Financial data full-process management system and method based on data fusion

Abstract

The invention relates to the technical field of financial data management, in particular to a financial data full-process management system and method based on data fusion. The method comprises the following steps: collecting original financial data; performing data cleaning on the original financial data to obtain standard financial data; determining business type information according to the standard financial data; identifying the used financial software based on the business type information, and generating a financial software list; mapping financial equipment according to the financial software list to obtain financial equipment information; and carrying out adaptability detection on the financial software list and the financial equipment information to obtain adaptability data. According to the invention, through the data processing technology and the mode identification technology, the adaptive matching of the financial software and the financial equipment is realized, and the fault condition of the financial equipment is optimized and solved, so that the whole-process management efficiency of the financial data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data management, and in particular to a financial data full-process management system and method based on data fusion. Background Art

[0002] Financial data is divided into internal financial data, business data, market data, and customer data, etc. These scattered data are integrated together through data fusion technology. In the full-process management of financial data, hardware equipment needs to be seamlessly connected with financial software; however, there are issues of interface or protocol incompatibility between old and new hardware devices. For example, a new server may not be able to seamlessly connect with old storage devices, resulting in unstable system operation; when performing high-frequency financial data migration, hardware equipment may fail due to insufficient power supply or insufficient performance; for example, some old equipment cannot meet the requirements of financial software for hardware resources (such as CPU, memory, storage speed, etc.), thereby affecting the efficiency and stability of data migration; during the data migration process, there are differences in data formats between different financial software or systems, resulting in data being unable to be directly migrated or errors after migration. For example, when migrating from an old system to a new system, data in CSV format cannot be parsed correctly and requires additional conversion work; and the existing financial data management system is not stable enough, especially when running in a multi-device, multi-software environment, and is prone to crashes or performance degradation. Summary of the Invention

[0003] Based on this, it is necessary to provide a financial data full-process management system and method based on data fusion to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a method for managing the entire financial data process based on data fusion is provided, the method comprising the following steps:

[0005] Step S1: Collecting original financial data; performing data cleaning on the original financial data to obtain standard financial data; determining business type information based on the standard financial data;

[0006] Step S2: Identify the financial software used based on the business type information and generate a financial software list; map the financial device according to the financial software list to obtain financial device information; perform compatibility testing on the financial software list and the financial device information to obtain compatibility data;

[0007] Step S3: Classify the adaptability data into adaptability levels, and monitor the high-frequency migration status of the financial data based on the adaptability levels; record the operating condition signals of the financial equipment based on the high-frequency migration status, detect abnormal device characteristics of the operating condition signals; and adjust the migration mode of the financial equipment based on the abnormal device characteristics;

[0008] Step S4: Adapt and optimize the financial software used according to the adjusted migration mode to obtain financial optimization software; transfer the standard financial data to the financial optimization software for multi-dimensional data fusion, and construct a financial data view.

[0009] The present invention collects raw financial data and performs data cleaning to obtain standard financial data, which can effectively remove noise and redundant information from the data. This enables subsequent financial analysis and processing processes to be carried out based on accurate and standardized data, avoiding analytical deviations and erroneous decisions caused by data quality issues, and laying a solid data foundation for the entire financial data management process. Determining business type information based on standard financial data enables accurate classification of the business activities behind the financial data. This helps to clarify the financial data characteristics and processing requirements corresponding to different business activities, provides an accurate basis for subsequent financial software identification and equipment adaptation for different business types, ensures the close integration of financial data management with actual business activities, and improves the pertinence and effectiveness of financial data processing. Based on the business type information, the financial software used is identified and a financial software list is generated, which can accurately match the financial software requirements in different business scenarios. This avoids blind selection of financial software, ensures that the selected financial software can meet the financial data processing requirements of specific business types, improves the efficiency and accuracy of financial data processing, and also reduces the risk of data processing errors and system failures caused by software mismatch. Financial device information is mapped to the financial software list. Compatibility testing is then performed on the financial software list and the financial device information to generate compatibility data, achieving precise compatibility between the financial software and the financial devices. This process ensures that the financial software fully utilizes the performance and functionality of the financial devices during operation, avoiding issues such as inefficient operation and data processing delays caused by insufficient device performance or software-device incompatibility, thereby improving the stability and reliability of the entire financial data processing system. Compatibility data is classified into levels, and the high-frequency migration status of financial data is monitored based on the level of compatibility, providing real-time visibility into the migration of financial data between different devices and software. This helps to promptly identify potential issues and risks during data migration, such as data loss and duplicate migrations, allowing appropriate intervention and optimization measures to ensure the integrity and accuracy of financial data migration and the smooth flow of financial data across different links. Based on the high-frequency migration status, operating signals of financial devices are recorded, and abnormal device characteristics of the operating signals are detected. The migration mode of the financial devices is adjusted based on these abnormal characteristics, enabling dynamic monitoring and intelligent adjustment of the operating status of financial devices. This process allows timely adjustments to the data migration model based on the actual operating status of the equipment, preventing migration failures or data errors caused by equipment failures or performance issues. This improves the operational efficiency and stability of financial equipment, extends its lifespan, and reduces maintenance costs. Financial software can be adapted and optimized based on the adjusted migration model, resulting in optimized financial software that further enhances its performance and functionality.The optimized financial software can better adapt to the migration and processing needs of financial data, improve the speed and accuracy of data processing, and also enhance the stability and compatibility of the software, providing strong support for the efficient management and analysis of financial data. Standard financial data is transferred to the financial optimization software for multi-dimensional data fusion, and a financial data view is constructed to achieve deep integration and visual display of financial data. Multi-dimensional data fusion can comprehensively analyze and process financial data from different sources and in different formats, dig out the potential correlations between the data, and provide comprehensive, accurate, and real-time data support for financial decision makers. Therefore, the present invention uses data processing technology and pattern recognition technology to achieve adaptability matching between financial software and financial equipment, optimize and solve the failure of financial equipment, thereby improving the efficiency of full-process management of financial data.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: calling and starting the full-process data interface of the financial system to obtain original financial data;

[0012] Step S12: Clean the original financial data to obtain standard financial data, where data cleaning includes standardizing data formats, correcting data logic, and completing data association values.

[0013] Step S13: extracting financial category information and financial object information from the standard financial data; matching the financial category information and financial object information with business project relationships to obtain business project relationship data;

[0014] Step S14: Mark the business project relationship data with the start and end timestamps, and determine the business project cycle based on the start and end timestamps; identify the business type of the standard financial data based on the business project cycle to obtain business type information.

[0015] The present invention calls and activates the full-process data interface of the financial system, enabling efficient and stable acquisition of raw financial data. This ensures the integrity and timeliness of data acquisition, providing a reliable data source for subsequent data processing. Data cleansing of the raw financial data, including data format unification, data logic correction, and data association value completion, effectively removes errors and inconsistencies. This ensures high accuracy and consistency of standard financial data, providing a high-quality data foundation for subsequent business analysis and processing. Financial category and financial object information is extracted from the standard financial data, and business project relationships are matched to obtain business project relationship data. This process accurately identifies the relationships between financial data and business activities, ensuring the accuracy and completeness of business information and providing an accurate basis for subsequent business cycle analysis. Business project relationship data is timestamped with start and end timestamps, and the business project cycle is determined based on the start and end timestamps. This accurately calculates the duration of business activities, providing a temporal reference for business type identification. Business types are identified based on the business project cycle, obtaining business type information. This process can accurately distinguish financial data of different business types, provide a clear business orientation for subsequent financial software selection and equipment adaptation, and ensure that financial data management is highly consistent with business needs.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Divide the business type information into business process nodes, determine the business process links according to the business process nodes; and obtain the financial software used in the links according to the business process links;

[0018] Step S22: marking the software parameter characteristics of the financial software used in the link and generating a financial software list;

[0019] Step S23: Identify the financial business processing type, data processing volume, and interaction mode of the financial software list to form software functional characteristics;

[0020] Step S24: determining the type of financial device used by the software according to the software function feature library, and extracting the financial device operating parameters based on the financial device type to obtain financial device information;

[0021] Step S25: Perform compatibility testing on the financial software list and financial device information to obtain compatibility data.

[0022] The present invention divides business type information into business process nodes, determines business process links according to business process nodes, can accurately identify the flow paths and key nodes of financial data in different business processes, and provide a clear business orientation for the selection of financial software; obtains the financial software used in the link according to the business process link, ensures that the selected financial software can accurately match the financial data processing requirements of different business links, and improves the efficiency and accuracy of financial data processing. The financial software used in the link is marked with software parameter characteristics, and a financial software list is generated, which can comprehensively record the operating parameters and functional characteristics of the financial software, and provide a detailed technical basis for subsequent adaptability testing; the financial business processing type, data processing volume and interaction mode of the financial software list are identified to form software functional characteristics, which can accurately identify the functional performance and performance indicators of the financial software in different business scenarios, and ensure that the financial software can efficiently process financial data. Determine the type of financial equipment used by the software based on the software function feature library, and extract the financial equipment operating parameters based on the financial equipment type to obtain financial equipment information, which can accurately match financial software and financial equipment to ensure that the equipment can meet the hardware requirements for software operation; perform adaptability testing on the financial software list and financial equipment information to obtain adaptability data, which can comprehensively evaluate the compatibility and matching between financial software and financial equipment to ensure the stable operation of the financial data processing system.

[0023] Preferably, step S25 includes the following steps:

[0024] Step S251: Detecting the interface types between the financial software list and the financial device information one by one to obtain the interface type compatibility; if the interface types do not match, record the specific type and reason of the mismatch and set an interface matching parameter, where a value of 1 indicates a complete match and a value of 0 indicates a complete mismatch;

[0025] Step S252: The data transmission formats between the financial software list and the financial device information are detected one by one to obtain the degree of transmission format compatibility; if the data transmission formats are inconsistent, the specific format type and difference of the inconsistency are recorded, and a data format matching parameter is set. The data format matching parameter is 1 for a complete match and 0 for a complete mismatch;

[0026] Step S253: Check the power supply stability between the financial software list and the financial device information one by one to obtain the power supply adaptability of the device; record whether the power supply voltage is within the range of 220V±10%, and set the power supply voltage matching parameter. The power supply voltage matching parameter is 1, indicating a complete match, and 0, indicating a complete mismatch.

[0027] Step S254: Integrate the interface type adaptability, transmission format adaptability, and device power adaptability to obtain adaptability data.

[0028] The present invention detects the interface type between the financial software list and the financial device information one by one to obtain the interface type adaptation degree. If the interface type does not match, the specific type and reason of the mismatch are recorded, and the interface matching parameter is set (the interface matching parameter is 1 for complete match, and 0 for complete mismatch). This process can accurately identify the interface compatibility between the financial software and the financial device, ensuring the efficiency and stability of data transmission; the data transmission format between the financial software list and the financial device information is detected one by one to obtain the transmission format adaptation degree; if the data transmission format is inconsistent, the specific format type and difference of the inconsistency are recorded, and the data format matching parameter is set (the data format matching parameter is 1 for complete match, and 0 for complete mismatch), ensuring the accuracy and consistency of the data during the transmission process, avoiding data errors caused by format mismatch; the power supply stability between the financial software list and the financial device information is detected one by one to obtain the device power supply adaptation degree. Record whether the power supply voltage is within the range of 220V±10%, and set the power supply voltage matching parameter (the power supply voltage matching parameter is 1 for complete match, and 0 for complete mismatch). This test can ensure the stability of power supply to financial equipment during operation and avoid equipment failure due to voltage problems. By integrating the adaptability of interface types, transmission formats and equipment power supply, it can comprehensively evaluate the compatibility between financial software and financial equipment.

[0029] Preferably, in step S3, recording the operating condition signal of the financial device according to the high-frequency migration state and detecting the abnormal characteristics of the device in the operating condition signal include:

[0030] Based on the high-frequency migration state, the operating condition signal of the financial equipment is recorded every 30 seconds. The operating condition signal includes the equipment's jitter resonance frequency, vibration intensity, and equipment load operating heat;

[0031] If the vibration frequency is abnormal around 22-24Hz, it is marked as abnormal device resonance;

[0032] If the vibration intensity exceeds 12 mm / s for three consecutive times, it will be marked as abnormal equipment intensity;

[0033] If the device load operating temperature exceeds 85°C, it will be marked as abnormal device load;

[0034] Equipment resonance anomalies, equipment intensity anomalies and equipment load anomalies are integrated and recorded as equipment anomaly features.

[0035] The present invention records the operating condition signal of the financial equipment every 30 seconds based on the high-frequency migration state, including the equipment's jitter resonance frequency, vibration intensity, and equipment load operating heat. It can timely capture subtle changes in the equipment operation and ensure real-time grasp of the equipment status; if the jitter resonance frequency vibrates abnormally near 22-24Hz, it is marked as equipment resonance abnormality. This abnormal marking method based on a specific frequency range can accurately identify the resonance problem of the equipment and avoid equipment damage and performance degradation caused by resonance; if the vibration intensity exceeds 12mm / s for three consecutive times, it is marked as equipment intensity abnormality. By setting the conditions for continuous exceeding the threshold, accidental errors can be effectively filtered to ensure that the marked abnormalities have practical significance; if the equipment load operating heat exceeds 85°C, it is marked as equipment load abnormality. The setting of this temperature threshold can timely detect equipment overheating problems and prevent equipment failures caused by high temperature. Integrating and recording equipment resonance anomalies, equipment severity anomalies, and equipment load anomalies as equipment anomaly features can comprehensively reflect the operating status of the equipment. Through the integrated equipment anomaly features, the system can adopt targeted management strategies based on different anomaly types; adjusting equipment operating parameters in the event of resonance anomalies, conducting equipment inspections in the event of severity anomalies, and optimizing equipment load distribution in the event of load anomalies.

[0036] Preferably, the step S3 of adjusting the migration mode of the financial device based on the abnormal characteristics of the device includes:

[0037] For equipment resonance anomalies, reduce the data migration frequency of financial equipment and increase the migration interval to 90 seconds to obtain the resonance anomaly migration adjustment amount;

[0038] For abnormal equipment vibration intensity, adjust the batch size of data migration for financial equipment and adjust the data migration priority, prioritizing the migration of critical data while reducing the migration frequency of non-critical data to obtain the migration adjustment amount for abnormal intensity.

[0039] For abnormal equipment load, adjust the time window for data migration of financial equipment to obtain the load abnormality migration adjustment amount;

[0040] The financial equipment adjustment migration mode is determined based on the resonance abnormality migration adjustment amount, the intensity abnormality migration adjustment amount, and the load abnormality migration adjustment amount.

[0041] In response to equipment resonance anomalies, the present invention reduces the frequency of data migration for financial equipment and increases the interval between each migration to 90 seconds, thereby obtaining a resonance anomaly migration adjustment. This adjustment effectively reduces data migration interruptions and errors caused by equipment resonance, ensuring data migration stability. In response to equipment vibration severity anomalies, the batch size of data migration for financial equipment is adjusted, and the data migration priority is adjusted, prioritizing critical data while reducing the migration frequency of non-critical data, thereby obtaining a severity anomaly migration adjustment. This adjustment method optimizes data migration efficiency, ensuring the priority transmission of critical data while reducing data transmission errors caused by excessive equipment vibration intensity. In response to equipment load anomalies, the time window for data migration for financial equipment is adjusted, thereby obtaining a load anomaly migration adjustment. By adjusting the migration time window, data migration can be avoided when the equipment load is excessively high, thereby reducing the risk of equipment failure due to overheating or overload. The financial equipment migration adjustment mode is determined based on the resonance anomaly migration adjustment, the severity anomaly migration adjustment, and the load anomaly migration adjustment. This comprehensive adjustment mode dynamically adjusts the data migration strategy based on the actual operating status of the equipment, ensuring efficient and stable data migration under different equipment anomaly conditions.

[0042] Preferably, the step S4 of adapting and optimizing the financial software used according to the adjusted migration mode includes:

[0043] Matching the adjusted migration mode with software operation parameter quantities to obtain software operation parameter quantities;

[0044] According to the software operation parameters, the data migration frequency of the data processing type unit of the financial software used is aligned to obtain the software adaptation frequency;

[0045] Synchronize the data migration batch size of the data processing unit of the financial software used according to the software operation parameters to obtain the software adaptation batch;

[0046] Optimize the migration time window of the data scheduling unit of the financial software used according to the software operation parameters to obtain the software optimization window;

[0047] Based on the software adaptation frequency, software adaptation batch and software optimization window, the financial software used is optimized accordingly to obtain financial optimization software.

[0048] The present invention matches the software operation parameter quantity of the adjusted migration mode to obtain the software operation parameter quantity. This process can ensure that the operation parameters of the financial software accurately correspond to the adjusted migration mode, providing an accurate parameter basis for subsequent data processing and migration; according to the software operation parameter quantity, the data migration frequency of the data processing type unit of the financial software used is aligned to obtain the software adaptation frequency. Through frequency alignment, different data processing type units can maintain a consistent migration rhythm during the migration process, reducing the problem of data asynchrony caused by frequency differences; according to the software operation parameter quantity, the data migration batch size of the data processing unit of the financial software used is synchronized to obtain a software adaptation batch. The synchronization of batch size can optimize the efficiency of data migration, ensure balanced data migration between different data processing units, and avoid resource waste or data backlog due to inconsistent batch sizes; according to the software operation parameter quantity, the migration time window of the data scheduling unit of the financial software used is optimized to obtain a software optimization window. Optimizing the time window allows for the proper scheduling of data migration periods, avoiding migration during high-load periods, thereby improving migration success rates and system stability. Financial software is optimized based on software adaptation frequency, software adaptation batches, and software optimization windows to achieve optimized financial software. This comprehensive optimization allows financial software to fully utilize its performance during data migration, improving migration efficiency and accuracy while ensuring the integrity and consistency of financial data.

[0049] Preferably, in step S4, the standard financial data is transferred to the financial optimization software for multi-dimensional data fusion, and the financial data view is constructed, including:

[0050] Determine the transmission priority and transmission time window of the financial data based on the standard financial data to obtain a transmission task list;

[0051] Transfer standard financial data to the financial optimization software based on the transfer task list, and perform data fusion according to financial types to generate financial type fusion data;

[0052] Conduct financial analysis on financial type fusion data and generate financial analysis reports;

[0053] Output financial analysis reports into visual chart formats to build a view of financial data.

[0054] The present invention determines the transmission priority and transmission time window of financial data based on standard financial data to obtain a transmission task list, which can ensure that financial data is transmitted in an orderly manner according to importance and timeliness, optimize the utilization of network resources, and improve the efficiency of data transmission; based on the transmission task list, the standard financial data is transmitted to the financial optimization software, and the data is fused according to the financial type to generate financial type fusion data. Financial analysis is performed on the financial type fusion data to generate a financial analysis report. This fusion and analysis process can integrate data of different financial types, explore the correlation between data, and provide comprehensive and accurate information for financial decision-making; the financial analysis report is output in a visual chart format to construct a financial data view. Through visual charts, financial personnel can intuitively understand financial status and trends, make decisions quickly, and improve the efficiency and accuracy of financial management.

[0055] In this specification, a financial data full-process management system based on data fusion is provided, which is used to implement the above-mentioned financial data full-process management method based on data fusion. The financial data full-process management system based on data fusion includes:

[0056] The financial data collection module is used to collect original financial data; clean the original financial data to obtain standard financial data; and determine business type information based on the standard financial data;

[0057] The software and device detection module is used to identify the financial software used based on the business type information and generate a financial software list; map the financial devices according to the financial software list to obtain financial device information; and perform compatibility testing on the financial software list and the financial device information to obtain compatibility data;

[0058] The financial equipment adjustment module is used to classify adaptability data into adaptability levels and monitor the high-frequency migration status of financial data based on the adaptability level; record the operating condition signals of financial equipment based on the high-frequency migration status, detect equipment abnormality characteristics of the operating condition signals, and adjust the migration mode of financial equipment based on the equipment abnormality characteristics;

[0059] The data fusion management module is used to adapt and optimize the financial software used according to the adjusted migration model to obtain financial optimization software; transmit standard financial data to the financial optimization software for multi-dimensional data fusion and construct a financial data view.

[0060] The present invention realizes the efficient collection, cleaning, adaptability detection, equipment adjustment, data fusion and visual display of financial data. The system can accurately collect and clean raw financial data to ensure the accuracy and consistency of the data. Through the software equipment detection module, the system can identify compatible financial software and equipment, and perform adaptability detection to ensure the stable operation of the system. The financial equipment adjustment module can dynamically adjust the migration mode according to the operating status of the equipment and optimize the performance of the equipment. The data fusion management module further adapts and optimizes the financial software, realizes multi-dimensional data fusion, and constructs an intuitive financial data view to provide comprehensive support for financial decision-making. Overall, the system improves the efficiency, accuracy and reliability of financial data management, enhances the intelligence level of financial management, and helps enterprises better cope with complex financial data processing needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The figure is a flowchart of the steps of a method for full-process management of financial data based on data fusion;

[0062] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0063] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0064] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0065] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0066] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0067] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0068] To achieve this, please refer to Figures 1 to 3 A method for managing the entire financial data process based on data fusion, comprising the following steps:

[0069] Step S1: Collecting original financial data; performing data cleaning on the original financial data to obtain standard financial data; determining business type information based on the standard financial data;

[0070] Step S2: Identify the financial software used based on the business type information and generate a financial software list; map the financial device according to the financial software list to obtain financial device information; perform compatibility testing on the financial software list and the financial device information to obtain compatibility data;

[0071] Step S3: Classify the adaptability data into adaptability levels, and monitor the high-frequency migration status of the financial data based on the adaptability levels; record the operating condition signals of the financial equipment based on the high-frequency migration status, detect abnormal device characteristics of the operating condition signals; and adjust the migration mode of the financial equipment based on the abnormal device characteristics;

[0072] Step S4: Adapt and optimize the financial software used according to the adjusted migration mode to obtain financial optimization software; transfer the standard financial data to the financial optimization software for multi-dimensional data fusion, and construct a financial data view.

[0073] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for managing the entire process of financial data based on data fusion according to the present invention. In this example, the method for managing the entire process of financial data based on data fusion includes the following steps:

[0074] Step S1: Collecting original financial data; performing data cleaning on the original financial data to obtain standard financial data; determining business type information based on the standard financial data;

[0075] In an embodiment of the present invention, raw financial data is collected from data sources such as an enterprise's financial information system and business system using a data extraction tool. Specific operations include: using ETL (Extract, Transform, Load) tools to extract data from data sources such as the enterprise's internal ERP system, financial software, and databases. Setting a data extraction time interval, such as daily extraction, ensures the timeliness of the data. The collected data includes financial statement data, transaction details, budget data, etc., and the data format is CSV, JSON, XML, etc.; using a data cleaning tool to clean the raw financial data. Specific operations include: processing missing values: using statistical methods to fill missing values, such as using numerical information such as the mean and median of the data set to fill missing values. For numerical data, the mean or median is used for filling; for categorical data, the mode is used for filling. Error detection and correction: identifying and correcting erroneous values ​​in the data, such as spelling errors and inconsistent formats. Regular expression matching and data dictionary verification are used to ensure that the format and content of the data comply with business rules. Duplicate data processing: comparing similarities between records, deleting duplicate records, and retaining uniqueness. Use a hash algorithm to calculate the fingerprint of data records and identify duplicate records by comparing fingerprint values. Data standardization and conversion: Format the data, such as unifying the date format and unit. Standardize the date format to ISO format (YYYY-MM-DD). Use data mining algorithms to analyze standard financial data and determine business type information. Specific operations include: using clustering algorithms (such as K-Means) to perform cluster analysis on transaction details data, and classify business activities into different categories based on transaction amount, frequency, and other characteristics. Use association rule mining algorithms (such as Apriori) to analyze the association relationships in the data and identify the association patterns between different business types. Based on the mined features and patterns, determine the business type information and store it as a business type label for subsequent analysis and processing.

[0076] Step S2: Identify the financial software used based on the business type information and generate a financial software list; map the financial device according to the financial software list to obtain financial device information; perform compatibility testing on the financial software list and the financial device information to obtain compatibility data;

[0077] In an embodiment of the present invention, the business type information identified is matched using preset mapping rules between business types and financial software. Specifically, a database is constructed containing various financial software programs and their applicable business types, and different business types are associated with corresponding financial software programs. Business type information is retrieved one by one, and matching financial software programs are selected based on the mapping rules and added to a financial software list. Device mapping is performed based on the software names in the financial software list, combined with compatibility information between the financial software and financial devices. A database is established covering various financial software programs and their supported financial device models, recording each software program's requirements for the financial device's operating system, hardware configuration, and other requirements. The financial software list is then traversed, and the corresponding financial device model and configuration requirements are queried for each software program. This information is then integrated into financial device information. An automated testing tool is used to perform compatibility testing on the financial software list and financial device information. Compatibility testing parameter thresholds are set, such as the software's minimum operating system version requirement, upper memory usage limit, and lower hard disk space limit. The testing tool then compares the actual parameters in the financial device information against these parameter thresholds. If the actual operating system version, memory usage, remaining hard disk space, etc. of the financial device meet the parameter threshold requirements of the corresponding financial software, the financial device is determined to be compatible with the financial software, and the adaptation result is recorded as "compatible"; otherwise, it is recorded as "incompatible"; finally, the adaptation results of all financial software and financial devices are summarized to form adaptability data.

[0078] Step S3: Classify the adaptability data into adaptability levels, and monitor the high-frequency migration status of the financial data based on the adaptability levels; record the operating condition signals of the financial equipment based on the high-frequency migration status, detect abnormal device characteristics of the operating condition signals; and adjust the migration mode of the financial equipment based on the abnormal device characteristics;

[0079] In an embodiment of the present invention, a preset adaptation level classification rule is used to classify and process the adaptability data. Specifically, the following steps are performed: setting parameter thresholds for adaptation level classification. For example, based on the ratio of "adaptable" to "unadaptable" records in the adaptability test results, the adaptability level is divided into three levels: high adaptability, medium adaptability, and low adaptability. If the proportion of "adaptable" records in the adaptability test results exceeds 80%, the adaptability level is classified as high; if the proportion is between 50% and 80%, the adaptability level is classified as medium; if the proportion is less than 50%, the adaptability level is classified as low. The adaptability data is traversed and, based on the set parameter thresholds, the compatibility of each financial software and financial device is evaluated to determine its adaptability level. The adaptability level information is then appended to the adaptability data to form complete adaptability level data. A data monitoring tool is used to monitor the migration frequency of financial data based on the adaptability level. Specifically, the following steps are performed: setting the parameters of the monitoring tool. For example, the threshold for high-frequency migration is set to more than 10 migrations per hour. For high-adaptability financial software and device combinations, the monitoring cycle records the number of financial data migrations every 10 minutes; for medium-adaptability combinations, the monitoring cycle is every 30 minutes; and for low-adaptability combinations, the monitoring cycle is every hour. During each monitoring cycle, the monitoring tool counts the number of financial data migrations and compares it with a high-frequency migration threshold. If the number of migrations exceeds the threshold, the financial software and device combination is recorded as being in a high-frequency migration state and this information is stored in the monitoring results database. When high-frequency migration of financial data is detected, the operating signal recording module is activated to record the operating status of the financial device. Specifically, the operating signal recording parameters are set, such as key indicators such as CPU usage, memory utilization, hard disk read / write speed, and network bandwidth utilization. The recording frequency is set to every 5 seconds. Based on the set parameters, the operating signal recording module collects operating data from the financial device in real time and stores it in the device operating status database, forming a complete operating signal record. Data analysis algorithms are used to analyze the operating signal records to detect abnormal device characteristics. The specific operation involves setting parameter thresholds for detecting device anomaly characteristics. For example, CPU usage exceeding 90% for more than one minute, memory usage exceeding 95%, or hard drive read and write errors exceeding five per minute. The data analysis algorithm analyzes each recorded operating signal. If a key metric in a record exceeds the set threshold, the financial device is identified as having an anomaly. This information is stored in the device anomaly database, along with the time and duration of the anomaly. Based on the detected anomaly characteristics, the migration mode of the financial device is adjusted.The specific operation involves setting rules for adjusting the migration mode. For example, if abnormal CPU usage is detected, the migration task priority is lowered, reducing the number of concurrent migration tasks. If a hard drive read or write error is detected, the migration task is paused for data verification and repair. Based on the abnormality characteristics in the device anomaly database, the migration mode adjustment module is invoked to adjust the migration mode of the financial device according to the preset rules. The adjusted migration mode parameters are stored in the migration configuration database to ensure that subsequent migration tasks can be executed in the optimized mode.

[0080] Step S4: Adapt and optimize the financial software used according to the adjusted migration mode to obtain financial optimization software; transfer the standard financial data to the financial optimization software for multi-dimensional data fusion, and construct a financial data view.

[0081] In an embodiment of the present invention, adjusted migration mode parameters, including migration task priority, number of concurrent tasks, and data validation mechanism, are read from a migration configuration database. These parameters are stored in a specific database table, such as the migration_config table, which contains the fields task_priority, concurrent_tasks, and data_validation_enabled. The task scheduling algorithm of the financial software is adjusted based on the read migration task priority parameter. If the task_priority value is below a preset threshold (e.g., 50), the scheduling priority of the migration task is set to low, reducing system resource allocation. Specifically, the priority weights in the task scheduling algorithm are modified so that low-priority tasks are ranked behind high-priority tasks when system resources are allocated. The number of migration tasks that can run simultaneously in the financial software is limited based on the concurrent_tasks parameter. If this parameter value is N, the maximum number of concurrent tasks is set to N in the task management logic. When the number of concurrent tasks reaches N, subsequent tasks will enter a waiting queue. Based on the data validation mechanism parameter data_validation_enabled, if this parameter value is true, data validation functionality is integrated into the financial software. Specifically, a data verification step is inserted into the data migration path, using a hash algorithm (such as SHA-256) to verify data integrity and consistency before and after migration. After completing these adjustments, the financial software undergoes functional testing. This testing includes migration task execution, resource allocation, and data verification accuracy. Once the test passes, the optimized financial software is stored in the optimized software library for future use. Standard financial data, including financial statements, transaction details, and budget data, is read from the standard financial data repository. This data is stored in the database's financial_data table, with fields such as report_data, transaction_details, and budget_data. Data fusion tools are used to transfer the standard financial data to the financial optimization software. During the transfer process, data encryption, such as the AES-256 algorithm, is used to ensure data security. The encryption process includes key generation, data encryption, and encrypted data transmission. Financial data from different time periods is aggregated and analyzed based on the timestamp field. For example, calculate monthly total revenue, expenditure, and profit to generate time series data. Use the business_type field in the data to categorize and consolidate financial data for different business types. For example, aggregate sales data, procurement data, and administrative data separately to generate data for the business type dimension. Use the region field in the data to compare and correlate financial data for different regions.For example, calculate the revenue share and expenditure differences by region to generate data with a geographic dimension. Use time series data to generate financial indicator trend charts, such as plotting monthly total revenue, expenditure, and profit trends. Use data from the business type dimension to generate business type distribution charts, such as plotting the revenue share of sales, procurement, and administrative services. Use data from the geographic dimension to generate regional financial comparison tables, such as generating a comparison table of revenue, expenditure, and profit for each region.

[0082] As an example of the present invention, refer to Figure 2 As shown, in this example, step S1 includes:

[0083] Step S11: calling and starting the full-process data interface of the financial system to obtain original financial data;

[0084] Step S12: Clean the original financial data to obtain standard financial data, where data cleaning includes standardizing data formats, correcting data logic, and completing data association values.

[0085] Step S13: extracting financial category information and financial object information from the standard financial data; matching the financial category information and financial object information with business project relationships to obtain business project relationship data;

[0086] Step S14: Mark the business project relationship data with the start and end timestamps, and determine the business project cycle based on the start and end timestamps; identify the business type of the standard financial data based on the business project cycle to obtain business type information.

[0087] In an embodiment of the present invention, the full-process data interface of the financial system is called through a preset API calling mechanism. The specific operation is: use the HTTP protocol to send a GET request to the server of the financial system, the request address is http: / / financial-system.com / api / data, and carry necessary authentication information, such as the API key api_key=123456. After the server responds, the returned original financial data is obtained, the data format is JSON, and contains fields such as transaction_id, amount, date, etc.; the obtained original financial data is cleaned. First, the data format is unified, and the date field date is uniformly converted from multiple formats (such as YYYY / MM / DD, DD-MM-YYYY) to the ISO standard format YYYY-MM-DD. Secondly, the data logic is corrected to check whether the amount field amount is a positive number. If it is a negative number, it is corrected to a positive value. Finally, data association values ​​are filled in. For the missing transaction type field (transaction_type), its possible value is inferred based on the transaction amount and date. For example, records with an amount greater than 10,000 are inferred as "large transactions" and the field value is filled in. Financial category and financial object information are extracted from the standard financial data. Financial category information includes the "category" field, and financial object information includes the "object_id" field. This information is then matched to business project relationships. Specifically, based on the preset mapping rules, records with the "income" category are associated with the project with the object_id "1001." This data is marked as business project relationship data and stored in a new data table (business_project_relation) with the fields project_id and related_data. The business project relationship data is timestamped, extracting the start_timestamp and end_timestamp of each record. The business project duration is calculated based on these timestamps using the formula project_duration = end_timestamp - start_timestamp. Then, the business type of the standard financial data is identified according to the business project cycle. If the project_duration is less than 30 days, it is identified as a "short-term project" and the business type information is stored in the business_type field of the standard financial data.

[0088] As an example of the present invention, refer to Figure 3 As shown, in this example, step S2 includes:

[0089] Step S21: Divide the business type information into business process nodes, determine the business process links according to the business process nodes; and obtain the financial software used in the links according to the business process links;

[0090] Step S22: marking the software parameter characteristics of the financial software used in the link and generating a financial software list;

[0091] Step S23: Identify the financial business processing type, data processing volume, and interaction mode of the financial software list to form software functional characteristics;

[0092] Step S24: determining the type of financial device used by the software according to the software function feature library, and extracting the financial device operating parameters based on the financial device type to obtain financial device information;

[0093] Step S25: Perform compatibility testing on the financial software list and financial device information to obtain compatibility data.

[0094] In this embodiment of the present invention, a business type information table is read from a database. This table records information such as business types and corresponding process steps. Business types are categorized and different process steps within the same business type are extracted as business process nodes. For example, for sales operations, the extracted process steps include order receipt, order processing, shipping, and payment collection. These steps are business process nodes. Adjacent nodes are grouped together into business process segments based on the order of the business process nodes. For example, order receipt and order processing are combined into the sales order processing segment, and shipping and payment collection are combined into the sales shipping and payment collection segment. These business process segments and their corresponding node information are stored in a new data table for easy subsequent query and use. A financial software mapping table is established, which records the correspondence between business process segments and financial software. Based on the business process segment, the corresponding financial software is searched in the financial software mapping table. For example, for the sales order processing segment, searching the mapping table reveals that the corresponding financial software is a certain financial software with order processing functionality. The configuration file storage location of the financial software is accessed and the corresponding software configuration file is read. The configuration file contains parameter characteristics such as the software name, version number, and supported functions. Extract information such as the software name, version number, and supported functions from the configuration file. For example, extract the software name as financial software, version number 2.0, and supported functions such as accounting and report generation. Integrate the extracted parameter features to form a financial software list. The financial software list contains information such as the software name, version number, and supported functions, facilitating subsequent analysis and processing. Analyze the software's supported functions in the financial software list to determine its financial business processing type. For example, if the software supports accounting and report generation, identify its financial business processing type as accounting and report processing. Use log analysis tools to read the financial software's log files and calculate its data processing volume over a certain period of time. For example, count the number of transaction records processed daily and the number of reports generated daily to measure the software's data processing volume. Examine the financial software's interface configuration and determine its interaction mode. For example, if the software has an API, identify it as supporting API interaction; if the software has a user interface, identify it as supporting user interface interaction. Integrate the identified financial business processing type, data processing volume, and interaction mode into software functional features to provide a basis for subsequent compatibility testing. Based on the information in the software feature library and pre-set mapping rules, the financial device type used by each financial software application is determined. For example, for financial software that processes large amounts of data and supports API interaction, the mapping rules determine that the corresponding financial device type is a high-performance server. Based on the determined financial device type, the operating parameters of the device are extracted from the device operating parameter database.Operating parameters include CPU usage, memory occupancy, disk read / write speed, etc. These parameters can reflect the operating status and performance of the device. The extracted operating parameters are integrated into financial device information. Based on the functional characteristics of the financial software and the operating parameters of the financial device, for example, for accounting and report processing software, the corresponding financial device CPU usage is required to be no more than 80%, memory occupancy no more than 70%, and disk read / write speed no less than 100MB / s. Perform compatibility testing on each software in the financial software list and the corresponding financial device. According to the compatibility testing rules, compare the functional characteristics of the software with the operating parameters of the device one by one to see if they meet the requirements. For example, if the software has a large data processing volume, but the disk read / write speed of the corresponding device is lower than the required value, it is determined that the software and the device are incompatible, and the test results are recorded to form compatibility data.

[0095] Preferably, step S25 includes the following steps:

[0096] Step S251: Detecting the interface types between the financial software list and the financial device information one by one to obtain the interface type compatibility; if the interface types do not match, record the specific type and reason of the mismatch and set an interface matching parameter, where a value of 1 indicates a complete match and a value of 0 indicates a complete mismatch;

[0097] Step S252: The data transmission formats between the financial software list and the financial device information are detected one by one to obtain the degree of transmission format compatibility; if the data transmission formats are inconsistent, the specific format type and difference of the inconsistency are recorded, and a data format matching parameter is set. The data format matching parameter is 1 for a complete match and 0 for a complete mismatch;

[0098] Step S253: Check the power supply stability between the financial software list and the financial device information one by one to obtain the power supply adaptability of the device; record whether the power supply voltage is within the range of 220V±10%, and set the power supply voltage matching parameter. The power supply voltage matching parameter is 1, indicating a complete match, and 0, indicating a complete mismatch.

[0099] Step S254: Integrate the interface type adaptability, transmission format adaptability, and device power adaptability to obtain adaptability data.

[0100] In this embodiment of the present invention, the interface type information for each software item is extracted from the financial software list. This information is stored in the software configuration file in the field named "interface_type." Simultaneously, the interface types supported by the device are extracted from the financial device information. This information is stored in the device operating parameter database in the field named "supported_interface." The interface_type field of the financial software item is compared with the supported_interface field of the financial device item. If the two match, the interface type adaptation level is recorded as a complete match, and the interface matching parameter is set to 1. If the two do not match, the specific type and reason for the mismatch are recorded. For example, if the software interface type is "API interface" and the device supports "serial interface," the mismatch type is recorded as "API interface and serial interface mismatch," and the interface matching parameter is set to 0. The data transmission format information for each software item is extracted from the financial software list. This information is stored in the software configuration file in the field named "data_format." Simultaneously, the data transmission format supported by the device is extracted from the financial device information. This information is stored in the device operating parameter database in the field named "supported_data_format." The data_format field of the financial software item is compared with the supported_data_format field of the financial device item. If the two are consistent, the transmission format adaptation is recorded as a perfect match, and the data format matching parameter is set to 1. If the two are inconsistent, the specific format type and difference are recorded. For example, if the software data transmission format is "JSON format" and the device supports "XML format," the inconsistent format type is recorded as "JSON format and XML format are inconsistent," and the data format matching parameter is set to 0. The device's supply voltage information is extracted from the financial device information. This information is stored in the device operating parameter database in the field "supply_voltage." Each supply voltage is checked to see if it is within the range of 220V ± 10%, that is, between 198V and 242V. If the supply voltage is within this range, the device power supply adaptation is recorded as a perfect match, and the supply voltage matching parameter is set to 1. If the supply voltage is not within this range, the specific value of the supply voltage is recorded, and the supply voltage matching parameter is set to 0. The test results for interface type adaptation, transmission format adaptation, and device power supply adaptation are integrated.The specific operation is to create a new data table, compatibility_data, containing the fields software_id (financial software identifier), equipment_type (financial equipment type), interface_compatibility (interface type compatibility), data_format_compatibility (transmission format compatibility), supply_voltage_compatibility (equipment power supply compatibility), and overall_compatibility (overall compatibility). The interface compatibility parameters, data format compatibility parameters, and supply voltage compatibility parameters for each financial software and financial equipment combination are stored in their respective fields. The overall compatibility is calculated based on preset weights to form complete compatibility data.

[0101] Preferably, step S3 includes classifying the adaptability data into adaptability levels, and monitoring the high-frequency migration status of the financial data according to the adaptability levels as follows:

[0102] If all the features in the adaptability data are completely matched, the adaptability level is marked as level one adaptability data;

[0103] If any of the feature quantities in the adaptability data is completely mismatched, the adaptability level will be marked as level 2 adaptability data;

[0104] If all the feature quantities in the adaptability data are completely mismatched, the adaptability level is marked as level 3 adaptability data;

[0105] Integrate the first-level adaptation data, the second-level adaptation data, and the third-level adaptation data to obtain the adaptation level;

[0106] Based on the adaptation level, the high-frequency migration data of financial data is monitored, and the adaptation level and the high-frequency migration data are matched to the level migration amount to obtain the level migration amount data;

[0107] Map the level migration data to high-frequency migration status.

[0108] In an embodiment of the present invention, adaptability data is read from an adaptability data storage system. This data records characteristic quantities such as interface type adaptability, transmission format adaptability, and device power supply adaptability. Specifically, a data query statement is used to extract all relevant records from the adaptability data table, including the adaptability characteristic quantity values ​​for each financial software and financial device combination. For each adaptability data record, the values ​​of the three characteristic quantities (interface type adaptability, transmission format adaptability, and device power supply adaptability) are checked one by one. If the values ​​of all three characteristic quantities are a preset complete match indicator (e.g., 1), the adaptability level of the record is marked as level 1 adaptability data. Specifically, a new field "Adaptability Level" is added to the adaptability data table and the value of this field is set to "Level 1." If any of the three characteristic quantities has a preset complete mismatch indicator (e.g., 0), the adaptability level of the record is marked as level 2 adaptability data. Specifically, the value of the "Adaptability Level" field is set to "Level 2." If the values ​​of all three characteristic quantities are a preset complete mismatch indicator, the adaptability level of the record is marked as level 3 adaptability data. The specific operation is to set the value of the "Adaptation Level" field to "Level 3." All records marked as Level 1, Level 2, and Level 3 adaptation data are consolidated to form a complete set of adaptation level data. Specifically, all records with adaptation levels of "Level 1," "Level 2," and "Level 3" are read from the adaptation level data storage system and merged into a new data table containing fields such as financial software identifier, financial device type, and adaptation level. Based on the actual needs of financial data migration, monitoring parameters for high-frequency migrations are set, such as a threshold for the number of migrations per unit time. Specifically, parameters are set in the migration monitoring system. For example, a threshold of more than 10 migrations per hour is considered high-frequency migration. Based on the set monitoring parameters, the financial data migration status is monitored in real time, recording information such as the time of each migration and the amount of data migrated. Specifically, the data migration monitoring tool captures financial data migration events in real time, records information such as the timestamp and amount of data migrated, and stores this information in the migration data monitoring log. Based on the monitored migration data, it is determined whether the conditions for high-frequency migration are met. Specifically, data is read from the migration data monitoring log and the migration count for each financial software application is calculated based on a set time unit (e.g., hourly). If the migration count exceeds a set threshold, the financial software application is marked as having a high-frequency migration status and this status information is stored in the migration status data table. Based on the characteristics of the adaptation level and high-frequency migration data, a corresponding rule for the migration amount is established, clarifying the migration amount identifiers for different adaptation levels in the case of high-frequency migration.The specific operation is to define rules in the system. For example, for the first-level adaptation data that is in a high-frequency migration state, it is marked as "first-level high-frequency migration amount"; for the second-level adaptation data that is in a high-frequency migration state, it is marked as "second-level high-frequency migration amount"; for the third-level adaptation data that is in a high-frequency migration state, it is marked as "third-level high-frequency migration amount". According to the set corresponding rules, the adaptation level is matched with the high-frequency migration data to obtain the corresponding level migration amount data, and these data are stored in the level migration amount data storage system. The specific operation is to read data from the adaptation level data storage system and the migration status data table, match the adaptation level and migration status of each financial software according to the defined corresponding rules, generate level migration amount data, and store it in the level migration amount data table, which contains fields such as financial software identification, adaptation level, migration status, and level migration amount. According to the meaning and characteristics of the level migration amount data, set the rules for mapping the level migration amount data to the high-frequency migration state to ensure that the mapping result can accurately reflect the actual status of the migration. The specific operation is to define mapping rules in the system. For example, mapping "level 1 high-frequency migration amount" to "stable high-frequency migration state", mapping "level 2 high-frequency migration amount" to "medium high-frequency migration state", and mapping "level 3 high-frequency migration amount" to "unstable high-frequency migration state"; according to the set mapping rules, the level migration amount data is mapped to the high-frequency migration state, and the mapped high-frequency migration state is stored in the high-frequency migration state data storage system. The specific operation is to read data from the level migration amount data table, convert the level migration amount data into the high-frequency migration state according to the defined mapping rules, and store the converted state information in the high-frequency migration state data table.

[0109] Preferably, in step S3, recording the operating condition signal of the financial device according to the high-frequency migration state and detecting the abnormal characteristics of the device in the operating condition signal include:

[0110] Based on the high-frequency migration state, the operating condition signal of the financial equipment is recorded every 30 seconds. The operating condition signal includes the equipment's jitter resonance frequency, vibration intensity, and equipment load operating heat;

[0111] If the vibration frequency is abnormal around 22-24Hz, it is marked as abnormal device resonance;

[0112] If the vibration intensity exceeds 12 mm / s for three consecutive times, it will be marked as abnormal equipment intensity;

[0113] If the device load operating temperature exceeds 85°C, it will be marked as abnormal device load;

[0114] Equipment resonance anomalies, equipment intensity anomalies and equipment load anomalies are integrated and recorded as equipment anomaly features.

[0115] In this embodiment of the present invention, upon detecting a high-frequency migration of financial data, the operating condition signal monitoring system is activated. This system uses sensors installed on financial equipment to collect operating condition signals from the equipment every 30 seconds, including the equipment's vibration resonance frequency, vibration severity, and load operating heat. The vibration resonance frequency is measured by a vibration sensor, recording the device's vibration frequency in Hz; the vibration severity is measured by an acceleration sensor, recording the device's vibration intensity in mm / s; and the load operating heat is measured by a thermocouple sensor, recording the device's operating temperature in °C. The collected operating condition signals are stored in an operating condition signal database. Each record contains information such as a timestamp, device identifier, vibration resonance frequency, vibration severity, and load operating heat. The device's vibration resonance frequency data is read from the operating condition signal database. For each device, the system checks whether its vibration resonance frequency is within the 22-24 Hz range. If the vibration resonance frequency is within this range and persists for a period exceeding a preset threshold (for example, three consecutive measurements within this range), the device is marked as having "device resonance abnormality." The "equipment resonance abnormality" mark is stored in the equipment abnormality feature database, and the time when the abnormality occurred, the equipment identification and the abnormality type are recorded. The vibration intensity data of the equipment is read from the working condition signal database. For each equipment, check whether its vibration intensity exceeds 12mm / s. If the vibration intensity exceeds 12mm / s for three consecutive measurements, the equipment is marked as "equipment intensity abnormality". The "equipment intensity abnormality" mark is stored in the equipment abnormality feature database, and the time when the abnormality occurred, the equipment identification and the abnormality type are recorded. The load operating heat data of the equipment is read from the working condition signal database. For each equipment, check whether its operating heat exceeds 85℃. If the equipment load operating heat exceeds 85℃, the equipment is marked as "equipment load abnormality". The "equipment load abnormality" mark is stored in the equipment abnormality feature database, and the time when the abnormality occurred, the equipment identification and the abnormality type are recorded. All marked abnormality features are read from the equipment abnormality feature database, including equipment resonance abnormality, equipment intensity abnormality and equipment load abnormality. These abnormal features are integrated into a comprehensive device abnormal feature record, each record contains information such as device identification, abnormality type, abnormality occurrence time, etc. The integrated device abnormal feature record is stored in the device abnormal feature database.

[0116] Preferably, the step S3 of adjusting the migration mode of the financial device based on the abnormal characteristics of the device includes:

[0117] For equipment resonance anomalies, reduce the data migration frequency of financial equipment and increase the migration interval to 90 seconds to obtain the resonance anomaly migration adjustment amount;

[0118] For abnormal equipment vibration intensity, adjust the batch size of data migration for financial equipment and adjust the data migration priority, prioritizing the migration of critical data while reducing the migration frequency of non-critical data to obtain the migration adjustment amount for abnormal intensity.

[0119] For abnormal equipment load, adjust the time window for data migration of financial equipment to obtain the load abnormality migration adjustment amount;

[0120] The financial equipment adjustment migration mode is determined based on the resonance abnormality migration adjustment amount, the intensity abnormality migration adjustment amount, and the load abnormality migration adjustment amount.

[0121] In this embodiment of the present invention, device resonance anomaly records are read from the device anomaly signature database to identify financial devices experiencing abnormal resonance. For devices experiencing abnormal resonance, the data migration frequency is adjusted. Specifically, the data migration interval is increased from the original setting (e.g., 30 seconds) to 90 seconds. Migration task scheduling parameters are modified to ensure that the trigger interval for each migration task complies with the new setting. The difference between the adjusted migration interval and the original interval is recorded as the resonance anomaly migration adjustment. For example, if the original interval is 30 seconds and the adjusted interval is 90 seconds, the adjustment is 60 seconds. This adjustment is stored in the migration adjustment database, recording information such as the device identifier, adjustment type (resonance anomaly), and adjustment amount. Device vibration severity anomaly records are read from the device anomaly signature database to identify financial devices experiencing abnormal vibration severity. For devices experiencing abnormal vibration severity, the data migration batch size is adjusted. Specifically, the amount of data migrated each time is reduced based on the device's current performance. For example, the batch size of each migration data can be reduced from 100 records to 50 records. Simultaneously, the data migration priority is adjusted to prioritize critical data. Using a data classification algorithm, critical data and non-critical data are identified. Critical data maintains its original migration frequency, while the migration frequency of non-critical data is reduced. For example, the migration frequency of non-critical data can be adjusted from 10 times per hour to 5 times per hour. The changes in the adjusted migration batch size and non-critical data migration frequency are recorded as the severity anomaly migration adjustment. For example, the batch size adjustment is 50 records, and the migration frequency adjustment is 5 times per hour. These adjustments are stored in the migration adjustment database, recording the device identification, adjustment type (severity anomaly), and specific adjustment information. Device load anomaly records are read from the device anomaly feature database to identify financial devices with abnormal loads. For devices with abnormal loads, the data migration time window is adjusted. Specifically, the data migration time window is adjusted from all-day migration to migration during low-load periods. For example, the migration time window can be set from 10:00 PM to 6:00 AM the following day to avoid daytime periods when the device is operating at high loads. The difference between the adjusted time window and the original time window is recorded as the load anomaly migration adjustment. For example, if the original time window is 24 hours and is adjusted to 8 hours, the adjustment is 16 hours. This adjustment is stored in the migration adjustment database, recording information such as the device identification, adjustment type (load anomaly), and adjustment amount. The resonance anomaly migration adjustment, intensity anomaly migration adjustment, and load anomaly migration adjustment are read from the migration adjustment database. For each financial device, all relevant adjustment data is integrated; based on this integrated adjustment data, the adjustment migration pattern for the financial device is determined.For example, if a device experiences both resonance anomalies and severity anomalies, its migration mode will be adjusted to include reducing the migration frequency to 90 seconds, reducing the migration batch size to 50 records, and reducing the frequency of non-critical data migration to five times per hour. This adjusted migration mode will be applied to the corresponding financial device. By modifying the migration task configuration file and writing the new migration mode parameters to the device's migration task scheduling system, the device will execute data migration tasks according to the adjusted migration mode.

[0122] It is particularly important that the determination of the financial equipment adjustment migration mode based on the resonance abnormal migration adjustment amount, the severity abnormal migration adjustment amount, and the load abnormal migration adjustment amount includes:

[0123] Determine the vibration frequency and vibration characteristics of the motor rotor of the financial equipment based on the resonance abnormal migration adjustment amount;

[0124] Detecting the vibration direction of the motor rotor according to the vibration frequency and vibration characteristics; identifying the cantilever axial vibration of the motor rotor based on the vibration direction, determining the vibration parameters of the cantilever axial vibration, and generating the cantilever axial vibration mode;

[0125] Determine the power fluctuation characteristics of the motor rotor of the financial equipment based on the intensity abnormal migration adjustment amount; detect the power fluctuation soundprint of the motor rotor based on the power fluctuation characteristics to obtain the rotor power soundprint pattern;

[0126] Determining an overheat load characteristic of a motor rotor of the financial device based on the load abnormality migration adjustment amount; detecting an abnormal motor surface temperature of the motor rotor based on the overheat load characteristic, and recording a motor temperature value of the abnormal motor surface temperature to obtain a motor temperature pattern;

[0127] The arm axial vibration mode, rotor power sound pattern and motor temperature mode are used to determine the financial equipment adjustment migration mode.

[0128] In this embodiment of the present invention, a high-precision vibration sensor is used to collect vibration signals from the rotor of a financial equipment motor. The sensor's sampling frequency is set to 10 kHz to ensure that the high-frequency characteristics of the rotor vibration can be captured. The collected vibration signals are analyzed in the frequency domain using a Fourier transform to extract the primary frequency components of the vibration signals. The rotor's vibration frequency adjustment value is calculated based on the resonance anomaly migration adjustment value. Based on the determined vibration frequency characteristics, a multi-channel vibration sensor is used to collect vibration signals in different directions of the motor rotor. The vibration direction is determined by analyzing the amplitude and phase relationship of the vibration signals in each direction. If the vibration amplitude in a particular direction is significantly higher than in other directions and the phase is stable, that direction is determined to be the vibration direction. For example, if the rotor exhibits cantilever axial vibration, the vibration amplitude in the axial direction will be significantly greater than the amplitude in the radial direction. After determining the vibration direction, the time-frequency characteristics of the vibration signal are further analyzed to identify cantilever axial vibration. Wavelet transform is used to perform time-frequency analysis on the vibration signal to extract vibration parameters of the cantilever axial vibration, including frequency, amplitude, and phase. For example, the frequency of cantilever axial vibration is typically related to the rotor speed, and its amplitude increases with increasing speed. These vibration parameters are integrated to generate the cantilever axial vibration mode. A high-precision power sensor is used to collect the motor rotor's power signal, with a sampling frequency set to 1kHz. Time-domain analysis is performed on the collected power signal, and the standard deviation and mean are calculated to determine the severity of the power fluctuation. The power fluctuation characteristics are determined based on the severity anomaly migration adjustment. The specific method is as follows: When the power fluctuation severity exceeds a set threshold, a power fluctuation anomaly is considered present. At this point, characteristic parameters such as the amplitude and frequency of the power fluctuation are recorded. A highly sensitive acoustic sensor is used to collect the motor rotor's operating sound signal at a sampling frequency of 44.1kHz. The collected sound signal is correlated with the power fluctuation characteristics to extract soundprint features related to the power fluctuation. Using soundprint feature extraction algorithms such as wavelet transform and empirical mode decomposition, the sound signal is decomposed into multiple intrinsic mode function components, and soundprint features corresponding to the power fluctuation characteristics are extracted. These soundprint features are then integrated to generate the rotor power soundprint pattern. The current and voltage signals of the motor rotor are collected through current sensors and voltage sensors to calculate the load power of the motor. The overheat load characteristics are determined based on the load abnormality migration adjustment amount. The specific method is as follows: when the load power exceeds a certain percentage of the rated power, it is considered that an overheat load abnormality exists. At this time, characteristic parameters such as the load power size and duration are recorded. The surface temperature of the motor rotor is monitored in real time using an infrared thermal imager with a sampling frequency of 1Hz. Based on the overheat load characteristics, the changing trend of the motor surface temperature is analyzed. When the motor surface temperature exceeds the set threshold, it is considered that an abnormal temperature exists. At this time, the temperature value of the motor surface is recorded. These temperature values ​​are integrated to obtain the motor temperature pattern.The aforementioned cantilever axial vibration mode, rotor power sound pattern, and motor temperature pattern are fused. Using a data fusion algorithm, such as the Kalman filter, weighted fusion of each pattern is performed to generate a financial equipment adjustment migration model. This model comprehensively reflects the operating status of the financial equipment motor rotor, providing a basis for equipment fault diagnosis and maintenance.

[0129] Preferably, the step S4 of adapting and optimizing the financial software used according to the adjusted migration mode includes:

[0130] Matching the adjusted migration mode with software operation parameter quantities to obtain software operation parameter quantities;

[0131] According to the software operation parameters, the data migration frequency of the data processing type unit of the financial software used is aligned to obtain the software adaptation frequency;

[0132] Synchronize the data migration batch size of the data processing unit of the financial software used according to the software operation parameters to obtain the software adaptation batch;

[0133] Optimize the migration time window of the data scheduling unit of the financial software used according to the software operation parameters to obtain the software optimization window;

[0134] Based on the software adaptation frequency, software adaptation batch and software optimization window, the financial software used is optimized accordingly to obtain financial optimization software.

[0135] In an embodiment of the present invention, the software's operating parameters are read from the financial software's configuration file. These parameters include the migration frequency of the data processing type unit, the migration batch size of the data processing volume unit, and the migration time window of the data scheduling unit. These parameters are then matched with the adjusted migration pattern. Specifically, for each financial software item, the migration frequency, batch size, and time window parameters in its configuration file are checked to see if they are consistent with the adjusted migration pattern. For example, if the adjusted migration pattern requires a migration frequency of 90 seconds, while the software configuration file specifies a migration frequency of 30 seconds, the software's migration frequency parameters are adjusted to 90 seconds. The matched software operating parameters are stored in a software operating parameter database. The migration frequency parameters of the data processing type unit of each financial software item are read from the software operating parameter database. For each software item, the migration frequency is adjusted to be consistent with the software operating parameter based on its data processing type. For example, if the software operating parameter specifies a migration frequency of 90 seconds, the migration frequency of the data processing type unit of that software item is adjusted to 90 seconds. After the adjustment is complete, the adjusted migration frequency is recorded as the software adaptation frequency and stored in the software adaptation frequency database. The migration batch size parameters of the data processing unit of each financial software are read from the software operation parameter database. For each software, the migration batch size is adjusted to be consistent with the software operation parameter based on its data processing capacity. For example, if the migration batch size in the software operation parameter is 50 records, the migration batch size of the data processing unit of the software is adjusted to 50 records. After the adjustment is completed, the adjusted migration batch size is recorded as the software adaptation batch and stored in the software adaptation batch database. The migration time window parameters of the data scheduling unit of each financial software are read from the software operation parameter database. For each software, the migration time window is adjusted to be consistent with the software operation parameter based on its data scheduling requirements. For example, if the migration time window in the software operation parameter is from 22:00 at night to 6:00 the next day, the migration time window of the data scheduling unit of the software is adjusted to this time period. After the adjustment is completed, the adjusted migration time window is recorded as the software optimization window and stored in the software optimization window database. The adaptation frequency, adaptation batch, and optimization window parameters for each financial software are read from the software adaptation frequency database, software adaptation batch database, and software optimization window database. For each software, an optimization configuration is performed based on these parameters. For example, the software's migration task scheduler is configured to execute migration tasks according to the adaptation frequency, divide the amount of data to be migrated each time according to the adaptation batch size, and execute the migration task within the optimization window. After the optimization configuration is completed, the optimized software is stored in the financial optimization software library, resulting in the financial optimization software.

[0136] Preferably, in step S4, the standard financial data is transferred to the financial optimization software for multi-dimensional data fusion, and the financial data view is constructed, including:

[0137] Determine the transmission priority and transmission time window of the financial data based on the standard financial data to obtain a transmission task list;

[0138] Transfer standard financial data to the financial optimization software based on the transfer task list, and perform data fusion according to financial types to generate financial type fusion data;

[0139] Conduct financial analysis on financial type fusion data and generate financial analysis reports;

[0140] Output financial analysis reports into visual chart formats to build a view of financial data.

[0141] In an embodiment of the present invention, standard financial data, including financial statement data, transaction detail data, and budget data, is read from a standard financial data storage system. Each data type has a corresponding identification field. The transmission priority of the financial data is determined based on its type and importance. For example, financial statement data is assigned a high priority because it involves key financial indicators; transaction detail data is assigned a medium priority because it is large in volume but relatively less important; and budget data is assigned a low priority because it is updated less frequently. Priority is represented by a numerical value, with high priority being 1, medium priority being 2, and low priority being 3. The transmission time window for the financial data is determined based on its frequency of use and business needs. For example, the transmission time window for financial statement data is set from 9:00 AM to 11:00 AM on weekdays because it needs to be used for decision-making in a timely manner; the transmission time window for transaction detail data is set from 10:00 PM to 6:00 AM the following day because it is large in volume; and the transmission time window for budget data is set to the first week of each month because it is updated less frequently. The transmission priority and transmission time window information for the standard financial data are integrated into a transmission task list. The transmission task list contains fields such as data type, priority, and time window. Standard financial data is transmitted to the financial optimization software based on the priority and time window information in the transfer task list. Data encryption technology is used during the transmission process to ensure data security. For example, data transmission tasks are prioritized within the transfer time window for high-priority data, and the data is encrypted using the AES-256 algorithm. Within the financial optimization software, the transmitted standard financial data is fused according to financial type. Specifically, data of the same financial type is aggregated and consolidated. For example, all financial statement data is aggregated chronologically to generate comprehensive financial statement data; transaction details are categorized and consolidated by business type to generate transaction data by business type; and the fused data is stored as financial type fused data. Financial analysis tools are used to analyze the financial type fused data. For example, financial ratio analysis is performed on the comprehensive financial statement data to calculate key financial indicators such as the current ratio and debt-to-asset ratio; trend analysis is performed on the transaction data by business type to identify growth trends and abnormal fluctuations; and the analysis results are compiled into a financial analysis report that includes the numerical values ​​of key financial indicators, trend analysis charts, and business growth information. Reports are stored in text format and contain detailed analysis results and conclusions. Key data from financial analysis reports can be output as visual charts using data visualization tools. For example, financial ratio analysis results can be generated as a bar chart, while business growth trends can be generated as a line chart. These visual charts are then integrated into financial data views, which are presented as web pages or reports. Users can use interactive interfaces to view analysis results and trends for different financial data types. Views support a variety of chart types, such as bar charts, line charts, and pie charts, enabling users to intuitively understand and analyze financial data.

[0142] Of particular importance, in step S4, transferring the standard financial data to the financial optimization software for multi-dimensional data fusion and constructing a financial data view also includes:

[0143] Transfer standard financial data to financial optimization software for layered processing, dividing the data into basic layer, middle layer and application layer. The basic layer stores the original data, the middle layer performs data conversion and formatting, and the application layer is used for the final presentation of the data.

[0144] Perform preliminary screening of base layer data to remove duplicate and invalid data, and record data characteristics during the screening process, including data type, data length, and data format;

[0145] The filtered data is grouped according to preset rules, including time series grouping, data volume grouping, and data type grouping. Each group of data is assigned a unique identifier;

[0146] Perform multi-dimensional fusion processing on the grouped data. The fusion dimensions include time dimension, space dimension, and logical dimension. Time dimension fusion involves aligning the timestamps of the data, space dimension fusion involves associating the spatial locations of the data, and logical dimension fusion involves mapping the logical relationships between the data.

[0147] During the fusion process, data is dynamically verified for consistency, integrity, and timeliness. If any data anomalies are found, the data repair process is automatically triggered. The repair process includes data filling, data correction, and data update.

[0148] A data view is constructed based on the fused data. The view includes visual display, data table and text description. The visual display uses dynamic charts and interactive maps, the data table provides a detailed data list, and the text description summarizes and analyzes the data.

[0149] In an embodiment of the present invention, standard financial data is transmitted to financial optimization software for layered processing, with the data divided into a base layer, an intermediate layer, and an application layer. The base layer stores raw data and uses a relational database (such as MySQL) to ensure data integrity and originality. The intermediate layer is responsible for data conversion and formatting. It uses ETL (Extract, Transform, Load) tools to clean, transform, and load data, remove duplicate and invalid data, and record data characteristics during the screening process, including data type, data length, and data format. The application layer is responsible for the final presentation of the data and stores the processed data in a high-performance storage system (such as Redis or Elasticsearch). The base layer data is initially screened, and data quality tools (such as the Pandas library) are used to remove duplicate and invalid data. During the screening process, data characteristics are recorded, including data type (such as string, number, date, etc.), data length (number of characters or bytes), and data format (such as JSON, CSV, etc.). The filtered data is grouped according to preset rules, including time series grouping (by year, month, day, etc.), data volume grouping (by data size or number of records), and data type grouping (such as financial data, transaction data, etc.). Each data set is assigned a unique identifier generated using a UUID (Universally Unique Identifier). The grouped data is then fused across multiple dimensions, including time, space, and logical dimensions. Time dimension fusion involves aligning data timestamps. Using a timestamp standardization algorithm, data in different time formats are unified into a standard time format (such as ISO 8601). Spatial dimension fusion involves associating data with their spatial locations. Geographic Information System (GIS) technology is used to associate data with geographic location information. Logical dimension fusion involves mapping logical relationships between data, achieved by establishing association rules (such as primary key-foreign key relationships). During the fusion process, data is dynamically validated for consistency (such as whether field values ​​comply with business rules), completeness, and timeliness (such as whether the data is up-to-date). If data anomalies are detected, a data repair process is automatically triggered. This process includes data filling (such as filling missing data with default values ​​or estimated values), data correction (such as correcting incorrect field values), and data updating (such as replacing outdated data with the latest data). Data views are constructed based on the fused data, including visual displays, data tables, and text descriptions. Visual presentations utilize dynamic charts (e.g., line charts and bar charts) and interactive maps (e.g., geographic heatmaps) using data visualization tools such as Tableau or Power BI. Data tables provide detailed data lists with paging and sorting support. Textual descriptions summarize and analyze the data, generating data reports using natural language processing techniques such as NLTK or spaCy.

[0150] In this specification, a financial data full-process management system based on data fusion is provided, which is used to implement the above-mentioned financial data full-process management method based on data fusion. The financial data full-process management system based on data fusion includes:

[0151] The financial data collection module is used to collect original financial data; clean the original financial data to obtain standard financial data; and determine business type information based on the standard financial data;

[0152] The software and device detection module is used to identify the financial software used based on the business type information and generate a financial software list; map the financial devices according to the financial software list to obtain financial device information; and perform compatibility testing on the financial software list and the financial device information to obtain compatibility data;

[0153] The financial equipment adjustment module is used to classify adaptability data into adaptability levels and monitor the high-frequency migration status of financial data based on the adaptability level; record the operating condition signals of financial equipment based on the high-frequency migration status, detect equipment abnormality characteristics of the operating condition signals, and adjust the migration mode of financial equipment based on the equipment abnormality characteristics;

[0154] The data fusion management module is used to adapt and optimize the financial software used according to the adjusted migration model to obtain financial optimization software; transmit standard financial data to the financial optimization software for multi-dimensional data fusion and construct a financial data view.

[0155] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0156] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A financial data full-process management method based on data fusion, characterized in that: The following steps are involved: Step S1: Collecting original financial data; performing data cleaning on the original financial data to obtain standard financial data; Determine business type information based on standard financial data; Step S2: Identify the financial software used based on the business type information and generate a financial software list; map the financial device according to the financial software list to obtain financial device information; perform compatibility testing on the financial software list and the financial device information to obtain compatibility data; Step S3: Classify the adaptability data into adaptability levels, and monitor the high-frequency migration status of the financial data based on the adaptability levels; record the operating condition signals of the financial equipment based on the high-frequency migration status, and detect abnormal characteristics of the equipment in the operating condition signals; Adjust the migration mode for financial equipment based on abnormal equipment characteristics; Step S4: Adapt and optimize the used financial software according to the adjusted migration mode to obtain financial optimization software; Transfer standard financial data to financial optimization software for multi-dimensional data integration and build financial data views.

2. The method for managing the entire financial data process based on data fusion according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: calling and starting the full-process data interface of the financial system to obtain original financial data; Step S12: Clean the original financial data to obtain standard financial data, where data cleaning includes standardizing data formats, correcting data logic, and completing data association values. Step S13: extracting financial category information and financial object information from the standard financial data; matching the financial category information and financial object information with business project relationships to obtain business project relationship data; Step S14: Mark the business project relationship data with the start and end timestamps, and determine the business project cycle based on the start and end timestamps; identify the business type of the standard financial data based on the business project cycle to obtain business type information.

3. The method for managing the entire financial data process based on data fusion according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Divide the business type information into business process nodes, determine the business process links according to the business process nodes; and obtain the financial software used in the links according to the business process links; Step S22: marking the software parameter characteristics of the financial software used in the link and generating a financial software list; Step S23: Identify the financial business processing type, data processing volume, and interaction mode of the financial software list to form software functional characteristics; Step S24: determining the type of financial device used by the software according to the software function feature library, and extracting the financial device operating parameters based on the financial device type to obtain financial device information; Step S25: Perform compatibility testing on the financial software list and financial device information to obtain compatibility data.

4. The method for managing the entire financial data process based on data fusion according to claim 3 is characterized in that: Step S25 includes the following steps: Step S251: Detecting the interface types between the financial software list and the financial device information one by one to obtain the interface type compatibility; if the interface types do not match, record the specific type and reason of the mismatch and set an interface matching parameter, where a value of 1 indicates a complete match and a value of 0 indicates a complete mismatch; Step S252: The data transmission formats between the financial software list and the financial device information are detected one by one to obtain the degree of transmission format compatibility; if the data transmission formats are inconsistent, the specific format type and difference of the inconsistency are recorded, and a data format matching parameter is set. The data format matching parameter is 1 for a complete match and 0 for a complete mismatch; Step S253: Check the power supply stability between the financial software list and the financial device information one by one to obtain the power supply adaptability of the device; record whether the power supply voltage is within the range of 220V±10%, and set the power supply voltage matching parameter. The power supply voltage matching parameter is 1, indicating a complete match, and 0, indicating a complete mismatch. Step S254: Integrate the interface type adaptability, transmission format adaptability, and device power adaptability to obtain adaptability data.

5. The method for managing the entire financial data process based on data fusion according to claim 1, characterized in that: Step S3 includes classifying the adaptability data into adaptability levels and monitoring the high-frequency migration status of the financial data according to the adaptability levels as follows: If all the features in the adaptability data are completely matched, the adaptability level is marked as level one adaptability data; If any of the feature quantities in the adaptability data is completely mismatched, the adaptability level will be marked as level 2 adaptability data; If all the feature quantities in the adaptability data are completely mismatched, the adaptability level is marked as level 3 adaptability data; Integrate the first-level adaptation data, the second-level adaptation data, and the third-level adaptation data to obtain the adaptation level; Based on the adaptation level, the high-frequency migration data of financial data is monitored, and the adaptation level and the high-frequency migration data are matched to the level migration amount to obtain the level migration amount data; Map the level migration data to high-frequency migration status.

6. The method for managing the entire financial data process based on data fusion according to claim 1, characterized in that: In step S3, recording the operating condition signal of the financial equipment according to the high-frequency migration state and detecting the abnormal characteristics of the equipment in the operating condition signal include: Based on the high-frequency migration state, the operating condition signal of the financial equipment is recorded every 30 seconds. The operating condition signal includes the equipment's jitter resonance frequency, vibration intensity, and equipment load operating heat; If the vibration frequency is abnormal around 22-24Hz, it is marked as abnormal device resonance; If the vibration intensity exceeds 12 mm / s for three consecutive times, it will be marked as abnormal equipment intensity; If the device load operating temperature exceeds 85°C, it will be marked as abnormal device load; Equipment resonance anomalies, equipment intensity anomalies and equipment load anomalies are integrated and recorded as equipment anomaly features.

7. The method for managing the entire financial data process based on data fusion according to claim 6, characterized in that: Adjusting the migration mode of financial equipment based on equipment abnormality characteristics in step S3 includes: For equipment resonance anomalies, reduce the data migration frequency of financial equipment and increase the migration interval to 90 seconds to obtain the resonance anomaly migration adjustment amount; For abnormal equipment vibration intensity, adjust the batch size of data migration for financial equipment and adjust the data migration priority, prioritizing the migration of critical data while reducing the migration frequency of non-critical data to obtain the migration adjustment amount for abnormal intensity. For abnormal equipment load, adjust the data migration time window of the financial equipment to obtain the load abnormality migration adjustment amount; The financial equipment adjustment migration mode is determined based on the resonance abnormality migration adjustment amount, the intensity abnormality migration adjustment amount, and the load abnormality migration adjustment amount.

8. The method for managing the entire financial data process based on data fusion according to claim 1, characterized in that: Adapting and optimizing the financial software used according to the adjusted migration mode in step S4 includes: Matching the adjusted migration mode with software operation parameter quantities to obtain software operation parameter quantities; According to the software operation parameters, the data migration frequency of the data processing type unit of the financial software used is aligned to obtain the software adaptation frequency; Synchronize the data migration batch size of the data processing unit of the financial software used according to the software operation parameters to obtain the software adaptation batch; Optimize the migration time window of the data scheduling unit of the financial software used according to the software operation parameters to obtain the software optimization window; Based on the software adaptation frequency, software adaptation batch and software optimization window, the financial software used is optimized accordingly to obtain financial optimization software.

9. The method for managing the entire financial data process based on data fusion according to claim 7, characterized in that: In step S4, the standard financial data is transferred to the financial optimization software for multi-dimensional data fusion and the financial data view is constructed, including: Determine the transmission priority and transmission time window of the financial data based on the standard financial data to obtain a transmission task list; Transfer standard financial data to the financial optimization software based on the transfer task list, and perform data fusion according to financial types to generate financial type fusion data; Conduct financial analysis on financial type fusion data and generate financial analysis reports; Output financial analysis reports into visual chart formats to build a view of financial data.

10. A financial data full-process management system based on data fusion, characterized in that: The method for managing the entire financial data process based on data fusion according to claim 1 is used to implement the method, the method comprising: The financial data collection module is used to collect original financial data; clean the original financial data to obtain standard financial data; and determine business type information based on the standard financial data; The software and device detection module is used to identify the financial software used based on the business type information and generate a financial software list; map the financial devices according to the financial software list to obtain financial device information; and perform compatibility testing on the financial software list and the financial device information to obtain compatibility data; The financial equipment adjustment module is used to classify adaptability data into adaptability levels and monitor the high-frequency migration status of financial data based on the adaptability level; record the operating condition signals of financial equipment based on the high-frequency migration status, detect equipment abnormality characteristics of the operating condition signals, and adjust the migration mode of financial equipment based on the equipment abnormality characteristics; The data fusion management module is used to adapt and optimize the financial software used according to the adjusted migration model to obtain financial optimization software; transmit standard financial data to the financial optimization software for multi-dimensional data fusion and construct a financial data view.

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