Financial data synchronization method and system

Abstract

The present invention relates to the field of data synchronization technology, and in particular to a financial data synchronization method and system. The method comprises the following steps: constructing a structured financial metamodel; monitoring synchronization requests of a financial system, and performing intelligent conversion of heterogeneous multi-source data through a structured financial metamodel to generate source financial synchronization data; performing financial chaos mapping eigenvalue processing on the source financial synchronization data to generate financial chaos eigenvalue data; identifying financial synchronization key factors based on the financial chaos eigenvalue data, and encoding a chaotic system drive signal to generate an encrypted financial synchronization drive signal; performing intelligent financial data synchronization processing based on the encrypted financial synchronization drive signal to generate intelligent synchronization status data. The present invention realizes precise synchronization of financial data across systems through a financial synchronization drive signal.

Description

Technical Field

[0001] The present invention relates to the technical field of data synchronization, and in particular to a financial data synchronization method and system. Background Art

[0002] With the rapid development of information technology, the financial management of enterprises has gradually shifted towards digitalization and networking. The accuracy, timeliness and consistency of financial data are crucial to the decision-making and operational management of enterprises. In the existing technology, financial data usually comes from multiple systems, such as sales management system, procurement management system, inventory management system, etc. These systems operate independently, resulting in scattered and inconsistent financial data, which brings many challenges to financial management. However, the traditional financial data synchronization method mainly relies on manual operation, that is, financial personnel need to extract data from various systems, sort and clean them, and then input them into the financial management system. This method is not only inefficient and error-prone, but also cannot meet the needs of real-time financial management. In addition, with the expansion of enterprise scale and the complexity of business, the data format and structure between different systems are quite different, which makes data synchronization more difficult and makes it impossible to monitor and correct the integrity of synchronized data. Summary of the invention

[0003] Based on this, the present invention provides a financial data synchronization method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a financial data synchronization method includes the following steps:

[0005] Step S1: monitor the synchronization request of the financial system and generate data synchronization request events; construct a structured financial metamodel; perform intelligent conversion of heterogeneous multi-source data on the data synchronization request events through the structured financial metamodel to generate source financial synchronization data;

[0006] Step S2: Perform perturbation sensitivity analysis on the source financial synchronization data to obtain the financial sensitive disturbance coefficient; construct the chaotic phase space based on the financial sensitive disturbance coefficient to generate the financial chaotic phase space matrix data; perform financial chaos mapping eigenvalue processing on the source financial synchronization data according to the financial chaotic phase space matrix data to generate the financial chaotic eigenvalue data;

[0007] Step S3: Monitor the data change log according to the data synchronization request event to generate source financial change monitoring data; use the source financial change monitoring data to identify the financial synchronization key factors of the financial chaos eigenvalue data to generate financial synchronization key factor data; encode the chaotic system driving signal according to the financial synchronization key factor data to generate an encrypted financial synchronization driving signal;

[0008] Step S4: securely transmit the encrypted financial synchronization drive signal to the target data synchronization terminal, and perform signal decryption processing to generate a decrypted financial synchronization drive signal; perform target synchronization data matching on the decrypted financial synchronization drive signal to generate precisely matched target synchronization data; perform intelligent financial data synchronization processing based on the precisely matched target synchronization data to generate intelligent synchronization status data.

[0009] The present invention monitors the synchronization request of the financial system, monitors the synchronization request and generates events to ensure that the system can respond to the data synchronization demand in time, avoid the risk caused by data synchronization delay, and ensure the real-time and accuracy of financial data. The structured financial metamodel is constructed to standardize the representation structure of financial data, simplify the data processing process, and improve the efficiency and accuracy of data synchronization. Intelligent conversion of heterogeneous multi-source data ensures the compatibility and consistency of data from different sources, and reduces the possibility of errors in the data conversion process. Perturbation sensitivity analysis is performed on the source financial synchronization data, and the security and unpredictability of the data are enhanced based on the chaotic phase space construction, which effectively prevents the illegal acquisition and tampering of the data. The generation of financial chaotic eigenvalue data makes the financial data more secure and unpredictable, and improves the confidentiality and integrity of the data. Data change log monitoring is performed according to the data synchronization request event, so that the change of data can be discovered in time. The identification of key factors of financial synchronization improves the accuracy and efficiency of data synchronization, avoids unnecessary data transmission and processing, and improves the performance of the system. Chaotic system drive signal encoding enhances the security and confidentiality of data transmission. The decrypted drive signal is accurately matched with the target data system, ensuring the accuracy and consistency of data synchronization and the seamless connection of data between different systems. Accurately matching the target synchronization data ensures the accuracy and consistency of data synchronization and avoids the risks brought by data inconsistency. Therefore, a financial data synchronization method of the present invention standardizes the data format and structure of different systems by constructing a structured financial meta-model. Chaos theory is used to construct a phase space to handle complex financial data changes. Through chaotic mapping processing, chaotic transformation is applied to the original financial data, which can convert sensitive financial information into unintuitive chaotic eigenvalues, ensuring that no data loss or errors occur during the synchronization process. The chaotic eigenvalues ​​are used to perform synchronization data binding and key synchronization factor identification on the synchronization data to obtain a synchronization drive signal, and accurate cross-system financial data synchronization is achieved based on the synchronization drive signal.

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

[0011] Step S11: monitor the synchronization request of the financial system and generate a data synchronization request event, wherein the data synchronization request event includes a source data request end, a target data synchronization end and synchronization request data;

[0012] Step S12: Performing request legitimacy verification according to the synchronization request data to generate synchronization request verification data;

[0013] Step S13: when the synchronization request verification data is successful, the source data request end is parsed for the request source format to obtain the heterogeneous data source format data; when the synchronization request verification data is failed, the data synchronization request is rejected and the process returns to step S11 to continue the synchronization request monitoring;

[0014] Step S14: constructing a structured financial meta-model according to the data in the format of heterogeneous data sources;

[0015] Step S15: Real-time data coordination and collection is performed on the source data request end through synchronization request verification data to obtain original financial synchronization data;

[0016] Step S16: Use the structured financial meta-model to perform intelligent conversion of heterogeneous multi-source data on the original financial synchronization data to generate source financial synchronization data.

[0017] The present invention can capture and identify data synchronization requirements in real time by monitoring the synchronization request of the financial system, ensuring that the synchronization request of financial data can be processed in time. By monitoring the synchronization request of the financial system, the data synchronization requirements can be captured and identified in real time, ensuring that the synchronization request of financial data can be processed in time. By monitoring the synchronization request of the financial system, the data synchronization requirements can be captured and identified in real time, ensuring that the synchronization request of financial data can be processed in time. By constructing a structured financial metamodel based on heterogeneous data source format data, the data structure of different sources can be standardized and unified, and the data processing efficiency and accuracy can be improved. Real-time data collection can reflect the latest financial information and ensure the real-time requirements of data synchronization. By using the structured financial metamodel to perform intelligent conversion of heterogeneous multi-source data, the original financial synchronization data can be efficiently and accurately converted into standardized source financial synchronization data, ensuring the compatibility and consistency between different data sources.

[0018] Preferably, step S14 comprises the following steps:

[0019] Step S141: Analyze the data source structure according to the heterogeneous data source format data, mark key element points, and generate data source key elements;

[0020] Step S142: performing data attribute semantic analysis on key elements of the data source through a preset financial concept vocabulary library to obtain data source attribute semantic data;

[0021] Step S143: performing calculation logic standardization processing on the key elements of the data source to obtain standard key element calculation logic data;

[0022] Step S144: performing cross-source data element correlation analysis based on standard key element calculation logic data and data source attribute semantic data to obtain cross-source financial correlation rule data;

[0023] Step S145: generating a financial data semantic mapping matrix according to the cross-source financial association rule data to obtain financial semantic mapping matrix data;

[0024] Step S146: Perform data source protocol mapping based on the financial semantic mapping matrix data, and construct a metadata structure model to obtain a structured financial meta-model.

[0025] The present invention can deeply understand and analyze the structure of different data sources by structurally analyzing the data in the format of heterogeneous data sources, and ensure that all types of data can be accurately identified and processed. Key element point marking can highlight the important parts of the data source. Using the preset financial concept vocabulary library to perform data attribute semantic analysis, the meaning of the key elements of the data source can be accurately understood and analyzed, and the accuracy and consistency of the data at the semantic level can be ensured. By standardizing the calculation logic of the key elements of the data source, the consistency of the calculation logic of different data sources is ensured, and the data inconsistency problem caused by the difference in calculation logic is avoided. Using the standard key element calculation logic data and the data source attribute semantic data to perform cross-source data element correlation analysis, the correlation relationship between different data sources can be effectively identified and analyzed, and the generated cross-source financial association rule data provides a reliable basis for data integration and consistency, which helps to achieve seamless integration of cross-source data. By generating a financial data semantic mapping matrix through cross-source financial association rule data, the financial data of different data sources can be mapped and corresponded at the semantic level, ensuring the accuracy and consistency of the data at the semantic level. Using the financial semantic mapping matrix data to perform data source protocol mapping ensures the compatibility and consistency between different data source protocols. By constructing a metadata structure model, financial data from different data sources can be standardized and structured, significantly improving the efficiency and accuracy of data processing.

[0026] Preferably, step S2 comprises the following steps:

[0027] Step S21: performing perturbation sensitivity analysis on the source financial synchronization data to obtain a financial sensitive perturbation coefficient;

[0028] Step S22: constructing the chaotic phase space of the source financial synchronization data through a preset chaotic mapping function based on the financial sensitive disturbance coefficient to generate financial chaotic phase space matrix data;

[0029] Step S23: performing singular value eigendecomposition on the financial chaotic phase space matrix data to obtain the financial main eigenvector data and the source financial synchronization eigenvalues ​​respectively;

[0030] Step S24: Calculate the maximum Lyapunov exponent using the main financial eigenvector data, and perform chaotic trajectory reconstruction processing to obtain chaotic trajectory reconstruction data;

[0031] Step S25: Perform financial chaos mapping eigenvalue processing on the source financial synchronization eigenvalue through chaotic trajectory reconstruction data to generate financial chaos eigenvalue data.

[0032] The present invention can identify and evaluate the system-sensitive parts of the data by performing perturbation sensitivity analysis on the source financial synchronization data, thereby quantifying its impact on the stability of the financial data. By using a preset chaos mapping function to construct a chaotic phase space, the source financial synchronization data can be converted into a highly complex and unpredictable chaotic data form. The generated financial chaos phase space matrix data enhances the security and anti-interference ability of the data, and prevents the leakage and tampering of the data during transmission and processing. By performing singular value eigendecomposition on the financial chaos phase space matrix data, the most important characteristic information in the financial data can be extracted, ensuring the accuracy and effectiveness of data processing. By calculating the maximum Lyapunov exponent and performing chaos trajectory reconstruction processing, the chaotic characteristics of the financial data can be quantified and analyzed, and the obtained chaos trajectory reconstruction data reflects the dynamic change law of the data. Using the chaotic trajectory reconstruction data to process the source financial synchronization eigenvalues ​​can generate more complex and secure financial chaos eigenvalue data, ensuring the synchronization and transmission of data in a high-security and complex environment.

[0033] Preferably, step S3 comprises the following steps:

[0034] Step S31: Monitor the data change log through the source data request end to generate source financial change monitoring data;

[0035] Step S32: performing time series snapshot aggregation processing on the source financial change monitoring data to obtain source financial change event data;

[0036] Step S33: classifying data change events according to the source financial change event data to generate change event classification data;

[0037] Step S34: identifying financial synchronization key factors of the financial chaos characteristic value data through the source financial change event data and the change event classification data, and generating financial synchronization key factor data;

[0038] Step S35: Encoding the chaotic system driving signal according to the financial synchronization key factor data to generate a financial synchronization driving signal;

[0039] Step S36: Perform security signal enhancement processing on the financial synchronization drive signal to generate an encrypted financial synchronization drive signal.

[0040] The present invention can timely capture and record data changes by monitoring the data change log of the source data request end, thereby ensuring the timeliness and accuracy of data synchronization. By performing time-series snapshot aggregation processing on the source financial change monitoring data, the continuous data changes can be effectively integrated and summarized, and the aggregated data is more readable and analyzable. By classifying the source financial change event data, the data change events can be classified according to certain rules and logic, so as to better understand and identify the types and characteristics of data changes. By analyzing the source financial change event data and the change event classification data, the key factors that have an important impact on the financial data synchronization process can be identified. By using the financial synchronization key factor data to encode the chaotic system drive signal, the key factor information can be converted into a drive signal, guiding the data synchronization process to proceed according to certain rules and logic. By performing security signal enhancement processing on the financial synchronization drive signal, the security and anti-interference ability of the signal can be enhanced to prevent the signal from being maliciously attacked or tampered with.

[0041] Preferably, step S34 includes the following steps:

[0042] Step S341: performing event feature engineering processing on the source financial change event data to generate change event feature data;

[0043] Step S342: performing event synchronization matching on the change event classification data according to the preset event synchronization rule to generate event synchronization data;

[0044] Step S343: by changing the event characteristic data, the financial chaos characteristic value data is associated and bound with the relevant characteristic values ​​to obtain the financial synchronization characteristic value data, and the event impact measurement calculation is performed according to the financial synchronization characteristic value data to obtain the associated event impact data;

[0045] Step S344: Screening the financial chaos characteristic value data for synchronous causal influence factors based on the associated event influence data to generate synchronous influence factor data;

[0046] Step S345: integrating the event synchronization mode data, the financial synchronization characteristic value data and the synchronization influencing factor data into a key factor structure to obtain financial synchronization key factor data.

[0047] The present invention can extract representative and important feature information from source financial change event data, such as change type, change time, change object, etc., through event feature engineering processing, so that the data is more interpretable and analyzable. Through the preset event synchronization mode rules, the change event classification data is matched and processed to determine the synchronization mode suitable for different event types. By associating and binding the change event feature data with the financial chaos eigenvalue data, the impact of the event on the financial data synchronization can be quantified and evaluated. By screening and analyzing the associated event impact data, the factors that have an important impact on the financial data synchronization process are determined, which helps to clarify the key influencing factors in the synchronization process and improves the accuracy and pertinence of data synchronization. By integrating the event synchronization mode data, the financial synchronization eigenvalue data and the synchronization influencing factor data, the impact of different factors on the financial data synchronization can be comprehensively considered to construct comprehensive financial synchronization key factor data.

[0048] Preferably, step S4 comprises the following steps:

[0049] Step S41: securely transmit the encrypted financial synchronization drive signal to the target data synchronization end, and perform signal decryption processing to generate a decrypted financial synchronization drive signal, wherein the decrypted financial synchronization drive signal includes decrypted event synchronization mode data, decrypted financial synchronization characteristic value data, decrypted synchronization influence factor data, and decrypted request end chaos parameters;

[0050] Step S43: reconstructing the chaotic state space according to the chaotic parameters of the decryption request end to generate reconstructed chaotic state space data;

[0051] Step S44: performing chaotic eigenvalue sequence iterative calculation on the reconstructed chaotic state space data by decrypting the financial synchronization eigenvalue data to generate chaotic eigenvalue copy data;

[0052] Step S45: matching the target synchronization data according to the chaotic eigenvalue replica data to generate accurate matching target synchronization data;

[0053] Step S46: Based on the decrypted event synchronization mode data, the decrypted financial synchronization feature value data, and the decrypted synchronization impact factor data, intelligent financial data synchronization processing is performed on the precise matching target synchronization data to generate intelligent synchronization status data.

[0054] The present invention ensures the security and integrity of data during transmission and prevents data leakage and tampering by securely transmitting encrypted financial synchronization drive signals to the target data synchronization end and performing decryption processing. The chaotic state space is reconstructed using the chaotic parameters of the decryption request end, and the complex dynamic characteristics of the original data can be accurately reproduced. The reconstructed chaotic state space data is iteratively calculated using the decrypted financial synchronization characteristic value data to generate chaotic characteristic value copy data consistent with the original data synchronization characteristics, thereby ensuring the accuracy and consistency of the characteristic value calculation. The target synchronization data is matched by using the chaotic characteristic value copy data, thereby ensuring the high consistency and accuracy of the synchronization data and the target data. By using the decrypted event synchronization mode data, the system can understand the specific methods and rules adopted in the data synchronization process, so that the synchronization strategy can be adjusted according to the actual situation to improve the efficiency and accuracy of data synchronization. The decrypted financial synchronization characteristic value data provides key financial feature information, which helps the system to identify and understand the key features of financial data, so that data matching and synchronization processing can be performed more accurately. At the same time, the decrypted synchronization influencing factor data provides various influencing factors in the data synchronization process, and the system can be intelligently adjusted according to these factors to improve the adaptability and robustness of data synchronization.

[0055] Preferably, step S45 includes the following steps:

[0056] Step S451: performing chaotic feature value metadata processing on the chaotic feature value copy data through a preset chaotic feature mapping knowledge base to generate chaotic feature metadata;

[0057] Step S452: performing chaos event semantic analysis according to chaos feature metadata to generate synchronization event semantic data;

[0058] Step S453: extract relevant mapping data according to the target data synchronization end to generate target relevant mapping data;

[0059] Step S454: using the synchronization event semantic data to perform Gaussian kernel matching processing on the target related mapping data to obtain event candidate matching data;

[0060] Step S455: performing chaotic inverse mapping processing on the event candidate matching data, and performing bidirectional consistency verification according to the chaotic feature metadata to generate inverse mapping verification data;

[0061] Step S456: Perform precise matching scoring processing based on the event candidate matching data, and perform confidence interval screening to obtain precise matching target synchronization data.

[0062] The present invention processes the chaotic feature value copy data by using the chaotic feature mapping knowledge base, thereby extracting key feature metadata. These metadata contain important feature information in the data synchronization process, which helps the system to accurately understand and analyze the chaotic characteristics of the data, and improve the accuracy and intelligence of data processing. By performing semantic analysis on the chaotic feature metadata, the system can deeply understand the chaotic events in the data synchronization process, thereby extracting the semantic information of the synchronization events. These semantic data help the system to more accurately understand various events and operations in the data synchronization process. By extracting the relevant mapping data of the target data synchronization end, the system can obtain the relevant information and characteristics of the target data, and these relevant mapping data help the system to more accurately understand the characteristics and requirements of the target data. By using the synchronization event semantic data to perform Gaussian kernel matching processing on the target related mapping data, the system can screen out event candidate matching data, which has the characteristics matching the synchronization event, and provides candidate objects for accurate matching. By performing chaotic inverse mapping processing on the event candidate matching data, and combining the chaotic feature metadata for bidirectional consistency verification, the system can verify the accuracy and reliability of the matching result, and ensure that the matching result is consistent with the original data. By accurately matching and scoring event candidate matching data and combining it with confidence interval screening, the system can screen out the target synchronization data that best meets the requirements, ensuring the accuracy and reliability of data matching.

[0063] Preferably, step S46 includes the following steps:

[0064] Step S461: performing intelligent difference analysis on the precise matching target synchronization data by decrypting the synchronization influence factor data to generate target synchronization difference data;

[0065] Step S462: extracting relevant feature values ​​from the decrypted financial synchronization feature value data through the target synchronization difference data to obtain the target synchronization feature value data;

[0066] Step S463: Perform synchronization end adaptive injection analysis according to the decrypted event synchronization mode data to obtain a synchronization end adaptive injection mode;

[0067] Step S464: Perform synchronization confirmation processing according to the synchronization end adaptive injection mode to generate synchronization confirmation voucher data;

[0068] Step S465: Synchronize and commit the target synchronization characteristic value data in a transactional phase by synchronizing the confirmation voucher data, and perform dynamic synchronization status monitoring to generate intelligent synchronization status data.

[0069] The present invention uses the decrypted synchronization influence factor data to perform difference analysis on the matched target synchronization data, and the system can accurately identify the differences and changes between the data. These target synchronization difference data reflect the actual changes in the data synchronization process. By using the target synchronization difference data to extract the decrypted financial synchronization feature value data, the system can obtain the key feature value information in the target synchronization data, which is helpful to understand the characteristics and change trends of the target data. By analyzing the decrypted event synchronization mode data, the system can understand the adaptability requirements and characteristics of the synchronization end, so as to formulate the corresponding synchronization end adaptive injection mode. These methods can ensure the adaptability and consistency of data in the synchronization process, and improve the flexibility and reliability of data synchronization. According to the determined synchronization end adaptive injection mode, the system performs synchronization confirmation processing and generates corresponding synchronization confirmation voucher data, which reflects the actual operation and processing in the synchronization process. Using the synchronization confirmation voucher data to perform transactional stage synchronization submission on the target synchronization feature value data, the system can ensure the transactional operation and data consistency of data synchronization. At the same time, dynamic synchronization status monitoring is performed, and the system can monitor the status and effect of the synchronization process in real time, discover and handle abnormal situations in time, and ensure the stability and reliability of data synchronization.

[0070] The present invention also provides a financial data synchronization system for executing the financial data synchronization method as described above, the financial data synchronization system comprising:

[0071] The heterogeneous data conversion module is used to monitor the synchronization request of the financial system and generate data synchronization request events; build a structured financial metamodel; perform intelligent conversion of heterogeneous multi-source data on data synchronization request events through the structured financial metamodel to generate source financial synchronization data;

[0072] The chaos feature extraction module is used to perform perturbation sensitivity analysis on the source financial synchronization data to obtain the financial sensitive disturbance coefficient; construct the chaos phase space based on the financial sensitive disturbance coefficient to generate the financial chaos phase space matrix data; perform financial chaos mapping eigenvalue processing on the source financial synchronization data according to the financial chaos phase space matrix data to generate the financial chaos eigenvalue data;

[0073] The security synchronization enhancement module is used to monitor the data change log according to the data synchronization request event and generate the source financial change monitoring data; use the source financial change monitoring data to identify the financial synchronization key factors of the financial chaos eigenvalue data and generate the financial synchronization key factor data; encode the chaotic system driving signal according to the financial synchronization key factor data and generate the encrypted financial synchronization driving signal;

[0074] The intelligent synchronization execution module is used to securely transmit the encrypted financial synchronization drive signal to the target data synchronization terminal, and perform signal decryption processing to generate a decrypted financial synchronization drive signal; perform target synchronization data matching on the decrypted financial synchronization drive signal to generate precisely matched target synchronization data; perform intelligent financial data synchronization processing based on the precisely matched target synchronization data to generate intelligent synchronization status data. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a schematic diagram of the steps of the financial data synchronization method and system of the present invention;

[0076] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0077] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0078] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0079] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3 The present invention provides a financial data synchronization method, comprising the following steps:

[0083] Step S1: monitor the synchronization request of the financial system and generate data synchronization request events; construct a structured financial metamodel; perform intelligent conversion of heterogeneous multi-source data on the data synchronization request events through the structured financial metamodel to generate source financial synchronization data;

[0084] Step S2: Perform perturbation sensitivity analysis on the source financial synchronization data to obtain the financial sensitive disturbance coefficient; construct the chaotic phase space based on the financial sensitive disturbance coefficient to generate the financial chaotic phase space matrix data; perform financial chaos mapping eigenvalue processing on the source financial synchronization data according to the financial chaotic phase space matrix data to generate the financial chaotic eigenvalue data;

[0085] Step S3: Monitor the data change log according to the data synchronization request event to generate source financial change monitoring data; use the source financial change monitoring data to identify the financial synchronization key factors of the financial chaos eigenvalue data to generate financial synchronization key factor data; encode the chaotic system driving signal according to the financial synchronization key factor data to generate an encrypted financial synchronization driving signal;

[0086] Step S4: securely transmit the encrypted financial synchronization drive signal to the target data synchronization terminal, and perform signal decryption processing to generate a decrypted financial synchronization drive signal; perform target synchronization data matching on the decrypted financial synchronization drive signal to generate precisely matched target synchronization data; perform intelligent financial data synchronization processing based on the precisely matched target synchronization data to generate intelligent synchronization status data.

[0087] The present invention monitors the synchronization request of the financial system, monitors the synchronization request and generates events to ensure that the system can respond to the data synchronization demand in time, avoid the risk caused by data synchronization delay, and ensure the real-time and accuracy of financial data. The structured financial metamodel is constructed to standardize the representation structure of financial data, simplify the data processing process, and improve the efficiency and accuracy of data synchronization. Intelligent conversion of heterogeneous multi-source data ensures the compatibility and consistency of data from different sources, and reduces the possibility of errors in the data conversion process. Perturbation sensitivity analysis is performed on the source financial synchronization data, and the security and unpredictability of the data are enhanced based on the chaotic phase space construction, which effectively prevents the illegal acquisition and tampering of the data. The generation of financial chaotic eigenvalue data makes the financial data more secure and unpredictable, and improves the confidentiality and integrity of the data. Data change log monitoring is performed according to the data synchronization request event, so that the change of data can be discovered in time. The identification of key factors of financial synchronization improves the accuracy and efficiency of data synchronization, avoids unnecessary data transmission and processing, and improves the performance of the system. Chaotic system drive signal encoding enhances the security and confidentiality of data transmission. The decrypted drive signal is accurately matched with the target data system, ensuring the accuracy and consistency of data synchronization and the seamless connection of data between different systems. Accurately matching the target synchronization data ensures the accuracy and consistency of data synchronization and avoids the risks brought by data inconsistency. Therefore, a financial data synchronization method of the present invention standardizes the data format and structure of different systems by constructing a structured financial meta-model. Chaos theory is used to construct a phase space to handle complex financial data changes. Through chaotic mapping processing, chaotic transformation is applied to the original financial data, which can convert sensitive financial information into unintuitive chaotic eigenvalues, ensuring that no data loss or errors occur during the synchronization process. The chaotic eigenvalues ​​are used to perform synchronization data binding and key synchronization factor identification on the synchronization data to obtain a synchronization drive signal, and accurate cross-system financial data synchronization is achieved based on the synchronization drive signal.

[0088] In the embodiment of the present invention, reference Figure 1 The above is a flowchart of the steps of a financial data synchronization method of the present invention. In this embodiment, the financial data synchronization method includes the following steps:

[0089] Step S1: monitor the synchronization request of the financial system and generate data synchronization request events; construct a structured financial metamodel; perform intelligent conversion of heterogeneous multi-source data on the data synchronization request events through the structured financial metamodel to generate source financial synchronization data;

[0090] In the embodiment of the present invention, a listening service is set on the financial system to receive synchronization requests. The source and data format of the request are verified. If they meet the requirements, the record is successful, otherwise it is marked as failed. According to the success mark, the coordination module is called to interact with the source system to obtain financial data. At the same time, a general financial meta-model is established to determine the structure and fields of each data source. According to the meta-model rules, the received heterogeneous data is intelligently converted to form financial synchronization data that meets the requirements of the meta-model.

[0091] Step S2: Perform perturbation sensitivity analysis on the source financial synchronization data to obtain the financial sensitive disturbance coefficient; construct the chaotic phase space based on the financial sensitive disturbance coefficient to generate the financial chaotic phase space matrix data; perform financial chaos mapping eigenvalue processing on the source financial synchronization data according to the financial chaotic phase space matrix data to generate the financial chaotic eigenvalue data;

[0092] In an embodiment of the present invention, a sensitivity analysis is performed on the source financial data, and the financial sensitive disturbance coefficient is calculated. The main analysis indicators such as sales revenue and profit are selected, and the time period and data granularity are determined. Perturbation processing is performed to randomly perturb each financial data point. The sensitive disturbance coefficient is calculated to reflect the sensitivity of each indicator to the disturbance. Based on the sensitive disturbance coefficient, a chaotic phase space is constructed and a chaotic mapping is prepared. A mapping function suitable for financial data and setting parameters are selected. A chaotic sequence is generated using the mapping function and the phase space is reconstructed. Singular value decomposition is performed to extract the main eigenvectors and eigenvalues. The time delay and embedding dimension are determined according to the Takens theorem, and the maximum Lyapunov exponent is calculated. The data to be processed is extracted from the source financial synchronization eigenvalues, and the eigenvalues ​​of the chaotic trajectory are calculated. The trajectory reconstruction eigenvalues ​​are mapped to the financial chaos eigenvalues. According to the mapping results, financial chaos eigenvalue data is generated.

[0093] Step S3: Monitor the data change log according to the data synchronization request event to generate source financial change monitoring data; use the source financial change monitoring data to identify the financial synchronization key factors of the financial chaos eigenvalue data to generate financial synchronization key factor data; encode the chaotic system driving signal according to the financial synchronization key factor data to generate an encrypted financial synchronization driving signal;

[0094] In an embodiment of the present invention, data change log monitoring is performed based on data synchronization request events. Source financial change monitoring data is generated, and it is sorted and aggregated to generate a time series snapshot. Events are matched with classification rules one by one to determine the event classification to which they belong, and marked as the corresponding classification. Important financial chaos eigenvalues ​​are identified using source data and marked as key factors. According to chaotic system theory, drive signal encoding rules are defined, and the eigenvalue changes of key factors are encoded into drive signals and saved. A secure random number generation algorithm is used to generate a key, and the financial synchronization drive signal is encrypted to ensure data confidentiality.

[0095] Step S4: securely transmit the encrypted financial synchronization drive signal to the target data synchronization terminal, and perform signal decryption processing to generate a decrypted financial synchronization drive signal; perform target synchronization data matching on the decrypted financial synchronization drive signal to generate precisely matched target synchronization data; perform intelligent financial data synchronization processing based on the precisely matched target synchronization data to generate intelligent synchronization status data.

[0096] In an embodiment of the present invention, the encrypted financial synchronization drive signal is sent to the target data synchronization end through a secure transmission protocol. At the target end, the signal is decrypted using a corresponding key or algorithm to generate a decrypted financial synchronization drive signal. Based on the decrypted data, the chaotic state space is reconstructed, and the financial synchronization eigenvalue data is used for iterative calculation to generate a chaotic eigenvalue sequence. The generated chaotic eigenvalue copy is used to match the target synchronization data, and intelligent financial data synchronization processing is performed. This includes adjusting the time interval, format or precision of the synchronization data, and correcting the data to generate intelligent synchronization state data.

[0097] Preferably, step S1 comprises the following steps:

[0098] Step S11: monitor the synchronization request of the financial system and generate a data synchronization request event, wherein the data synchronization request event includes a source data request end, a target data synchronization end and synchronization request data;

[0099] Step S12: Performing request legitimacy verification according to the synchronization request data to generate synchronization request verification data;

[0100] Step S13: when the synchronization request verification data is successful, the source data request end is parsed for the request source format to obtain the heterogeneous data source format data; when the synchronization request verification data is failed, the data synchronization request is rejected and the process returns to step S11 to continue the synchronization request monitoring;

[0101] Step S14: constructing a structured financial meta-model according to the data in the format of heterogeneous data sources;

[0102] Step S15: Real-time data coordination and collection is performed on the source data request end through synchronization request verification data to obtain original financial synchronization data;

[0103] Step S16: Use the structured financial meta-model to perform intelligent conversion of heterogeneous multi-source data on the original financial synchronization data to generate source financial synchronization data.

[0104] In an embodiment of the present invention, for example, a listening service is deployed and started on a financial system server, and the service continuously listens to a specific port or message queue. The ERP system of a certain enterprise sends a synchronization request containing sales data to the listening port of the financial system. After receiving the request, the listening service records the detailed information of the request, including the request source (ERP system), the target system (sales data module in the financial system), and the specific sales data content. The system checks whether the source of the request is in the whitelist, for example, confirms whether the ERP system has the authority to access the financial system. The system checks whether the data format in the request meets expectations, for example, whether the sales data contains the correct fields (date, product ID, sales quantity, etc.). The system further verifies whether the data content meets business rules, for example, whether the sales quantity is a positive number, whether the date is within a reasonable range, etc. If all verifications pass, a synchronization request verification data marked as "successful" is generated; if any verification fails, a verification data marked as "failed" is generated. According to the result of the synchronization request verification data, take corresponding actions. If the verification data is "successful", the system performs format parsing on the synchronization request data of the source data request end, and converts the data in the heterogeneous data source format into a format recognizable by the financial system. If the verification data is "failed", the system rejects the data synchronization request and returns to step S11 to continue listening for new synchronization requests. Analyze the heterogeneous data source formats received from different source data request terminals, determine the specific structure and fields of each data source, and design a general structured financial metamodel based on the data analysis results. For example, design a relational database model containing multiple tables, each table corresponds to a different financial data type (such as sales, procurement, inventory, etc.), and define the fields and their data types. For example, when the synchronization request verification data received by the system is successful, confirm the legitimacy of the source data request terminal and the correctness of the request data. The system calls a specific coordination module to interact with the source data request terminal (such as ERP system, CRM system, etc.) in real time to collect the required original financial data. For example, call the sales data interface of the ERP system through the API interface to obtain the sales records of the day in real time. According to the structured financial metamodel, define data conversion rules, which include field mapping, data format conversion, value verification and conversion, etc. For example, map the JSON format sales data field in the ERP system to the database table field of the financial system. The converted data meets the requirements of the structured financial metamodel and forms new source financial synchronization data.

[0105] Preferably, step S14 comprises the following steps:

[0106] Step S141: Analyze the data source structure according to the heterogeneous data source format data, mark key element points, and generate data source key elements;

[0107] Step S142: performing data attribute semantic analysis on key elements of the data source through a preset financial concept vocabulary library to obtain data source attribute semantic data;

[0108] Step S143: performing calculation logic standardization processing on the key elements of the data source to obtain standard key element calculation logic data;

[0109] Step S144: performing cross-source data element correlation analysis based on standard key element calculation logic data and data source attribute semantic data to obtain cross-source financial correlation rule data;

[0110] Step S145: generating a financial data semantic mapping matrix according to the cross-source financial association rule data to obtain financial semantic mapping matrix data;

[0111] Step S146: Perform data source protocol mapping based on the financial semantic mapping matrix data, and construct a metadata structure model to obtain a structured financial meta-model.

[0112] In the embodiment of the present invention, the received data in the format of heterogeneous data sources is analyzed to determine its data structure. For example, the XML format sales data file provided by the ERP system is analyzed to determine the tags and hierarchical relationships it contains. Key elements such as fields, tags, nodes, etc. are identified in the data structure. For example, in the sales data file, key fields such as date, product ID, sales quantity, etc. are identified. For example, "<sales_date> ","<product_id> "," <quantity>" is marked as a key element to form a list record. The preset financial concept vocabulary library is loaded, which contains commonly used financial terms and their definitions. The terms in the vocabulary library are used to perform semantic analysis on the key elements of the data source. For example, "<sales_date> "parses to "sales date"," <quantity>" is parsed into "sales quantity". The parsing results are used to generate data source attribute semantic data. For example, the semantic attribute of "sales date" is "Date" and the semantic attribute of "sales quantity" is "Integer". The calculation logic of key elements of the data source is analyzed, such as the accumulation of sales quantity and the format conversion of dates. The calculation logic of different data sources is standardized. For example, dates in different formats are uniformly converted to the standard "YYYY-MM-DD" format, and all quantity fields are converted to standard numeric types. The standard key element calculation logic data and data source attribute semantic data are combined to prepare for cross-source correlation analysis. Match the key elements of different data sources. For example, the "sales quantity" of the ERP system is matched with the "sales quantity" of the CRM system. "Number of sales records" is matched. Cross-source financial association rule data is generated based on the matching results. Based on the association rules, a financial data semantic mapping matrix is ​​generated. For example, the rows of the matrix represent the key elements of the ERP system, the columns represent the key elements of the CRM system, and the values ​​in the matrix represent the association relationship. Map the data protocols of different data sources, for example, map the XML data protocol of the ERP system to the JSON data protocol of the financial system. Based on the mapped data protocol, a metadata structure model is constructed. For example, a new database schema is created that contains standardized table structures and field definitions from different data sources. : Verify the constructed structured financial metamodel to ensure that it can be compatible with and process financial data from different data sources, and finally generate a structured financial metamodel.

[0113] Preferably, step S2 comprises the following steps:

[0114] Step S21: performing perturbation sensitivity analysis on the source financial synchronization data to obtain a financial sensitive perturbation coefficient;

[0115] Step S22: constructing the chaotic phase space of the source financial synchronization data through a preset chaotic mapping function based on the financial sensitive disturbance coefficient to generate financial chaotic phase space matrix data;

[0116] Step S23: performing singular value eigendecomposition on the financial chaotic phase space matrix data to obtain the financial main eigenvector data and the source financial synchronization eigenvalues ​​respectively;

[0117] Step S24: Calculate the maximum Lyapunov exponent using the main financial eigenvector data, and perform chaotic trajectory reconstruction processing to obtain chaotic trajectory reconstruction data;

[0118] Step S25: Perform financial chaos mapping eigenvalue processing on the source financial synchronization eigenvalue through chaotic trajectory reconstruction data to generate financial chaos eigenvalue data.

[0119] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0120] Step S21: performing perturbation sensitivity analysis on the source financial synchronization data to obtain a financial sensitive perturbation coefficient;

[0121] In an embodiment of the present invention, source financial synchronization data is extracted from a financial system, including various financial indicators, such as revenue, expenditure, profit, assets and liabilities, etc. A sensitivity analysis framework is established to define the scope and indicators of the analysis. For example, sales revenue and profit are selected as the main analysis indicators, and the time period and data granularity of the analysis (such as monthly or quarterly analysis) are determined. The source financial synchronization data is perturbed. A perturbation operation is performed on each financial data point. For example, the sales revenue data of a certain month is randomly perturbed from 1 million yuan to a value between 950,000 yuan and 1.05 million yuan. The financial sensitive perturbation coefficient is calculated. The sensitive perturbation coefficient reflects the sensitivity of each financial indicator to data perturbation. For example, the sensitive perturbation coefficient of sales revenue is calculated to be 0.2, and the sensitive perturbation coefficient of profit is calculated to be 0.5, indicating that profit is more sensitive to perturbation.

[0122] Step S22: constructing the chaotic phase space of the source financial synchronization data through a preset chaotic mapping function based on the financial sensitive disturbance coefficient to generate financial chaotic phase space matrix data;

[0123] In an embodiment of the present invention, the source financial synchronization data and the financial sensitive disturbance coefficient calculated previously are used to prepare for the construction of the chaotic phase space. For example, the financial data such as sales revenue and profit of the previous quarter and their corresponding sensitive disturbance coefficients are prepared. A chaotic mapping function suitable for financial data, such as a Logistic mapping function or a Henon mapping function, is selected as the basis for constructing the chaotic phase space. According to the requirements of the chaotic mapping function, the corresponding parameters are set. For example, for the Logistic mapping function, the initial value and the control parameter are set. Based on the financial sensitive disturbance coefficient and the set parameters, a chaotic sequence is generated using the chaotic mapping function. For example, a series of chaotic values ​​are generated using the Logistic mapping function, which will be used as time series data when constructing the chaotic phase space. According to the Takens theorem, an appropriate time delay and embedding dimension are selected to reconstruct the phase space of the financial data. For example, the time delay is set to τ, the embedding dimension is set to d, and the chaotic sequence generated by the chaotic mapping function and the source financial synchronization data jointly construct the phase space. The reconstructed phase space is represented as a matrix form, and each row represents a multidimensional vector at a time point. For example, for a d-dimensional phase space, each row contains d values, corresponding to the financial data under different time delays. The numerical values ​​of the financial data and chaotic sequence are filled into the phase space matrix to form a complete financial chaotic phase space matrix data. For example, the sales revenue, profit and their corresponding chaotic values ​​at each time point are filled into different columns of the matrix.

[0124] Step S23: performing singular value eigendecomposition on the financial chaotic phase space matrix data to obtain the financial main eigenvector data and the source financial synchronization eigenvalues ​​respectively;

[0125] In an embodiment of the present invention, singular value decomposition (SVD) is performed on the loaded chaotic phase space matrix data. SVD decomposes the matrix into the product of three matrices: U, Σ and V^T. Among them, U is the left singular vector matrix, Σ is the singular value diagonal matrix, and V^T is the right singular vector matrix. The main financial eigenvector data is extracted from the decomposed U matrix. The main eigenvectors usually correspond to larger singular values, representing the main direction and change trend in the data. For example, the first few columns of the U matrix are extracted as the main eigenvector data. The source financial synchronization eigenvalues ​​are extracted from the singular value diagonal matrix Σ. The singular values ​​represent the importance of the data, and the larger the singular value, the more important the feature. For example, the diagonal elements of the Σ matrix are extracted as the eigenvalues ​​of the source financial synchronization.

[0126] Step S24: Calculate the maximum Lyapunov exponent using the main financial eigenvector data, and perform chaotic trajectory reconstruction processing to obtain chaotic trajectory reconstruction data;

[0127] In an embodiment of the present invention, according to Takens' theorem, the time delay (τ) and embedding dimension (d) required for chaotic trajectory reconstruction are determined. For example, the time delay is determined by calculating the mutual information or the autocorrelation function, and the embedding dimension is determined by the pseudo nearest neighbor method. Using the time delay τ, the main feature vector data is expanded in a multidimensional space. For example, for the time series data x(t), [x(t), x(t+τ), x(t+2τ), ..., x(t+(d-1)τ)] is reconstructed. A pair of points closest to each other is selected in the reconstructed phase space, and the initial distance is recorded. As time evolves, the distance change of these points in the reconstructed phase space is calculated. According to the exponential growth relationship of the distance over time, the maximum Lyapunov exponent is calculated. The linear regression method is usually used to fit the logarithmic change curve of the distance, and the slope is the Lyapunov exponent.

[0128] Step S25: Perform financial chaos mapping eigenvalue processing on the source financial synchronization eigenvalue through chaotic trajectory reconstruction data to generate financial chaos eigenvalue data.

[0129] In an embodiment of the present invention, eigenvalue data to be processed is extracted from the source financial synchronization eigenvalues, which is used to describe the importance and change trend of financial data. For example, eigenvalues ​​representing financial indicators such as sales revenue and profit are extracted. Eigenvalues ​​are calculated for the chaotic trajectory reconstruction vector at each time point. For example, for each row of the chaotic trajectory reconstruction matrix, an eigenvalue analysis method (such as principal component analysis) is used to calculate its eigenvalue. The calculated trajectory reconstruction eigenvalue is mapped to the source financial synchronization eigenvalue. A linear or nonlinear mapping method can be used to convert the chaotic trajectory reconstruction eigenvalue into a financial chaos eigenvalue. For example, the mapping process can be performed through a linear regression or a neural network model. According to the result of the mapping process, financial chaos eigenvalue data is generated.

[0130] Preferably, step S3 comprises the following steps:

[0131] Step S31: Monitor the data change log through the source data request end to generate source financial change monitoring data;

[0132] Step S32: performing time series snapshot aggregation processing on the source financial change monitoring data to obtain source financial change event data;

[0133] Step S33: classifying data change events according to the source financial change event data to generate change event classification data;

[0134] Step S34: identifying financial synchronization key factors of the financial chaos characteristic value data through the source financial change event data and the change event classification data, and generating financial synchronization key factor data;

[0135] Step S35: Encoding the chaotic system driving signal according to the financial synchronization key factor data to generate a financial synchronization driving signal;

[0136] Step S36: Perform security signal enhancement processing on the financial synchronization drive signal to generate an encrypted financial synchronization drive signal.

[0137] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S3 in the flowchart, in this example, step S3 includes:

[0138] Step S31: Monitor the data change log through the source data request end to generate source financial change monitoring data;

[0139] In an embodiment of the present invention, a data change log monitoring function is configured at the source data request end to capture changes in financial data in real time. For example, the monitoring frequency and monitoring object (such as a database table or a file directory) are set to ensure that the data range that needs to be monitored can be fully covered. The source data request end starts to monitor changes in financial data and records the change information in a log. These changes include the impact of operations such as adding, modifying, and deleting on the data. For example, when a sales order in the financial system is modified, the source data request end will record the change information of the order, including the data content before and after the modification and the change time.

[0140] Step S32: performing time series snapshot aggregation processing on the source financial change monitoring data to obtain source financial change event data;

[0141] In an embodiment of the present invention, the source financial change monitoring data is sorted by time according to the change time field, and a time series snapshot is generated. The time series snapshot reflects the changes of the financial data at different time points. For example, the monitoring data is sorted by the change time to obtain snapshot data arranged in chronological order. According to the change time field, the source financial change monitoring data is sorted by time, and a time series snapshot is generated. The time series snapshot reflects the changes of the financial data at different time points. For example, the monitoring data is sorted by the change time to obtain snapshot data arranged in chronological order. The time series snapshot data is aggregated, and the change information at adjacent time points is merged and summarized. This can reduce the amount of data and highlight the important features of the change event. For example, the same type of changes at adjacent time points are merged into one event, and the number of changes is counted.

[0142] Step S33: classifying data change events according to the source financial change event data to generate change event classification data;

[0143] In the embodiment of the present invention, for example, sales order modification events and purchase order addition events are classified into different categories. Each event in the source financial change event data is traversed one by one. Each event is matched with the classification rule to determine the event category to which it belongs. For example, according to the defined rules, it is determined whether the sales order modification event meets the "sales order change" classification. The successfully matched events are marked as corresponding categories. For example, the sales order modification event is marked as "sales order change".

[0144] Step S34: identifying financial synchronization key factors of the financial chaos characteristic value data through the source financial change event data and the change event classification data, and generating financial synchronization key factor data;

[0145] In an embodiment of the present invention, based on the source financial change event data and the change event classification data, it is determined which change events corresponding to the financial chaos eigenvalues ​​are important. For example, the financial eigenvalues ​​that change frequently and have a greater impact are identified as key factors. For example, each eigenvalue is matched with the defined identification rules to determine whether it belongs to a key factor. For example, based on the source financial change event data and the change event classification data, it is determined whether the change event corresponding to the eigenvalue belongs to an important financial change event. The eigenvalues ​​that are successfully matched are marked as key factors. For example, the eigenvalues ​​of important change events that occur frequently are marked as financial synchronization key factors.

[0146] Step S35: Encoding the chaotic system driving signal according to the financial synchronization key factor data to generate a financial synchronization driving signal;

[0147] In an embodiment of the present invention, the driving signal encoding rules are defined according to the chaotic system theory and actual needs. These rules can determine how to encode into the driving signal based on the characteristic value and change event of the key factor. For example, the characteristic value change of the key factor is mapped as the input signal of the chaotic system. According to the defined encoding rules, the characteristic value and the change event of the key factor are encoded to generate the driving signal. For example, the change amount of the characteristic value is mapped as the input signal of the chaotic system. The encoded driving signal is saved. For example, the driving signal of each key factor is saved as a sequence or array.

[0148] Step S36: Perform security signal enhancement processing on the financial synchronization drive signal to generate an encrypted financial synchronization drive signal.

[0149] In the embodiment of the present invention, a secure random number generation algorithm is used to generate a key. This key will be used to encrypt and decrypt the financial synchronization drive signal. For example, a secure random number generation function in an encryption library can be used to generate a 128-bit random key. The generated key is used to encrypt the financial synchronization drive signal, and a symmetric encryption algorithm is used to ensure the confidentiality of the data. For example, the AES symmetric encryption algorithm is used to encrypt the drive signal and the generated key.

[0150] Preferably, step S34 includes the following steps:

[0151] Step S341: performing event feature engineering processing on the source financial change event data to generate change event feature data;

[0152] Step S342: performing event synchronization matching on the change event classification data according to the preset event synchronization rule to generate event synchronization data;

[0153] Step S343: by changing the event characteristic data, the financial chaos characteristic value data is associated and bound with the relevant characteristic values ​​to obtain the financial synchronization characteristic value data, and the event impact measurement calculation is performed according to the financial synchronization characteristic value data to obtain the associated event impact data;

[0154] Step S344: Screening the financial chaos characteristic value data for synchronous causal influence factors based on the associated event influence data to generate synchronous influence factor data;

[0155] Step S345: integrating the event synchronization mode data, the financial synchronization characteristic value data and the synchronization influencing factor data into a key factor structure to obtain financial synchronization key factor data.

[0156] In an embodiment of the present invention, for financial change event data, features including timestamp, change type, change amount, change object, etc. can be extracted, and data cleaning and normalization processing can be performed to extract and construct representative features. The change event classification data is matched through the preset event synchronization mode rules to determine the synchronization mode of each change event. The event synchronization mode rules can be pre-defined according to business rules and data analysis results, for example, matching according to factors such as change type and change object. Using statistical analysis methods or machine learning algorithms, association analysis is performed on change event feature data and financial chaos feature value data to determine the correlation between them. Then, according to the correlation results, the financial feature values ​​that have a significant correlation with the change event are selected as financial synchronization feature value data. Methods such as Pearson correlation coefficient or Spearman rank correlation coefficient are used to measure the correlation between change event feature data and financial feature value data, and feature values ​​that are highly correlated with the event are selected as financial synchronization feature value data. The event impact data and financial chaos eigenvalue data are analyzed to identify the factors that have the greatest impact on financial synchronization, and the factors with the greatest impact are selected as synchronization influencing factor data, which is achieved with the help of data analysis tools (such as the Scikit-learn library in Python). The event synchronization mode data, financial synchronization eigenvalue data, and synchronization influencing factor data are integrated into a key factor structure to obtain financial synchronization key factor data. The event synchronization mode data, financial synchronization eigenvalue data, and synchronization influencing factor data are integrated according to the dimensions of financial synchronization key factors to form a complete financial synchronization key factor data set.

[0157] Preferably, step S4 comprises the following steps:

[0158] Step S41: securely transmit the encrypted financial synchronization drive signal to the target data synchronization end, and perform signal decryption processing to generate a decrypted financial synchronization drive signal, wherein the decrypted financial synchronization drive signal includes decrypted event synchronization mode data, decrypted financial synchronization characteristic value data, decrypted synchronization influence factor data, and decrypted request end chaos parameters;

[0159] Step S43: reconstructing the chaotic state space according to the chaotic parameters of the decryption request end to generate reconstructed chaotic state space data;

[0160] Step S44: performing chaotic eigenvalue sequence iterative calculation on the reconstructed chaotic state space data by decrypting the financial synchronization eigenvalue data to generate chaotic eigenvalue copy data;

[0161] Step S45: matching the target synchronization data according to the chaotic eigenvalue replica data to generate accurate matching target synchronization data;

[0162] Step S46: Based on the decrypted event synchronization mode data, the decrypted financial synchronization feature value data, and the decrypted synchronization impact factor data, intelligent financial data synchronization processing is performed on the precise matching target synchronization data to generate intelligent synchronization status data.

[0163] In an embodiment of the present invention, an encrypted financial synchronization drive signal is obtained from a source end. Then, these signals are transmitted to a target data synchronization end using a secure transmission protocol. At the target end, a signal decryption process is performed, which includes using a corresponding key or algorithm to decrypt the signal. After decryption, a decrypted financial synchronization drive signal is generated, which includes decrypted event synchronization mode data, decrypted financial synchronization characteristic value data, decrypted synchronization influence factor data, and decrypted request end chaos parameters. For example, if the source end uses an RSA algorithm to encrypt data, the target end needs a corresponding private key to decrypt the data and separate the decrypted data. According to the chaos parameters provided by the decryption request end, the chaotic state space is reconstructed. This means that the target end uses the provided parameters to simulate or reconstruct the same chaotic state space as the request end. For example, if the chaotic parameters are an initial seed value and a set of parameter values, the target end can use these values ​​to reconstruct the same chaotic state space as the request end. On the basis of the reconstructed chaotic state space, the decrypted financial synchronization characteristic value data is used to perform iterative calculation of the chaotic characteristic value sequence. This means that the financial synchronization characteristic value data is used to initialize or influence the iterative process of the chaotic state space to generate a series of chaotic characteristic values. For example, if the eigenvalue data is a sequence containing numerical values, the target end can use these numerical values ​​as the starting values ​​or parameters in the iteration process to calculate a series of chaotic eigenvalues. The generated chaotic eigenvalue replica data is used to match the target synchronization data. This means using the chaotic eigenvalue replica to identify or match the synchronization data of the target end. For example, if the data of the target end is a set of time series data, the generated chaotic eigenvalue replica can be used to identify the data points or sequences that match it. Based on the decrypted event synchronization mode data, the decrypted financial synchronization eigenvalue data, and the decrypted synchronization influencing factor data, the matched target synchronization data is subjected to intelligent financial data synchronization processing. This means using the decrypted data and related parameters to intelligently process or analyze the target synchronization data to generate intelligent synchronization status data. For example, the decrypted event synchronization mode data can be used to determine the time interval of data synchronization, the decrypted financial synchronization eigenvalue data can be used to adjust the format or precision of the data, and the decrypted synchronization influencing factor data can be considered to adjust or correct the data, and finally generate intelligent synchronization status data.

[0164] Preferably, step S45 includes the following steps:

[0165] Step S451: performing chaotic feature value metadata processing on the chaotic feature value copy data through a preset chaotic feature mapping knowledge base to generate chaotic feature metadata;

[0166] Step S452: performing chaos event semantic analysis according to chaos feature metadata to generate synchronization event semantic data;

[0167] Step S453: extract relevant mapping data according to the target data synchronization end to generate target relevant mapping data;

[0168] Step S454: using the synchronization event semantic data to perform Gaussian kernel matching processing on the target related mapping data to obtain event candidate matching data;

[0169] Step S455: performing chaotic inverse mapping processing on the event candidate matching data, and performing bidirectional consistency verification according to the chaotic feature metadata to generate inverse mapping verification data;

[0170] Step S456: Perform precise matching scoring processing based on the event candidate matching data, and perform confidence interval screening to obtain precise matching target synchronization data.

[0171] In an embodiment of the present invention, a preset chaotic feature mapping knowledge base is used to process the generated chaotic feature value copy data to extract the chaotic feature metadata therein. This process can be to map the generated feature value copy data to the corresponding metadata according to the chaotic feature mapping rules in the existing knowledge base. For example, if the knowledge base defines the mapping relationship between the chaotic feature value and certain financial indicators, then for each feature value copy, the corresponding financial indicator metadata can be extracted by searching the knowledge base. According to the extracted chaotic feature metadata, a chaotic event semantic analysis is performed. This means that according to the semantic information of the metadata, the chaotic event is analyzed and understood to generate the semantic data of the synchronization event. For example, if the chaotic feature metadata represents a certain financial pattern or trend, then the meaning of the event can be analyzed based on this information, such as whether it represents a certain financial risk or opportunity. According to the relevant mapping data of the target data synchronization end, the mapping data related to the chaotic event is extracted. This can be to extract data or features corresponding to the chaotic event from the target data. For example, if the chaotic event represents a certain financial change, then the target data synchronization end contains financial data or indicators related to this change. Using the generated synchronous event semantic data, the extracted target-related mapping data is subjected to Gaussian kernel matching. This means matching the semantic data with the target data to identify data points or sequences related to the event. For example, the Gaussian kernel function can be used to calculate the similarity between the semantic data and the target data to determine the event candidate matching data. The obtained event candidate matching data is subjected to chaotic inverse mapping, and bidirectional consistency verification is performed based on the chaotic feature metadata. This means inverse mapping the matched data to verify its consistency with the original chaotic feature value and ensure that the mapping is bidirectional. For example, the candidate data can be inversely mapped back to the original chaotic feature value space using an inverse chaotic mapping function and compared with the original feature value for verification. According to the event candidate matching data, accurate matching scoring is performed, and confidence interval screening is performed to obtain accurate matching target synchronous data. This means scoring the candidate data to determine its matching degree with the event, and screening is performed based on the confidence interval to ensure the credibility of the matching result. For example, a score can be given based on the similarity or distance between the matching data and the event, and the final matching result is determined based on a pre-set confidence interval.

[0172] Preferably, step S46 includes the following steps:

[0173] Step S461: performing intelligent difference analysis on the precise matching target synchronization data by decrypting the synchronization influence factor data to generate target synchronization difference data;

[0174] Step S462: extracting relevant feature values ​​from the decrypted financial synchronization feature value data through the target synchronization difference data to obtain the target synchronization feature value data;

[0175] Step S463: Perform synchronization end adaptive injection analysis according to the decrypted event synchronization mode data to obtain a synchronization end adaptive injection mode;

[0176] Step S464: Perform synchronization confirmation processing according to the synchronization end adaptive injection mode to generate synchronization confirmation voucher data;

[0177] Step S465: Synchronize and commit the target synchronization characteristic value data in a transactional phase by synchronizing the confirmation voucher data, and perform dynamic synchronization status monitoring to generate intelligent synchronization status data.

[0178] In an embodiment of the present invention, the synchronization impact factor is compared with the target synchronization data to identify the difference between them. For example, if the synchronization impact factor indicates the degree of influence of a certain financial change, it can be applied to the target synchronization data to analyze the financial difference or change in the data. According to the difference information in the target synchronization data, the financial characteristic value related thereto is extracted. For example, if the difference of the target synchronization data mainly involves the change of certain financial indicators, the characteristic values ​​related to these indicators can be extracted from the financial characteristic value data. According to the information in the event synchronization mode data, the adaptability of the synchronization end is analyzed, and the mode of adaptive injection is determined. For example, if the event synchronization mode data indicates the time interval or frequency of data synchronization, the frequency of synchronization end data update or processing can be determined based on this information. According to the injection mode, the synchronization end is correspondingly confirmed to ensure the correctness and integrity of the synchronization data. According to the information in the synchronization confirmation voucher data, a new synchronization transaction is started. Before the transaction starts, the target synchronization characteristic value data is verified. This includes checking the integrity, format correctness and matching of the data with the confirmation voucher data. For example, the hash value in the synchronization confirmation voucher can be compared with the hash value of the target data to ensure that the data has not been tampered with during the transmission process. After verification, the target synchronization feature value data is submitted to the synchronization target. This can be writing data to the target database or storage system, or performing other required synchronization operations. When the data is successfully submitted to the target, the entire synchronization transaction is committed. This means marking all operations in the transaction as completed and saving them permanently to the target. The synchronization operation is considered successful only after the transaction is successfully submitted. If an error occurs during data verification or submission, a transaction rollback operation needs to be performed. This means undoing all previous operations and restoring to the state before the transaction started to ensure that the consistency and integrity of the data are not affected. And monitor the synchronization status in real time, and intelligent synchronization status data can be generated.

[0179] The present invention also provides a financial data synchronization system for executing the financial data synchronization method as described above, the financial data synchronization system comprising:

[0180] The heterogeneous data conversion module is used to monitor the synchronization request of the financial system and generate data synchronization request events; build a structured financial metamodel; perform intelligent conversion of heterogeneous multi-source data on data synchronization request events through the structured financial metamodel to generate source financial synchronization data;

[0181] The chaos feature extraction module is used to perform perturbation sensitivity analysis on the source financial synchronization data to obtain the financial sensitive disturbance coefficient; construct the chaos phase space based on the financial sensitive disturbance coefficient to generate the financial chaos phase space matrix data; perform financial chaos mapping eigenvalue processing on the source financial synchronization data according to the financial chaos phase space matrix data to generate the financial chaos eigenvalue data;

[0182] The security synchronization enhancement module is used to monitor the data change log according to the data synchronization request event and generate the source financial change monitoring data; use the source financial change monitoring data to identify the financial synchronization key factors of the financial chaos eigenvalue data and generate the financial synchronization key factor data; encode the chaotic system driving signal according to the financial synchronization key factor data and generate the encrypted financial synchronization driving signal;

[0183] The intelligent synchronization execution module is used to securely transmit the encrypted financial synchronization drive signal to the target data synchronization terminal, and perform signal decryption processing to generate a decrypted financial synchronization drive signal; perform target synchronization data matching on the decrypted financial synchronization drive signal to generate precisely matched target synchronization data; perform intelligent financial data synchronization processing based on the precisely matched target synchronization data to generate intelligent synchronization status data.

[0184] The beneficial effect of the present application is that the synchronization request monitoring of the financial system, the monitoring of the synchronization request and the generation of events ensure that the system can respond to the data synchronization requirements in a timely manner, avoid the risks caused by data synchronization delays, and ensure the real-time and accuracy of financial data. The construction of a structured financial metamodel standardizes the representation structure of financial data, simplifies the data processing process, and improves the efficiency and accuracy of data synchronization. This makes data exchange between different systems simpler and more reliable, while also reducing the complexity and error rate of data processing, thereby improving the overall data synchronization efficiency and quality. And based on the chaotic phase space construction, the security and unpredictability of the data are enhanced, and the illegal acquisition and tampering of the data are effectively prevented. This makes the financial data more secure and reliable during the transmission process, while also increasing the confidentiality and integrity of the data, and protecting the financial information from unauthorized access or tampering. According to the data synchronization request event, the data change log is monitored, and the changes in the data can be discovered in time. The identification of the key factors of financial synchronization improves the accuracy and efficiency of data synchronization, avoids unnecessary data transmission and processing, and improves the performance of the system. This means that the system can monitor the changes in the data in real time, and perform corresponding data synchronization operations according to the changes, thereby ensuring the accuracy and efficiency of data synchronization. The precise match between the decrypted drive signal and the target data system ensures the accuracy and consistency of data synchronization, ensures seamless connection of data between different systems, and ensures that the synchronized data fully matches the data in the target system, thereby avoiding data inconsistency problems that occur during data synchronization, ensuring the accuracy and consistency of data synchronization, and reducing the risks of data management and analysis.

[0185] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0186] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.< / quantity> < / quantity>

Claims

1. A financial data synchronization method, characterized in that: The following steps are involved: Step S1: monitor the synchronization request of the financial system and generate a data synchronization request event; Constructing a structured financial meta-model; Through the structured financial meta-model, the data synchronization request events are intelligently converted into heterogeneous multi-source data to generate source financial synchronization data; Step S2: Perform perturbation sensitivity analysis on the source financial synchronization data to obtain the financial sensitive perturbation coefficient; Based on the financial sensitive disturbance coefficient, the chaotic phase space is constructed to generate the financial chaotic phase space matrix data; according to the financial chaotic phase space matrix data, the source financial synchronization data is processed by the financial chaotic mapping eigenvalue to generate the financial chaotic eigenvalue data; wherein, step S2 is specifically as follows: Step S21: performing perturbation sensitivity analysis on the source financial synchronization data to obtain a financial sensitive perturbation coefficient; Step S22: constructing the chaotic phase space of the source financial synchronization data through a preset chaotic mapping function based on the financial sensitive disturbance coefficient to generate financial chaotic phase space matrix data; Step S23: performing singular value eigendecomposition on the financial chaotic phase space matrix data to obtain the financial main eigenvector data and the source financial synchronization eigenvalues ​​respectively; Step S24: Calculate the maximum Lyapunov exponent using the main financial eigenvector data, and perform chaotic trajectory reconstruction processing to obtain chaotic trajectory reconstruction data; Step S25: performing financial chaos mapping eigenvalue processing on the source financial synchronization eigenvalue through chaotic trajectory reconstruction data to generate financial chaos eigenvalue data; Step S3: Monitor the data change log according to the data synchronization request event to generate source financial change monitoring data; use the source financial change monitoring data to identify the financial synchronization key factors of the financial chaos eigenvalue data to generate financial synchronization key factor data; encode the chaotic system driving signal according to the financial synchronization key factor data to generate an encrypted financial synchronization driving signal; Step S4: securely transmit the encrypted financial synchronization drive signal to the target data synchronization terminal, and perform signal decryption processing to generate a decrypted financial synchronization drive signal; perform target synchronization data matching on the decrypted financial synchronization drive signal to generate accurate matching target synchronization data; perform intelligent financial data synchronization processing according to the accurate matching target synchronization data to generate intelligent synchronization status data; wherein, step S4 is specifically as follows: Step S41: securely transmit the encrypted financial synchronization drive signal to the target data synchronization end, and perform signal decryption processing to generate a decrypted financial synchronization drive signal, wherein the decrypted financial synchronization drive signal includes decrypted event synchronization mode data, decrypted financial synchronization characteristic value data, decrypted synchronization influence factor data, and decrypted request end chaos parameters; Step S43: reconstructing the chaotic state space according to the chaotic parameters of the decryption request end to generate reconstructed chaotic state space data; Step S44: performing chaotic eigenvalue sequence iterative calculation on the reconstructed chaotic state space data by decrypting the financial synchronization eigenvalue data to generate chaotic eigenvalue copy data; Step S45: matching the target synchronization data according to the chaotic eigenvalue replica data to generate accurate matching target synchronization data; Step S46: Based on the decrypted event synchronization mode data, the decrypted financial synchronization feature value data, and the decrypted synchronization impact factor data, intelligent financial data synchronization processing is performed on the precise matching target synchronization data to generate intelligent synchronization status data.

2. The financial data synchronization method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: monitor the synchronization request of the financial system and generate a data synchronization request event, wherein the data synchronization request event includes a source data request end, a target data synchronization end and synchronization request data; Step S12: Performing request legitimacy verification according to the synchronization request data to generate synchronization request verification data; Step S13: when the synchronization request verification data is successful, the source data request end is parsed for the request source format to obtain the heterogeneous data source format data; when the synchronization request verification data is failed, the data synchronization request is rejected and the process returns to step S11 to continue the synchronization request monitoring; Step S14: constructing a structured financial meta-model according to the data in the format of heterogeneous data sources; Step S15: Real-time data coordination and collection is performed on the source data request end through synchronization request verification data to obtain original financial synchronization data; Step S16: Use the structured financial meta-model to perform intelligent conversion of heterogeneous multi-source data on the original financial synchronization data to generate source financial synchronization data.

3. The financial data synchronization method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Analyze the data source structure according to the heterogeneous data source format data, mark key element points, and generate data source key elements; Step S142: performing data attribute semantic analysis on key elements of the data source through a preset financial concept vocabulary library to obtain data source attribute semantic data; Step S143: performing calculation logic standardization processing on the key elements of the data source to obtain standard key element calculation logic data; Step S144: performing cross-source data element correlation analysis based on standard key element calculation logic data and data source attribute semantic data to obtain cross-source financial correlation rule data; Step S145: generating a financial data semantic mapping matrix according to the cross-source financial association rule data to obtain financial semantic mapping matrix data; Step S146: Perform data source protocol mapping based on the financial semantic mapping matrix data, and construct a metadata structure model to obtain a structured financial meta-model.

4. The financial data synchronization method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Monitor the data change log through the source data request end to generate source financial change monitoring data; Step S32: performing time series snapshot aggregation processing on the source financial change monitoring data to obtain source financial change event data; Step S33: classifying data change events according to the source financial change event data to generate change event classification data; Step S34: identifying financial synchronization key factors of the financial chaos characteristic value data through the source financial change event data and the change event classification data, and generating financial synchronization key factor data; Step S35: Encoding the chaotic system driving signal according to the financial synchronization key factor data to generate a financial synchronization driving signal; Step S36: Perform security signal enhancement processing on the financial synchronization drive signal to generate an encrypted financial synchronization drive signal.

5. The financial data synchronization method according to claim 4, characterized in that: Step S34 includes the following steps: Step S341: performing event feature engineering processing on the source financial change event data to generate change event feature data; Step S342: performing event synchronization matching on the change event classification data according to the preset event synchronization rule to generate event synchronization data; Step S343: by changing the event characteristic data, the financial chaos characteristic value data is associated and bound with the relevant characteristic values ​​to obtain the financial synchronization characteristic value data, and the event impact measurement calculation is performed according to the financial synchronization characteristic value data to obtain the associated event impact data; Step S344: Screening the financial chaos characteristic value data for synchronous causal influence factors based on the associated event influence data to generate synchronous influence factor data; Step S345: integrating the event synchronization mode data, the financial synchronization characteristic value data and the synchronization influencing factor data into a key factor structure to obtain financial synchronization key factor data.

6. The financial data synchronization method according to claim 1, characterized in that: Step S45 includes the following steps: Step S451: performing chaotic feature value metadata processing on the chaotic feature value copy data through a preset chaotic feature mapping knowledge base to generate chaotic feature metadata; Step S452: performing chaos event semantic analysis according to chaos feature metadata to generate synchronization event semantic data; Step S453: extract relevant mapping data according to the target data synchronization end to generate target relevant mapping data; Step S454: using the synchronization event semantic data to perform Gaussian kernel matching processing on the target related mapping data to obtain event candidate matching data; Step S455: performing chaotic inverse mapping processing on the event candidate matching data, and performing bidirectional consistency verification according to the chaotic feature metadata to generate inverse mapping verification data; Step S456: Perform precise matching scoring processing based on the event candidate matching data, and perform confidence interval screening to obtain precise matching target synchronization data.

7. The financial data synchronization method according to claim 1, characterized in that: Step S46 includes the following steps: Step S461: performing intelligent difference analysis on the precise matching target synchronization data by decrypting the synchronization influence factor data to generate target synchronization difference data; Step S462: extracting relevant feature values ​​from the decrypted financial synchronization feature value data through the target synchronization difference data to obtain the target synchronization feature value data; Step S463: Perform synchronization end adaptive injection analysis according to the decrypted event synchronization mode data to obtain the synchronization end adaptive injection mode; Step S464: Perform synchronization confirmation processing according to the synchronization end adaptive injection method to generate synchronization confirmation voucher data; Step S465: Synchronize and commit the target synchronization characteristic value data in a transactional phase by synchronizing the confirmation voucher data, and perform dynamic synchronization status monitoring to generate intelligent synchronization status data.

8. A financial data synchronization system, characterized in that: Used to execute the financial data synchronization method according to claim 1, the financial data synchronization system comprises: The heterogeneous data conversion module is used to monitor the synchronization request of the financial system and generate data synchronization request events; build a structured financial metamodel; perform intelligent conversion of heterogeneous multi-source data on data synchronization request events through the structured financial metamodel to generate source financial synchronization data; The chaos feature extraction module is used to perform perturbation sensitivity analysis on the source financial synchronization data to obtain the financial sensitive disturbance coefficient; construct the chaos phase space based on the financial sensitive disturbance coefficient to generate the financial chaos phase space matrix data; perform financial chaos mapping eigenvalue processing on the source financial synchronization data according to the financial chaos phase space matrix data to generate the financial chaos eigenvalue data; The security synchronization enhancement module is used to monitor the data change log according to the data synchronization request event and generate the source financial change monitoring data; use the source financial change monitoring data to identify the financial synchronization key factors of the financial chaos eigenvalue data and generate the financial synchronization key factor data; encode the chaotic system driving signal according to the financial synchronization key factor data and generate the encrypted financial synchronization driving signal; The intelligent synchronization execution module is used to securely transmit the encrypted financial synchronization drive signal to the target data synchronization terminal, and perform signal decryption processing to generate a decrypted financial synchronization drive signal; perform target synchronization data matching on the decrypted financial synchronization drive signal to generate precisely matched target synchronization data; perform intelligent financial data synchronization processing based on the precisely matched target synchronization data to generate intelligent synchronization status data.

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