, the aforementioned usage data threshold specifically refers to the load data threshold. When the current load data of the rule engine exceeds this load data threshold, it indicates that the rule engine is currently operating under overload. The value of the aforementioned usage data threshold is not limited and can be set according to actual business needs.

[0108] If the data usage threshold is not exceeded, then the first rule engine will be used as the target rule engine.

[0109] If the data usage threshold is exceeded, the second rule engine is obtained according to the sorting order in the sorting results.

[0110] If it is determined that the second usage data of the second rule engine does not exceed the usage data threshold, then the second rule engine is used as the target rule engine.

[0111] In this embodiment, similarly, if the second usage data of the second rule engine exceeds the usage data threshold, the rule engine ranked third in the sorting results will continue to be selected for the corresponding judgment process, and so on, until the target rule engine is determined from all the rule engines included in the rule engine set.

[0112] This application sorts all rule engines in descending order of priority value to obtain a sorting result. Then, it obtains the first rule engine ranked first in the sorting result and its current first usage data. If the first usage data does not exceed a preset usage data threshold, the first rule engine is selected as the target rule engine. Otherwise, the rule engine ranked first in the sorting result and whose usage data does not exceed the usage data threshold is determined from the rule engine set and selected as the target rule engine. This application utilizes an engine priority data table to filter rule engines with larger priority values ​​and whose usage data does not exceed the usage data threshold from the rule engine set as target rule engines. This improves the intelligence and accuracy of target rule engine determination. Furthermore, by using this target rule engine, data reconciliation between the first and second transaction data can be completed quickly and accurately, improving the efficiency of transaction data reconciliation and ensuring the accuracy of the generated reconciliation results.

[0113] In some alternative implementations, prior to the step of calling the preset engine priority data table, the electronic device may also perform the following steps:

[0114] Obtain the processing efficiency, processing accuracy, and evaluation value of each rule engine included in the rule engine set within a preset time period.

[0115] In this embodiment, the value of the preset time period is not specifically limited and can be set according to actual business needs. For example, it can be set to the month preceding the current time. The processing efficiency value refers to the average processing time of all reconciliation tasks processed by the rule engine within the preset time period; the processing accuracy refers to the success rate of all reconciliation tasks processed by the rule engine within the preset time period; and the evaluation value refers to the average satisfaction level of the rule-based epidemic response obtained within the preset time period.

[0116] Obtain the first weight, the second weight, and the third weight corresponding to the processing efficiency value, the processing accuracy, and the evaluation value, respectively.

[0117] In this embodiment, the values ​​of the first weight, the second weight, and the third weight are not specifically limited and can be set according to actual business needs. Preferably, the sum of the first weight, the second weight, and the third weight is 1.

[0118] Based on the first weight, the second weight, and the third weight, a preset calculation formula is invoked to calculate the processing efficiency, processing accuracy, and evaluation value of each rule engine within the preset time period, thereby generating the priority value of each rule engine.

[0119] In this embodiment, the above calculation formula can specifically be a weighted summation calculation formula.

[0120] All the aforementioned priority values ​​are stored in the engine priority data table.

[0121] This application obtains the processing efficiency, processing accuracy, and evaluation value of each rule engine within the rule engine set within a preset time period. Then, it obtains a first weight, a second weight, and a third weight corresponding to each of these values. Based on these weights, a preset calculation formula is used to calculate the processing efficiency, accuracy, and evaluation value of each rule engine within the preset time period, generating a priority value for each rule engine. All priority values ​​are then stored in an engine priority data table. This application allows for the rapid and accurate generation of priority values ​​for each rule engine based on their processing efficiency, accuracy, and evaluation value within the rule engine set within a preset time period. The obtained priority values ​​are stored in the engine priority data table, ensuring data security and facilitating quick and convenient filtering of required rule engine priority values ​​by users or systems. This improves the acquisition speed and intelligence of priority value data, enhancing the user experience.

[0122] In some alternative implementations, step S206 includes the following steps:

[0123] Establish a communication connection with the target rule engine.

[0124] Obtain the preset reconciliation algorithm.

[0125] In this embodiment, the reconciliation algorithm may specifically include: Where Z represents the reconciliation result between the first transaction data and the second transaction data, X i T represents the first transaction data. i This indicates the second transaction data.

[0126] The target rule engine is invoked to reconcile the first transaction data and the second transaction data according to the reconciliation algorithm, thereby obtaining the reconciliation result.

[0127] This application establishes a communication connection with the target rule engine, then obtains a preset reconciliation algorithm, and then calls the target rule engine to perform data reconciliation between the first transaction data and the second transaction data according to the reconciliation algorithm, thereby obtaining the reconciliation result. This enables the use of the target rule engine and the reconciliation algorithm to quickly and accurately complete the data reconciliation processing between the first transaction data and the second transaction data, improving the efficiency of transaction data reconciliation processing and ensuring the accuracy of the generated reconciliation result.

[0128] Furthermore, in another embodiment, the aforementioned transaction data can be processed based on existing payroll calculation algorithms to generate agent payslips. By caching different tax rates (value-added tax, surtax, taxable income) and the agent's unique identifier (agent's ID number + organization), database interaction can be reduced. A commission percentage allocation algorithm and unified scheduling of individual income tax calculation rules can be adopted to achieve consolidated tax calculation for different contracted agents' unique identifiers. Specifically, the unified scheduling of the commission percentage allocation algorithm and rules refers to identifying agents requiring consolidated tax calculation based on their unique identifiers, and calling the individual income tax calculation rule engine by inputting individual income tax calculation factors such as total commission, signing date, ID number, and organization to obtain output individual income tax, value-added tax, surtax, and net pay. The actual individual income tax, value-added tax, surtax, and net commission amount for each agent are calculated based on the commission percentage of the agents requiring consolidated tax calculation, thereby achieving consolidated tax calculation and tax allocation. In this embodiment, the technical problems of cumbersome code stacking, complex processes, difficulty in secondary maintenance, poor performance, failure to meet the consolidated tax calculation rules for the same agent, and non-compliance with insurance regulatory and relevant tax policies can be effectively solved.

[0129] In some optional implementations of this embodiment, before step S206, the electronic device may further perform the following steps:

[0130] Obtain the preset standard data structure format.

[0131] In this embodiment, the transaction data sent by different business channel systems has different data formats. For example, some systems record transaction data in tabular form, while others record it in a non-tabular form; some systems use order numbers to represent transaction serial numbers, while other systems use business serial numbers directly; that is, the data format of transaction data sent by different business channel systems can be set according to the channel's own transaction contract, which refers to the regulations on the data format of the transaction data. The content of the standard data structure format is not specifically limited and can be set according to actual business needs.

[0132] The first transaction data is standardized based on the standard data structure format to obtain the processed first transaction data.

[0133] In this embodiment, the first transaction data is standardized by using the standard data structure format, so that the processed first transaction data has the same data structure format as the standard data structure format.

[0134] The second transaction data is standardized based on the standard data structure format to obtain the processed second transaction data.

[0135] In this embodiment, the second transaction data is standardized by using the standard data structure format, so that the processed second transaction data has the same data structure format as the standard data structure format.

[0136] Step S206 includes the following steps:

[0137] Based on the target rule engine, the processed first transaction data and the processed second transaction data are reconciled to obtain the reconciliation result.

[0138] In this embodiment, by standardizing the format of the first transaction data and the second transaction data, the problem of inconsistent data formats in the reconciliation data can be solved, thereby improving the timeliness of the reconciliation data.

[0139] This application obtains a preset standard data structure format, then performs format standardization processing on the first transaction data based on the standard data structure format to obtain processed first transaction data, and performs format standardization processing on the second transaction data based on the standard data structure format to obtain processed second transaction data. This can effectively solve the problem of inconsistent data formats in reconciliation data, enabling subsequent data reconciliation between the processed first transaction data and the processed second transaction data based on the target rule engine to obtain the reconciliation result. This can effectively improve the timeliness of the reconciliation data and ensure the accuracy of the obtained reconciliation result.

[0140] In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:

[0141] Generate reminder information corresponding to the content of the reconciliation results.

[0142] In this embodiment, the reconciliation results include reconciliation success or reconciliation failure. Correspondingly, the reminder information associated with a reconciliation success result is a reconciliation success notification, and the reminder information associated with a reconciliation failure result is a reconciliation failure warning. The reminder information associated with different reconciliation result pairs can be pre-generated and stored according to actual business needs.

[0143] Obtain the target user's communication address information.

[0144] In this embodiment, the target user can be the user mentioned above, or it can also be the maintenance personnel related to the reconciliation data. The communication address information may include an email address or a mobile phone number.

[0145] Based on the communication address information, the reconciliation results and the reminder information are pushed to the target user's user terminal.

[0146] After performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation results, this application further generates reminder information corresponding to the content of the reconciliation results and obtains the target user's communication address information. Then, based on the communication address information, the reconciliation results and the reminder information are pushed to the target user's user terminal, so that the target user can clearly understand the reconciliation status between the transaction data according to the obtained reconciliation results and perform corresponding processing measures on the transaction data according to the obtained reminder information, thereby improving the user experience of the target user.

[0147] It should be emphasized that, to further ensure the privacy and security of the above reconciliation results, the reconciliation results can also be stored in a node of a blockchain.

[0148] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0149] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0150] Foundational technologies in artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0151] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0152] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0153] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a data processing apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0154] like Figure 3 As shown, the data processing device 300 described in this embodiment includes: a first judgment module 301, a first acquisition module 302, a second acquisition module 303, a second judgment module 304, a determination module 305, and a first processing module 306. Wherein:

[0155] The first judgment module 301 is used to determine whether a data reconciliation request input by the user has been received; wherein the data reconciliation request carries a first system identifier, a second system identifier, and time information;

[0156] The first acquisition module 302 is configured to, if so, acquire first transaction data of the first business channel system corresponding to the first system identifier and the time information, and acquire second transaction data of the second business channel system corresponding to the second system identifier and the time information;

[0157] The second acquisition module 303 is used to acquire a first total transaction amount corresponding to the data identifier from the first transaction data based on a preset data identifier, and to acquire a second total transaction amount corresponding to the data identifier from the second transaction data.

[0158] The second judgment module 304 is used to determine whether the first transaction total amount is the same as the second transaction total amount;

[0159] The determination module 305 is used to determine the target rule engine from the preset set of rule engines if they are the same.

[0160] The first processing module 306 is used to perform data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result.

[0161] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0162] In some optional implementations of this embodiment, the determining module 305 includes:

[0163] Call the submodule to access the preset engine priority data table;

[0164] The first acquisition submodule is used to acquire the priority value of each rule engine contained in the rule engine set from the engine priority data table;

[0165] A determination submodule is used to determine the target rule engine from all the rule engines included in the rule engine set based on the priority value of each rule engine.

[0166] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0167] In some optional implementations of this embodiment, the determined sub-module includes:

[0168] A sorting unit is used to sort all the rule engines in descending order of the priority values ​​to obtain the corresponding sorting results;

[0169] The first acquisition unit is used to acquire the first rule engine that ranks the first position in the sorting result;

[0170] The second acquisition unit is used to acquire the current first usage data of the first rule engine;

[0171] The judgment unit is used to determine whether the first usage data exceeds a preset usage data threshold.

[0172] The first determining unit is configured to use the first rule engine as the target rule engine if the data usage threshold is not exceeded.

[0173] The third acquisition unit is used to acquire the second rule engine in the sorting result according to the sorting order if the data usage threshold is exceeded.

[0174] The second determining unit is configured to, if it is determined that the second usage data of the second rule engine does not exceed the usage data threshold, then designate the second rule engine as the target rule engine.

[0175] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0176] In some optional implementations of this embodiment, the determining module 305 further includes:

[0177] The second acquisition submodule is used to acquire the processing efficiency value, processing accuracy value and evaluation value of each rule engine included in the rule engine set within a preset time period.

[0178] The third acquisition submodule is used to acquire the first weight, the second weight, and the third weight corresponding to the processing efficiency value, the processing accuracy value, and the evaluation value, respectively.

[0179] The calculation submodule is used to calculate the processing efficiency, processing accuracy and evaluation value of each rule engine within the preset time period based on the first weight, the second weight and the third weight, and to generate the priority value of each rule engine.

[0180] The storage submodule is used to store all the priority values ​​into the engine priority data table.

[0181] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0182] In some optional implementations of this embodiment, the first processing module 306 includes:

[0183] Create a submodule to establish a communication connection with the target rule engine;

[0184] The fourth submodule is used to obtain the preset reconciliation algorithm;

[0185] The first processing submodule is used to call the target rule engine to perform data reconciliation between the first transaction data and the second transaction data according to the reconciliation algorithm, and obtain the reconciliation result.

[0186] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0187] In some optional implementations of this embodiment, the data processing apparatus further includes:

[0188] The third acquisition module is used to acquire preset standard data structure formats;

[0189] The second processing module is used to perform format standardization processing on the first transaction data based on the standard data structure format to obtain the processed first transaction data.

[0190] The third processing module is used to perform format standardization processing on the second transaction data based on the standard data structure format to obtain the processed second transaction data.

[0191] The first processing module 306 includes:

[0192] The second processing submodule is used to perform data reconciliation between the processed first transaction data and the processed second transaction data based on the target rule engine, and obtain the reconciliation result.

[0193] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0194] In some optional implementations of this embodiment, the data processing device further includes:

[0195] The generation module is used to generate reminder information corresponding to the content of the reconciliation results;

[0196] The fourth acquisition module is used to acquire the target user's communication address information;

[0197] The sending module is used to push the reconciliation result and the reminder information to the target user's user terminal based on the communication address information.

[0198] In this embodiment, the operations performed by the above modules or units correspond one-to-one with the steps of the data processing method in the aforementioned embodiments, and will not be repeated here.

[0199] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0200] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that... Figure 4 Only a computer device 4 with components 41-43 is shown in the illustration. However, it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0201] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0202] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for data processing methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0203] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the data processing method.

[0204] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0205] Compared with the prior art, the embodiments of this application have the following main advantages:

[0206] In this embodiment, when a user-input data reconciliation request is received, the system first acquires first transaction data from a first business channel system corresponding to the first system identifier and the time information, and second transaction data from a second business channel system corresponding to the second system identifier and the time information. Then, based on a preset data identifier, the system acquires a first total transaction amount corresponding to the data identifier from the first transaction data, and a second total transaction amount corresponding to the data identifier from the second transaction data. Next, it determines whether the first total transaction amount and the second total transaction amount are the same. If they are the same, a target rule engine is determined from a preset set of rule engines. Finally, data reconciliation processing is performed on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result. This embodiment, by determining a target rule engine from a preset set of rule engines, can utilize this target rule engine to quickly and accurately complete the data reconciliation processing between the first and second transaction data, improving the efficiency of transaction data reconciliation processing and ensuring the accuracy of the generated reconciliation results.

[0207] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the data processing method described above.

[0208] Compared with the prior art, the embodiments of this application have the following main advantages:

[0209] In this embodiment, when a user-input data reconciliation request is received, the system first acquires first transaction data from a first business channel system corresponding to the first system identifier and the time information, and second transaction data from a second business channel system corresponding to the second system identifier and the time information. Then, based on a preset data identifier, the system acquires a first total transaction amount corresponding to the data identifier from the first transaction data, and a second total transaction amount corresponding to the data identifier from the second transaction data. Next, it determines whether the first total transaction amount and the second total transaction amount are the same. If they are the same, a target rule engine is determined from a preset set of rule engines. Finally, data reconciliation processing is performed on the first transaction data and the second transaction data based on the target rule engine to obtain the corresponding reconciliation result. This embodiment, by determining a target rule engine from a preset set of rule engines, can utilize this target rule engine to quickly and accurately complete the data reconciliation processing between the first and second transaction data, improving the efficiency of transaction data reconciliation processing and ensuring the accuracy of the generated reconciliation results.

[0210] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0211] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A data processing method, characterized by, The method comprises the following steps: determining whether a user input data reconciliation request is received; wherein the data reconciliation request carries a first system identifier, a second system identifier, and time information; if so, obtaining first transaction data of a first business channel system corresponding to the first system identifier and the time information, and obtaining second transaction data of a second business channel system corresponding to the second system identifier and the time information; based on a preset data identifier, obtaining a first transaction total amount corresponding to the data identifier from the first transaction data, and obtaining a second transaction total amount corresponding to the data identifier from the second transaction data; determining whether the first transaction total amount and the second transaction total amount are the same; if so, determining a target rule engine from a preset rule engine set; based on the target rule engine, performing data reconciliation processing on the first transaction data and the second transaction data to obtain a corresponding reconciliation result; wherein the step of determining the target rule engine from the preset rule engine set comprises: calling a preset engine priority data table; obtaining a priority value of each rule engine included in the rule engine set from the engine priority data table; based on the priority value of each rule engine, determining the target rule engine from all rule engines included in the rule engine set; wherein before the step of calling the preset engine priority data table, the method further comprises: obtaining a processing efficiency value, a processing accuracy, and an evaluation value of each rule engine included in the rule engine set within a preset time period; obtaining a first weight, a second weight, and a third weight corresponding to the processing efficiency value, the processing accuracy, and the evaluation value, respectively; based on the first weight, the second weight, and the third weight, calling a preset calculation formula to calculate the processing efficiency value, the processing accuracy, and the evaluation value of each rule engine within the preset time period to generate a priority value of each rule engine; storing all the priority values in the engine priority data table; wherein the processing efficiency value refers to the average processing time of all reconciliation tasks processed by the rule engine within the preset time period, the processing accuracy refers to the processing success rate of all reconciliation tasks processed by the rule engine within the preset time period, and the evaluation value refers to the average value of the evaluation satisfaction of the rule engine within the preset time period; wherein the step of determining the target rule engine from all rule engines included in the rule engine set based on the priority value of each rule engine comprises: sorting all rule engines in descending order of the priority value to obtain a corresponding sorting result; obtaining a first rule engine at the top of the sorting result; obtaining current first usage data of the first rule engine; determining whether the first usage data exceeds a preset usage data threshold; if the first usage data does not exceed the usage data threshold, the first rule engine is used as the target rule engine. if the usage data threshold is exceeded, a second rule engine ranked second in the ranking result is obtained according to the ranking order in the ranking result; if it is determined that the second usage data of the second rule engine does not exceed the usage data threshold, the second rule engine is taken as the target rule engine; wherein the first usage data specifically refers to the current load data of the first rule engine, and the usage data threshold specifically refers to the load data threshold.

2. The data processing method according to claim 1, characterized in that, The step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain a corresponding reconciliation result specifically includes: establishing a communication connection with the target rule engine; obtaining a preset reconciliation algorithm; calling the target rule engine to perform data reconciliation on the first transaction data and the second transaction data according to the reconciliation algorithm to obtain the reconciliation result.

3. The data processing method of claim 1, wherein, Before the step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain a corresponding reconciliation result, it further includes: obtaining a preset standard data structure format; performing format standardization processing on the first transaction data based on the standard data structure format to obtain processed first transaction data; performing format standardization processing on the second transaction data based on the standard data structure format to obtain processed second transaction data; The step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain a corresponding reconciliation result includes: performing data reconciliation on the processed first transaction data and the processed second transaction data based on the target rule engine to obtain the reconciliation result.

4. The data processing method of claim 1, wherein, After the step of performing data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine to obtain a corresponding reconciliation result, it further includes: generating reminder information corresponding to the content of the reconciliation result; obtaining the communication address information of the target user; based on the communication address information, pushing the reconciliation result and the reminder information to the user terminal of the target user.

5. A data processing apparatus, characterized by, It includes: The first judgment module is used for judging whether a data reconciliation request input by a user is received; wherein the data reconciliation request carries a first system identifier, a second system identifier and time information; The first acquisition module is used for acquiring first transaction data of a first business channel system corresponding to the first system identifier and the time information, and acquiring second transaction data of a second business channel system corresponding to the second system identifier and the time information if the data reconciliation request is received; The second acquisition module is used for acquiring a first transaction total amount corresponding to the data identifier from the first transaction data and a second transaction total amount corresponding to the data identifier from the second transaction data based on a preset data identifier; The second judgment module is used for judging whether the first transaction total amount and the second transaction total amount are the same; The determination module is used for determining a target rule engine from a preset rule engine set if the first transaction total amount and the second transaction total amount are the same; The first processing module is configured to perform data reconciliation processing on the first transaction data and the second transaction data based on the target rule engine, to obtain a corresponding reconciliation result. The determination module includes: The calling submodule is configured to call a preset engine priority data table. The first obtaining submodule is configured to obtain, from the engine priority data table, a priority value of each rule engine included in the rule engine set. The determination submodule is configured to determine the target rule engine from all the rule engines included in the rule engine set based on the priority values of the rule engines. The determination module further includes: The second obtaining submodule is configured to obtain, for each rule engine included in the rule engine set, a processing efficiency value, a processing accuracy, and an evaluation value within a preset time period. The third obtaining submodule is configured to obtain a first weight, a second weight, and a third weight corresponding to the processing efficiency value, the processing accuracy, and the evaluation value, respectively. The calculation submodule is configured to calculate the processing efficiency value, the processing accuracy, and the evaluation value of each rule engine within the preset time period based on the first weight, the second weight, and the third weight, and generate a priority value of each rule engine by calling a preset calculation formula. The storage submodule is configured to store all the priority values in the engine priority data table. The processing efficiency value refers to the average processing time of all reconciliation tasks processed by a rule engine within the preset time period, the processing accuracy refers to the processing success rate of all reconciliation tasks processed by a rule engine within the preset time period, and the evaluation value refers to the average value of the evaluation satisfaction of the rule engine within the preset time period. The determination submodule includes: The sorting unit is configured to sort all the rule engines in descending order of the priority values to obtain a corresponding sorting result. The first obtaining unit is configured to obtain a first rule engine ranked first in the sorting result. The second obtaining unit is configured to obtain a first usage data of the first rule engine. The judgment unit is configured to determine whether the first usage data exceeds a preset usage data threshold. The first determination unit is configured to determine the first rule engine as the target rule engine if the first usage data does not exceed the usage data threshold. The third obtaining unit is configured to obtain a second rule engine ranked second in the sorting result in the arrangement order of the sorting result if the first usage data exceeds the usage data threshold. The second determination unit is configured to determine the second rule engine as the target rule engine if a second usage data of the second rule engine is determined not to exceed the usage data threshold. The first usage data specifically refers to the current load data of the first rule engine, and the usage data threshold specifically refers to a load data threshold.

6. A computer device comprising a memory and a processor, wherein the memory stores computer readable instructions, and the processor executes the computer readable instructions to implement the steps of the data processing method according to any one of claims 1 to 4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores computer readable instructions which, when executed by the processor, implement the steps of the data processing method according to any one of claims 1 to 4.

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