Financial Data Synchronization Method, System, Computer Device, and Storage Medium

Abstract

The present invention relates to the field of data processing technology, specifically to a financial data synchronization method, system, computer equipment and storage medium, comprising the following steps: based on input financial data, pre-processing the data through formatting and data cleaning, analyzing the capital flow data, performing consistency verification on the capital data, identifying and recording inconsistent data, and generating consistency verification results. In the present invention, by analyzing the data and extracting the incremental data that needs to be synchronized, the synchronization efficiency is improved, the real-time update of the data is ensured, the synchronization task time is predicted in combination with the computing resource demand and the real-time system load, the synchronization priority is calculated, the rationality of resource use is improved, the synchronized data is fully verified, and the missing, duplicated and inconsistent data are discovered and corrected in time, the accuracy and consistency of the financial data are ensured, the efficiency of financial data synchronization is improved, the accurate and timely transmission and update of the data are ensured, and the financial management process is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a financial data synchronization method, system, computer device, and storage medium. Background Art

[0002] The technical field of data processing involves multiple processes of collecting, organizing, storing, analyzing, and transmitting raw data, aiming to improve the effectiveness, accuracy, and value of data, including data mining, data cleaning, data transformation, data integration, and data synchronization, involving database management, distributed computing, machine learning, artificial intelligence, real-time data processing, and big data technology, and is applied to multiple industries such as enterprise information management, finance, healthcare, the Internet, and government, aiming to support decision-making, optimize operational processes, and enhance data-driven business effects through efficient data flow and analysis.

[0003] Among them, the financial data synchronization method is used to ensure data consistency and real-time update between multiple financial systems. Through various technical means, including API interfaces, batch synchronization, and real-time synchronization, it ensures the accurate and timely transmission and update of financial data stored in different systems, avoids problems of data inconsistency or lag, improves the efficiency, accuracy, and transparency of enterprise financial management, reduces errors in manual data entry, enhances the basis for financial decision-making, and improves the accuracy and timeliness of financial statements.

[0004] Traditional financial data synchronization methods rely on batch synchronization and traditional timed synchronization methods, unable to capture data changes in real time, resulting in excessive system load, transmitting a large amount of redundant data, affecting the efficiency of data synchronization, lacking precise synchronization control between multiple financial systems, leading to data consistency problems, affecting the accuracy of financial statements, and lacking dynamic adjustment of real-time system load in the priority management of synchronization tasks, resulting in unreasonable execution order and resource allocation of tasks, reducing synchronization efficiency and resource utilization. Summary of the Invention

[0005] To solve the technical problems existing in the prior art, embodiments of the present invention provide a financial data synchronization method, system, computer device, and storage medium. The technical solutions are as follows:

[0006] On the one hand, a financial data synchronization method is provided, and the method includes:

[0007] S1: Based on the input financial data, through formatting and data cleaning, preprocess the data, analyze the fund flow data, perform consistency verification on the fund data, identify and record inconsistent data, and generate a consistency verification result;

[0008] S2: Based on the consistency check result, classify the financial data by analyzing the amount, timestamp, and account type information of the financial data to generate a financial data classification result;

[0009] S3: Based on the financial data classification result, identify and extract the incremental data that needs to be synchronized by comparing the differences in multiple data fields in the financial data, and package the target data to generate an incremental data packet;

[0010] S4: Based on the incremental data packet, predict the execution time of multiple tasks according to the computing resource requirements of the data synchronization task and the real-time system load, and combine the urgency and sensitivity of multiple transaction types to calculate the synchronization priority of multiple pieces of data to generate a synchronization task priority queue;

[0011] S5: Based on the synchronization task priority queue, analyze the synchronized data, verify the integrity of the data, identify data missing, duplicate, and data inconsistency, and correct the abnormal data to generate a financial data synchronization record.

[0012] As a further solution of the present invention, the consistency check result includes fund flow data, data anomaly identification, and timestamp consistency check result. The financial data classification result includes revenue financial data, expenditure financial data, and accounts payable financial data. The incremental data packet includes incremental data fields, difference data records, and incremental synchronization marks. The synchronization task priority queue includes task execution time estimation information, task priority level, and system load prediction information. The financial data synchronization record includes a synchronized data set, a data correction record, and a synchronized abnormal data detection result.

[0013] As a further solution of the present invention, based on the input financial data, preprocess the data through formatting and data cleaning. The steps of performing consistency check on the fund data by analyzing the fund flow data, identifying and recording inconsistent data, and generating a consistency check result are specifically as follows:

[0014] S101: Based on the input financial data, perform formatting and data cleaning on the data, including standardizing the amount, account, and timestamp fields, and adjusting the currency unit and time format to generate a data preprocessing result;

[0015] S102: According to the data preprocessing result, analyze the fund flow data, and identify non-compliant transaction data by comparing the fund flow direction and financial rules to generate a fund flow analysis result;

[0016] S103: Based on the results of the fund flow analysis, verify the consistency of the data, including amount balance, fund flow rules between accounts, and timestamp sorting, identify and mark abnormal data, and generate a consistency verification result.

[0017] As a further solution of the present invention, based on the consistency verification result, by analyzing the amount, timestamp, and account type information of the financial data, the steps of classifying the financial data and generating a financial data classification result are specifically as follows:

[0018] S201: Based on the consistency verification result, extract the amounts and timestamps of multiple pieces of financial data to generate a basic information extraction result;

[0019] S202: Based on the basic information extraction result, identify the outflow account and inflow account of the funds, analyze the flow direction and business type of the funds between accounts, and generate fund type information;

[0020] S203: Based on the fund type information, classify multiple pieces of financial data, including income, expenditure, and accounts payable, to generate a financial data classification result.

[0021] As a further solution of the present invention, based on the financial data classification result, by comparing the differences in multiple data fields in the financial data, identify and extract the incremental data that needs to be synchronized, package the target data, and the steps of generating an incremental data packet are specifically as follows:

[0022] S301: Based on the financial data classification result, extract the amount, account, and transaction type field information of multiple pieces of data, identify and mark the changed data points through data comparison, and generate changed data identification information;

[0023] S302: Based on the changed data identification information, identify the incremental data that needs to be synchronized to generate a synchronization processing data set;

[0024] S303: Based on the synchronization processing data set, package the incremental data according to the requirements of the data transmission for the data packet capacity to generate an incremental data packet.

[0025] As a further solution of the present invention, based on the incremental data packet, predict the execution time of multiple tasks according to the computing resource requirements of the data synchronization task and the real-time system load, combine the urgency and sensitivity of multiple transaction types, calculate the synchronization priority of multiple pieces of data, and the steps of generating a synchronization task priority queue are specifically as follows:

[0026] S401: Based on the incremental data packet, evaluate the computing resources required for multiple synchronization tasks to generate a resource requirement evaluation result;

[0027] S402: Based on the resource requirement assessment result, combined with the real-time system load information, evaluate and predict the actual execution time of multiple synchronization tasks, and generate an execution time prediction result;

[0028] S403: Based on the execution time prediction result, combined with the urgency and sensitivity of multiple transaction types, calculate the processing priorities of multiple synchronization tasks, and generate a synchronization task priority queue.

[0029] As a further solution of the present invention, the specific formula for calculating the processing priorities of multiple synchronization tasks is:

[0030] ;

[0031] where P represents the priority score value of the synchronization task, U represents the urgency characteristic vector value of the task, S represents the business sensitivity characteristic vector value, T represents the standardized predicted execution time, represents the deviation value of the current system load from the benchmark load, N represents the number of same-type tasks waiting to be executed in the current queue, represents the comprehensive weight coefficient of the characteristic vector, represents the time impact adjustment coefficient, represents the queue saturation balance factor.

[0032] As a further solution of the present invention, S501: Based on the synchronization task priority queue, analyze the synchronized data, verify the integrity of the data, and generate data integrity verification information;

[0033] S502: Based on the data integrity verification information, mark duplicate data by comparing multiple key fields, including timestamp, amount, and account, and generate a duplicate data identification result;

[0034] S503: Based on the duplicate data identification result, correct the abnormal data according to the type of abnormal data fields, and generate a financial data synchronization record.

[0035] As a further solution of the present invention, the specific formula for verifying the integrity of the data is:

[0036] ;

[0037] where, represents the data integrity evaluation index, represents the total number of data fields for which data synchronization is completed, represents the total number of data fields to be synchronized, represents the number of successfully synchronized financial data records, represents the total number of data records to be synchronized, Represents the number of detected missing data fields, Represents the total number of critical data fields for verification, Represents the data timestamp continuity ratio, Represents the data field integrity weight coefficient, Represents the data record integrity weight coefficient, Represents the missing data impact weight coefficient.

[0038] On the other hand, a financial data synchronization system is provided. This system is applied to the financial data synchronization method and includes:

[0039] The data preprocessing module formats and cleans the data based on the input financial data. By analyzing the amounts, account balances, and fund flows of multiple financial data, it identifies and records inconsistent data, and generates a consistency verification result;

[0040] The data type analysis module extracts the amounts, timestamps, and account type information from the financial data based on the consistency verification result, and analyzes the data according to the preset classification rules, classifies the data into multiple types, and generates a financial data classification result;

[0041] The incremental synchronization processing module identifies the incremental data that needs to be synchronized and packs it based on the financial data classification result by comparing the differences of multiple data fields, and generates an incremental data packet;

[0042] The task queue adjustment module evaluates and predicts the execution times of multiple tasks based on the incremental data packet according to the computing resource requirements of the data synchronization task and the real-time system load, and combines the urgency and sensitivity of multiple transaction types to calculate the synchronization priorities of multiple tasks, and generates a synchronization task priority queue;

[0043] The synchronized data correction module analyzes the integrity of the data after synchronization based on the synchronization task priority queue, identifies data missing, duplicate, and data inconsistency, corrects the abnormal data, and generates a financial data synchronization record.

[0044] On the other hand, a financial data synchronization device is provided. The financial data synchronization device includes: a processor; a memory, and computer-readable instructions are stored on the memory. When the computer-readable instructions are executed by the processor, any one of the methods in the above financial data synchronization method is implemented.

[0045] On the other hand, a computer-readable storage medium is provided. At least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by the processor to implement any one of the methods in the above financial data synchronization method.

[0046] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0047] By analyzing data and extracting incremental data that needs to be synchronized, the synchronization efficiency is improved to ensure real-time data updates. Combining the computing resource requirements with the real-time system load for predicting the synchronization task time, calculating the synchronization priority, and enhancing the rationality of resource usage. The synchronized data undergoes comprehensive verification to promptly detect and correct data missing, duplication, and inconsistencies, ensuring the accuracy and consistency of financial data, improving the efficiency of financial data synchronization, ensuring accurate and timely transmission and update of data, and optimizing the financial management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0049] Figure 1 It is a schematic diagram of the working process of the present invention;

[0050] Figure 2 It is a detailed flowchart of S1 of the present invention;

[0051] Figure 3 It is a detailed flowchart of S2 of the present invention;

[0052] Figure 4 It is a detailed flowchart of S3 of the present invention;

[0053] Figure 5 It is a detailed flowchart of S4 of the present invention;

[0054] Figure 6 It is a detailed flowchart of S5 of the present invention;

[0055] Figure 7 It is a system flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following describes the technical solutions in the present invention with reference to the drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, their intended meanings are the same. "of", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, their intended meanings are the same.

[0059] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, their intended meanings are the same.

[0060] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0061] The embodiments of the present invention provide a financial data synchronization method, as Figure 1 shown in the flowchart of the financial data synchronization method. The processing flow of this method can include the following steps:

[0062] S1: Based on the input financial data, through formatting and data cleaning, preprocess the data. By analyzing the fund flow data, perform consistency verification on the fund data, identify and record inconsistent data, and generate a consistency verification result;

[0063] S2: Based on the consistency verification result, classify the financial data by analyzing the amount, timestamp, and account type information of the financial data, and generate a financial data classification result;

[0064] S3: Based on the financial data classification result, by comparing the differences in multiple data fields in the financial data, identify and extract the incremental data that needs to be synchronized, package the target data, and generate an incremental data packet;

[0065] S4: Based on the incremental data packet, according to the computing resource requirements of the data synchronization task and the real-time system load, predict the execution time of multiple tasks, and combine the urgency and sensitivity of multiple transaction types to calculate the synchronization priorities of multiple pieces of data, and generate a synchronization task priority queue;

[0066] S5: Based on the synchronization task priority queue, analyze the synchronized data, verify the integrity of the data, identify data missing, duplicate, and inconsistent data, and correct the abnormal data to generate a financial data synchronization record.

[0067] The consistency check results include fund flow data, data anomaly flags, and timestamp consistency check results. The financial data classification results include revenue financial data, expenditure financial data, and accounts payable financial data. The incremental data packet includes incremental data fields, difference data records, and incremental synchronization markers. The synchronization task priority queue includes estimated task execution time information, task priority levels, and system load prediction information. The financial data synchronization record includes synchronized data sets, data correction records, and synchronized anomaly data detection results.

[0068] Please refer to Figure 2 , based on the input financial data, through formatting and data cleaning, preprocess the data. The steps for performing consistency check on the fund data by analyzing the fund flow data, identifying and recording inconsistent data, and generating the consistency check results are as follows:

[0069] S101: Based on the input financial data, format and clean the data, including standardizing the amount, account, and timestamp fields, and adjusting the currency unit and time format, to generate the data preprocessing results;

[0070] In the sub-step of S101, for the amount field, use a currency unit conversion algorithm based on the market exchange rate to uniformly convert all currency units to a unified benchmark currency and ensure that it complies with the currency specifications of international financial accounting standards. When standardizing the account field, use the predefined account type mapping table in the database to check whether all account formats comply with the account coding standards of the international financial system. The standardization of the timestamp field is achieved by unifying the time information into the ISO 8601 format to ensure its accurate representation of the date and time and avoid incorrect data caused by time zone or format differences. Fill in the null values for all fields and use a filling method based on domain knowledge to supplement the null values. After processing through the target steps, the generated data preprocessing results are the cleaned financial data set.

[0071] S102: According to the data preprocessing results, analyze the fund flow data, identify non-compliant transaction data by comparing the fund flow direction and financial rules, and generate the fund flow analysis results;

[0072] In sub-step S102, the financial rule model is used to compare and match the fund flow direction of each transaction. The model uses a decision tree algorithm based on a rule engine. By setting predefined fund flow rules, it ensures that each transaction conforms to the fund flow direction of its corresponding account. Data mining techniques such as association rule mining are used to further analyze the potential correlations of fund flows and identify potential non-compliant transactions, such as cross-border fund flows or fund transfers between unauthorized accounts. Through target analysis, a fund flow analysis result is generated, marking the transaction data that does not conform to the predefined financial rules and outputting the corresponding non-compliance flag. This analysis result provides a basis for subsequent abnormal data processing and fund flow optimization.

[0073] S103: Based on the fund flow analysis result, verify the data consistency, including amount balance, fund flow rules between accounts, and timestamp sorting, identify and mark abnormal data, and generate a consistency verification result;

[0074] In sub-step S103, check whether the amount balance in the data meets the transaction rules, that is, ensure that the amount in each fund flow transaction matches the transfer and balance between accounts. Use an amount balance verification algorithm for checking. The algorithm is based on the comparison of the inflow and outflow amounts of accounts in each transaction to ensure that there are no balance errors in all fund flow transactions. Verify whether the fund flow rules between accounts comply with the predefined financial requirements. Use a rule verification model to compare the transfer rules between accounts to ensure that each fund flow conforms to the agreed payment and receipt rules, such as the funds in the fund outflow account are sufficient to pay the fund inflow account. Check the timeliness of the data through a timestamp sorting algorithm to ensure that each transaction is in the correct chronological order and avoid data chronological errors. Mark and record the transactions that do not conform to the rules, generate a consistency verification result, and output the identified abnormal data as a basis for subsequent data repair or troubleshooting.

[0075] Please refer to Figure 3 , based on the consistency verification result, by analyzing the amount, timestamp, and account type information of the financial data, the steps for classifying the financial data to generate a financial data classification result are as follows:

[0076] S201: Based on the consistency verification result, extract the amount and timestamp of multiple financial data to generate a basic information extraction result;

[0077] In sub-step S201, the extraction of the amount field is carried out through the financial data standardization system. The system extracts the amount from the original financial records according to the preset rules, converts the amount into a unified currency unit, and applies the currency unit conversion model in the process. Based on the real-time exchange rate data, the amounts in different currencies are standardized into the target currency. The extraction of the timestamp field is carried out through the time parsing model, which converts the original timestamp data into the ISO 8601 standard format to ensure the consistency of the time stamps of each piece of data. All the extracted amount and timestamp information will be summarized as the basic information extraction result, forming a financial data set that meets the specifications. The result provides the basic data support for further analyzing the fund flow, account identification, and classification.

[0078] S202: Based on the basic information extraction result, identify the outflow account and inflow account of the funds, analyze the flow direction and business type of the funds between the accounts, and generate the fund type information.

[0079] In sub-step S202, by comparing the account fields in the extracted financial data with the pre-defined account coding database for matching, the outflow account and inflow account of each transaction are determined, and the flow direction of the funds between the accounts is analyzed. The fund flow analysis model is adopted. By comparing the amount, account, and timestamp data in the transaction, the outflow and inflow directions of the funds are identified. The model makes judgments according to the fund flow direction set by the rule engine to ensure that the flow of each transaction conforms to the predetermined rules. According to the fund flow direction of the transaction, the business type of each transaction is identified. The business type analysis is carried out through the classification algorithm based on the decision tree. The model determines which business type the transaction belongs to according to the marks and features in the historical data. After the analysis is completed, the fund type information is generated for subsequent fund flow and financial data classification processing.

[0080] S203: Based on the fund type information, classify multiple pieces of financial data, including income, expenditure, and accounts payable, and generate the financial data classification result.

[0081] In sub-step S203, the financial classification model is used. Based on the known business type information, the classification algorithm classifies each piece of financial data according to the fund flow direction, transaction amount, and business characteristics. During the classification process, the model performs data clustering through the support vector machine algorithm, automatically learns according to the characteristics of categories such as income, expenditure, and accounts payable in the historical financial data, and adjusts the classification rules to adapt to the changing financial data. During the determination process of each category, the classification result is verified according to the set financial rules to ensure that each piece of data is accurately classified into the corresponding category, generating the financial data classification result, which includes the transaction data of each category and its corresponding classification labels, and this classification result is used for subsequent financial report generation and analysis.

[0082] Please refer to Figure 4, based on the financial data classification result, by comparing the differences in multiple data fields in the financial data, the steps of identifying and extracting the incremental data that needs to be synchronized, and packing the target data to generate an incremental data packet are specifically as follows:

[0083] S301: Based on the financial data classification result, extract the amount, account, and transaction type field information of multiple pieces of data. Through data comparison, identify and mark the changed data points to generate change data identification information;

[0084] In sub-step S301, the amount field is extracted through a standardization conversion model. The model converts all amounts into a unified benchmark currency unit according to different currency units to ensure that the amounts of all financial data are under a unified standard. The extraction of the account field depends on the account identification model. The model identifies the account information of each transaction according to the account coding specification and classifies the account type according to the predefined account classification rules. The extraction of the transaction type field uses a rule matching model and classifies according to different types of transactions. By comparing field information such as transaction descriptions and amounts, determine which type each transaction belongs to. After the extraction is completed, compare the data and identify those changed data points through a difference detection model. The detection model uses a threshold-based change analysis method to compare the differences between current and historical data, mark the changed data points, and generate change data identification information, providing a basis for subsequent synchronization processing.

[0085] S302: Based on the change data identification information, identify the incremental data that needs to be synchronized and generate a synchronization processing data set;

[0086] In sub-step S302, according to the markings of the change data identification information, screen the changed data in the financial data. The identification process finds all changed fields by comparing the differences between current data and historical data, such as amount changes, new accounts, or adjustments to transaction types. The incremental identification algorithm uses a timestamp sorting method to ensure that the screened incremental data is in chronological order and avoid data order errors. By comparing historical data and current data, the incremental identification algorithm ensures the uniqueness of each incremental data and excludes duplicate data, generating an incremental data set that needs to be synchronized. The generated synchronization processing data set provides a basis for subsequent data transmission and packing processing, ensuring the efficient execution of subsequent steps.

[0087] S303: Based on the synchronization processing data set, pack the incremental data according to the requirements of the data packet capacity for data transmission to generate an incremental data packet;

[0088] In sub-step S303, using a data packet allocation algorithm, the incremental data is grouped according to a preset maximum data packet capacity. The algorithm checks the total data volume of the synchronization processing data set and calculates the required number of packets based on the capacity limit of each data packet. The data packet allocation algorithm dynamically adjusts the data packet size to ensure that each data packet meets the transmission capacity requirements, minimizes the number of data packets as much as possible, and improves the transmission efficiency. The incremental data is packed according to the data packet allocation strategy to ensure that the data in each data packet has high cohesion, that is, data of the same type of transaction or data in adjacent segments is preferably placed in the same data packet. After packing, incremental data packets are generated and prepared for the data transmission phase to ensure that the data can be transmitted in an optimal manner.

[0089] Please refer to Figure 5 , based on the incremental data packets, according to the computing resource requirements of the data synchronization task and the real-time system load, predict the execution time of multiple tasks, and combine the urgency and sensitivity of multiple transaction types to calculate the synchronization priorities of multiple pieces of data. The specific steps for generating the synchronization task priority queue are as follows:

[0090] S401: Based on the incremental data packets, evaluate the computing resources required for multiple synchronization tasks and generate a resource requirement evaluation result;

[0091] In sub-step S401, through a resource requirement evaluation model, estimate the computing resource requirements according to the size and complexity of the incremental data packets. For each synchronization task, the resource requirement evaluation model considers the processing time of the data packets, the data volume size, and the requirements of computationally intensive operations. The computing resource evaluation model analyzes the number of fields, data types, the complexity of encryption and decryption operations, and possible computing tasks of the incremental data packets, and calculates the CPU, memory, storage, and bandwidth resources required for each synchronization task according to the characteristics of the data packets. Adjust according to the historical execution data and load analysis, combined with the preset computing resource standard, to ensure that the evaluation result meets the requirements of the actual system load, and generate a resource requirement evaluation result as the basic data for subsequent resource scheduling and optimization.

[0092] S402: Based on the resource requirement evaluation result, combined with the real-time system load information, evaluate and predict the actual execution time of multiple synchronization tasks and generate an execution time prediction result;

[0093] In sub-step S402, an execution time prediction model is used to predict the execution time of each synchronization task based on historical execution data, resource requirements, and system load information. The execution time prediction model collects the current load information of the system, including CPU usage, memory usage, network bandwidth utilization, etc. The target information is obtained through a real-time system monitoring tool, which provides potential performance bottlenecks or resource limitations that may be encountered during the task execution. The execution time prediction model will combine the evaluation results of the computing resource requirements of each synchronization task and simulate the execution process of the task under the current system load. This model is based on machine learning algorithms, including regression analysis or decision tree algorithms. By training historical data, it learns the relationship between the execution time of the task and the resource requirements, predicts the actual execution time, and takes into account the resource occupancy differences of different tasks, generating the execution time prediction results of multiple synchronization tasks to support task scheduling and priority calculation.

[0094] S403: Based on the execution time prediction results, combined with the urgency and sensitivity of multiple transaction types, calculate the processing priorities of multiple synchronization tasks, and generate a synchronization task priority queue;

[0095] The specific formula for calculating the processing priorities of multiple synchronization tasks is:

[0096] ;

[0097] Among them, P represents the priority score value of the synchronization task, U represents the characteristic vector value of the task's urgency, S represents the characteristic vector value of business sensitivity, T represents the standardized predicted execution time, represents the deviation value between the current system load and the benchmark load, N represents the number of tasks of the same type waiting to be executed in the current queue, represents the comprehensive weight coefficient of the characteristic vector, represents the time impact adjustment coefficient, represents the queue saturation balance factor.

[0098] Formula:

[0099] ;

[0100] Detailed explanation of the formula and the formula calculation derivation process:

[0101] The formula is used to calculate the priority score value of the financial data synchronization task, and the result is used for sorting the synchronization task priority queue;

[0102] Meaning and setting values of parameters:

[0103] U represents the characteristic vector value of the urgency, assumed to be 0.85;

[0104] S represents the characteristic vector value of business sensitivity, assumed to be 0.72;

[0105] T represents the predicted execution time after standardization, assumed to be 1.5 hours;

[0106] ΔR represents the system load deviation value, assumed to be 15%;

[0107] N represents the number of queue tasks, assumed to be 3;

[0108] β represents the comprehensive weight coefficient of the feature vector, assumed to be 0.85;

[0109] α represents the time impact adjustment coefficient, assumed to be 1.2;

[0110] δ represents the queue saturation balance factor, assumed to be 0.5;

[0111] Substitute the parameters into the formula for calculation:

[0112] ;

[0113] The calculation result of 4.22 indicates that the priority score of the target task is 4.22. The value is used to dynamically adjust the priority of the task queue to ensure the priority execution of the key business data synchronization task.

[0114] Please refer to Figure 6 , S501: Based on the synchronization task priority queue, analyze the synchronized data, verify the data integrity, and generate data integrity verification information;

[0115] The specific formula for verifying the data integrity is:

[0116] ;

[0117] Among them, represents the data integrity evaluation index, represents the total number of data fields for which data synchronization is completed, represents the total number of data fields to be synchronized, represents the number of successfully synchronized financial data records, represents the total number of data records to be synchronized, represents the number of missing data fields detected, represents the total number of critical data fields for verification, represents the data timestamp continuity ratio, represents the data field integrity weight coefficient, represents the data record integrity weight coefficient, represents the missing data impact weight coefficient.

[0118] Formula:

[0119] ;

[0120] Detailed Explanation of the Formula and the Derivation Process of Formula Calculation:

[0121] The formula is used to calculate the data integrity evaluation index, and the result is used to measure the integrity degree after data synchronization;

[0122] Meaning of Parameters and Set Values:

[0123] is the weight coefficient of data field integrity, assumed to be 0.4;

[0124] is the weight coefficient of data record integrity, assumed to be 0.35;

[0125] is the weight coefficient of the impact of missing data, assumed to be 0.25;

[0126] is the total number of data fields with data synchronization completed, assumed to be 875;

[0127] is the total number of data fields to be synchronized, assumed to be 1000;

[0128] is the number of successfully synchronized financial data records, assumed to be 9200;

[0129] is the total number of data records to be synchronized target, assumed to be 10000;

[0130] is the number of detected missing data fields, assumed to be 50;

[0131] is the total number of key data fields for verification, assumed to be 500;

[0132] is the data timestamp continuity ratio, assumed to be 0.98;

[0133] Substitute the parameters into the formula for calculation:

[0134] ;

[0135] The result 0.893 indicates that the data synchronization integrity reaches a relatively high level. The data integrity verification information shows that the synchronization process maintains good data quality. The evaluation value will be used to generate data integrity verification information, providing an important reference basis for subsequent data processing and quality control.

[0136] S502: Based on the data integrity verification information, by comparing multiple key fields, including timestamp, amount, and account, mark duplicate data and generate a duplicate data identification result;

[0137] In sub-step S502, use a duplicate data identification algorithm to detect whether there are duplicate records in the dataset by comparing multiple field values such as timestamp, amount, and account. Specifically, the timestamp field is used to judge the uniqueness of the data. If two pieces of data have the same timestamp and the amount and account information are the same, they are judged as duplicate data. The amount field and the account field are also used as comparison bases. If the amount and the account field are duplicate and have the same sign, the system will mark them as duplicate data. Through the multiple comparisons of the target fields, the system can identify the duplicate data records in the dataset and generate a duplicate data identification result. Use a duplicate data screening strategy based on thresholds to avoid mislabeling duplicate data when there are partial field changes and ensure the accuracy of the identification result.

[0138] S503: Based on the duplicate data identification result, according to the type of abnormal data fields, correct the abnormal data and generate a financial data synchronization record;

[0139] In sub-step S503, adopt a data cleaning algorithm to perform specific repairs on different types of abnormal data. If it is identified that there is an error in the amount field, through a budget calculation model, according to historical transaction data or the law of capital flow between accounts, automatically calculate and adjust the amount field value to ensure consistency with other fields. If there is an abnormality in the account field, the system will match it with a predefined account library and correct it through account verification rules. In the case of inconsistent timestamps, the system will use a time sorting algorithm to correct the timestamps to ensure that the data is arranged in the correct time order. After the target correction operation, generate a financial data synchronization record and output the correction result to ensure the accuracy and consistency of all synchronized data.

[0140] Please refer to Figure 7 , a financial data synchronization system. The financial data synchronization system is used to execute the above financial data synchronization method. The system includes:

[0141] The data preprocessing module, based on the input financial data, formats and cleans the data, identifies and records inconsistent data by analyzing the amount, account balance, and capital flow of multiple financial data, and generates a consistency verification result;

[0142] The data type analysis module, based on the consistency verification result, extracts the amount, timestamp, and account type information from the financial data, analyzes the data according to preset classification rules, classifies the data into multiple types, and generates a financial data classification result;

[0143] Based on the financial data classification results, the incremental synchronization processing module identifies the incremental data that needs to be synchronized and packs it into an incremental data packet by comparing the differences in multiple data fields, generating an incremental data packet;

[0144] Based on the incremental data packet, the task queue adjustment module evaluates and predicts the execution time of multiple tasks according to the computing resource requirements of the data synchronization tasks and the real-time system load, and combines the urgency and sensitivity of multiple transaction types to calculate the synchronization priorities of multiple tasks, generating a synchronization task priority queue;

[0145] Based on the synchronization task priority queue, the synchronized data correction module analyzes the integrity of the synchronized data, identifies data missing, duplicate, and inconsistent data, corrects the abnormal data, and generates a financial data synchronization record.

[0146] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0147] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0148] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single item or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or plural.

[0149] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above - mentioned processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0150] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0151] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above - described devices, apparatuses, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0152] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0153] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, in each embodiment of the present invention, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0155] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0156] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A financial data synchronization method, characterized in that: The method comprises: S1: Based on the input financial data, pre-process the data through formatting and data cleaning, analyze the capital flow data, perform consistency check on the capital data, identify and record inconsistent data, and generate consistency check results; S2: Based on the consistency check result, the financial data is classified by analyzing the amount, timestamp, and account type information of the financial data to generate a financial data classification result; S3: Based on the classification result of the financial data, by comparing the differences of multiple data fields in the financial data, identifying and extracting the incremental data that needs to be synchronized, packaging the target data, and generating an incremental data packet; S4: Based on the incremental data packet, according to the computing resource requirements of the data synchronization task and the real-time system load, the execution time of multiple tasks is predicted, and the synchronization priority of multiple data is calculated in combination with the urgency and sensitivity of multiple transaction types, and a synchronization task priority queue is generated; Based on the incremental data packet, according to the computing resource requirements of the data synchronization task and the real-time system load, the execution time of multiple tasks is predicted, and the synchronization priority of multiple data is calculated in combination with the urgency and sensitivity of multiple transaction types. The steps of generating the synchronization task priority queue are specifically as follows: S401: Based on the incremental data packet, evaluate the computing resources required for multiple synchronization tasks and generate a resource requirement evaluation result; S402: Based on the resource demand evaluation result and in combination with real-time system load information, the actual execution time of multiple synchronization tasks is evaluated and predicted to generate an execution time prediction result; S403: Based on the execution time prediction result, combined with the urgency and sensitivity of multiple transaction types, the processing priorities of multiple synchronization tasks are calculated to generate a synchronization task priority queue; The specific formula for calculating the processing priority of multiple synchronization tasks is: ； Among them, P represents the priority score of the synchronization task, U represents the urgency feature vector value of the task, S represents the business sensitivity feature vector value, and T represents the standardized predicted execution time. represents the deviation between the current system load and the benchmark load, N represents the number of tasks of the same type waiting to be executed in the current queue, represents the comprehensive weight coefficient of the feature vector, represents the time impact adjustment coefficient, represents the queue saturation balance factor; S5: Based on the synchronization task priority queue, the synchronized data is analyzed to verify the integrity of the data, identify data missing, duplicate, and inconsistent data, and correct abnormal data to generate a financial data synchronization record.

2. The financial data synchronization method according to claim 1, characterized in that: The consistency check result includes cash flow data, data anomaly identification, and timestamp consistency check result; the financial data classification result includes income financial data, expenditure financial data, and accounts payable financial data; the incremental data packet includes incremental data fields, difference data records, and incremental synchronization marks; the synchronous task priority queue includes task execution time estimation information, task priority level, and system load prediction information; the financial data synchronization record includes synchronous data set, data correction record, and synchronous abnormal data detection result.

3. The financial data synchronization method according to claim 1, characterized in that: Based on the input financial data, the data is pre-processed through formatting and data cleaning, the fund flow data is analyzed, the fund data is consistency checked, and inconsistent data is identified and recorded. The specific steps for generating consistency check results are as follows: S101: Based on the input financial data, format and clean the data, including standardizing the amount, account, and timestamp fields, and adjusting the currency unit and time format to generate data preprocessing results; S102: Analyze the capital flow data according to the data preprocessing result, identify non-compliant transaction data by comparing the capital flow direction with financial rules, and generate capital flow analysis results; S103: Based on the fund flow analysis result, verify the consistency of the data, including the amount balance, fund flow rules between accounts, and timestamp sorting, identify and mark abnormal data, and generate a consistency verification result.

4. The financial data synchronization method according to claim 1, characterized in that: Based on the consistency check result, the financial data is classified by analyzing the amount, timestamp, and account type information of the financial data. The steps of generating the financial data classification result are specifically as follows: S201: Based on the consistency check result, extract the amount and time stamp of multiple financial data to generate a basic information extraction result; S202: Based on the basic information extraction result, identify the outflow account and inflow account of funds, analyze the flow direction and business type of funds between accounts, and generate fund type information; S203: Based on the fund type information, multiple financial data are classified, including income, expenditure, and accounts payable, and a financial data classification result is generated.

5. The financial data synchronization method according to claim 1, characterized in that: Based on the financial data classification result, by comparing the differences of multiple data fields in the financial data, identifying and extracting the incremental data that needs to be synchronized, and packaging the target data, the steps of generating the incremental data packet are specifically as follows: S301: Based on the financial data classification result, extract the amount, account, and transaction type field information of multiple data, identify and mark the changed data points through data comparison, and generate changed data identification information; S302: Based on the changed data identification information, identify the incremental data that needs to be synchronized, and generate a synchronized processing data set; S303: Based on the synchronously processed data set, the incremental data is packaged according to the data packet capacity requirement of data transmission to generate an incremental data packet.

6. The financial data synchronization method according to claim 1, characterized in that: Based on the synchronization task priority queue, the synchronized data is analyzed, the integrity of the data is verified, data missing, duplicated, and inconsistent data are identified, and abnormal data is corrected. The specific steps of generating financial data synchronization records are: S501: Analyze the synchronized data based on the synchronization task priority queue, verify the integrity of the data, and generate data integrity verification information; The specific formula for verifying the integrity of the data is: ； in, represents the data integrity assessment indicator, Indicates the total number of data fields that have completed data synchronization. Represents the total number of data fields to be synchronized. Represents the number of financial data records that were successfully synchronized. Represents the total number of data records synchronized by the target. represents the number of missing data fields detected, Represents the total number of key data fields to be verified, Represents the data timestamp continuity ratio, Represents the data field integrity weight coefficient, Represents the data record integrity weight coefficient, Represents the weight coefficient of missing data impact; S502: Based on the data integrity check information, duplicate data is marked by comparing multiple key fields, including timestamp, amount, and account, and duplicate data identification results are generated; S503: Based on the duplicate data identification result, the abnormal data is corrected according to the type of the abnormal data field to generate a financial data synchronization record.

7. Financial data synchronization system, characterized in that: According to any one of claims 1 to 6, the financial data synchronization method comprises: The data preprocessing module formats and cleans the input financial data. It identifies and records inconsistent data by analyzing the amounts, account balances, and capital flows of multiple financial data, and generates consistency verification results. The data type analysis module extracts the amount, timestamp, and account type information from the financial data based on the consistency check result, analyzes the data according to preset classification rules, classifies the data into multiple types, and generates financial data classification results; The incremental synchronization processing module identifies the incremental data that needs to be synchronized and packages it based on the financial data classification result, by comparing the differences of multiple data fields, to generate an incremental data packet; The task queue adjustment module evaluates and predicts the execution time of multiple tasks based on the incremental data packet, according to the computing resource requirements of the data synchronization task and the real-time system load, calculates the synchronization priority of multiple tasks in combination with the urgency and sensitivity of multiple transaction types, and generates a synchronization task priority queue; The synchronization data correction module analyzes the integrity of the synchronized data based on the synchronization task priority queue, identifies data missing, duplication and data inconsistency, corrects abnormal data, and generates financial data synchronization records.

8. Financial data synchronization equipment, characterized in that, The financial data synchronization device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 6.

Images